{"success":true,"data":{"username":"arc","posts":[{"id":"web-wiioum","shareId":"G6r0z1Bym9","platform":"web","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/","title":"How Do Algorithmic Management Practices Affect Workforce Well-Being? A Parallel Moderated Mediation Model","shareMode":"full","createdAt":1787222894353,"postedAt":"2026-08-20T10:48:07.362Z","publishedDate":"2026-08-20T10:48:07.362Z","archivedAt":"2026-08-20T10:48:10.651Z","archivedDate":"2026-08-20T10:48:10.651Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/G6r0z1Bym9","contentType":"article","content":{"text":". 2024 Nov 23;14(12):1123. doi: [10.3390/bs14121123](https://doi.org/10.3390/bs14121123)\n\n[Husam Zayid](https://pubmed.ncbi.nlm.nih.gov/?term=%22Zayid%20H%22[Author]) 1, [Ahmad Alzubi](https://pubmed.ncbi.nlm.nih.gov/?term=%22Alzubi%20A%22[Author]) 1,*, [Ayşen Berberoğlu](https://pubmed.ncbi.nlm.nih.gov/?term=%22Berbero%C4%9Flu%20A%22[Author]) 1, [Amir Khadem](https://pubmed.ncbi.nlm.nih.gov/?term=%22Khadem%20A%22[Author]) 1\n\nEditors: Xin-An Zhang 1, Lin Lu 1, Ming Kong 1\n\nPMCID: PMC11672927 PMID: [39767264](https://pubmed.ncbi.nlm.nih.gov/39767264/)\n\n## Abstract\n\nModern workplaces increasingly use algorithmic management practices (AMPs), which shape task assignment, monitoring, and evaluation. Despite the potential benefits these practices offer, like increased efficiency and objectivity, their impact on workforce well-being (WFW) has raised concerns. Drawing on self-determination theory (SDT) and conservation of resources theory (COR), this study examines the relationship between algorithmic management practices and workforce well-being, incorporating job burnout (JBO) and perceived threat (PT) as parallel mediators and person–job fit (PJF) as a moderator. The research employed a cross-sectional survey design targeting 2450 KOSGEB-registered manufacturing SMEs in Istanbul, Turkey. A sample of 666 respondents participated, and the data were analyzed using Smart PLS 4, employing structural equation modeling to test the proposed model. The results indicated that algorithmic management practices significantly increased job burnout and perceived threat, both of which negatively impacted workforce well-being. However, the direct effect of algorithmic management practices on workforce well-being was non-significant. Person–job fit moderated the relationships between algorithmic management practices and both job burnout and perceived threat, further influencing workforce well-being. The findings underscore the critical need for organizations to balance algorithmic efficiency with human-centric practices. Prioritizing person–job fit and fostering transparency in algorithmic processes can mitigate negative impacts, enhance employee well-being, and drive sustainable organizational success in the digital age.\n\n**Keywords:** algorithmic management, workforce well-being, job burnout, person–job fit, perceived threat\n\n## 1. Introduction\n\nTechnological advancements, at the forefront of Industry 4.0, have brought about profound changes across various facets of human society [[1](#fn:1),[2](#fn:2)], including the management of businesses and organizational structures [[3](#fn:3)]. In recent years, researchers have observed a growing interest in and concern for the impact of technological innovations on human resource management [[4](#fn:4)], with particular attention to algorithmic management practices, which are considered one of the most disruptive forms of technological change currently underway [[5](#fn:5),[6](#fn:6)].\n\nInitially, the term algorithmic management practices described how gig-working platforms utilized sophisticated algorithms to allocate, optimize, and evaluate work [[7](#fn:7)]. While these traits are typical of algorithmic management practices, they offer a restrictive definition that excludes many algorithmic practices currently implemented in workplaces [[8](#fn:8)]. For instance, in Amazon, algorithms are used extensively to manage and optimize various aspects of operation to determine the most efficient routes for workers to pick items from shelves, ensuring that they can fulfill orders as quickly as possible. Job burnout affects employees, with contributing factors such as limited career growth opportunities and inadequate management support. Such an environment encourages employees to seek employment in different fields or organizations [[9](#fn:9)].\n\nConversely, higher organizational commitment and job satisfaction are associated with lower levels of burnout [[10](#fn:10)]. While developing management algorithms aims to improve organizational efficiency and individual productivity, new challenges arise for both organizations and their employees. The reliance on algorithms can lead to the dehumanization of human resource management, compromising the personal and empathetic aspects of managing people [[11](#fn:11)]. Further analysis and research are needed to understand the impact of these practices on employee well-being.\n\nJob burnout is a critical factor influencing various work outcomes [[12](#fn:12)]. Algorithmic management practices alter how work is performed, reducing personal influence in the process. Studies suggest a negative relationship between job burnout and control over work, particularly in highly skilled positions [[13](#fn:13)]. These effects can diminish job burnout and responsibility within tasks, potentially affecting workforce well-being [[14](#fn:14)]. Existing research lacks sufficient empirical analysis of the positive or negative effects of algorithmic management practices on workforce well-being, both directly and indirectly, through their impact on work autonomy and overall compensation [[15](#fn:15)]. The study seeks to answer the following research questions:\n\nRQ 1: How do algorithmic management practices affect workforce well-being and what roles do perceived threat and job burnout play in mediating this relationship?\n\nRQ 2: To what extent does a person’s job fit to play as a moderator influence algorithmic management practices and workforce well-being?\n\nRQ 3: What strategies can organizations implement to mitigate the negative effects of algorithmic management practices?\n\nThis study adds to the ongoing conversation by combining the theoretical frameworks of self-determination theory and conservation of resources theory. To examine the relationship between algorithmic management practices and workforce well-being, we incorporated job burnout and perceived threat as parallel mediators and person–job fit as a moderator. We achieved this by using a large dataset from many different organizations. This study contributes to the literature by providing valuable insights into how algorithmic management practices are often utilized to enhance employee mental health and satisfaction. While previous studies have primarily focused on the benefits of algorithmic management practices for productivity and organizational control, fewer have examined their implications for workforce well-being. By introducing perceived threat and job burnout as mediating factors, this study identifies the specific pathway through algorithmic management practices that may negatively impact workforce well-being. Additionally, examining person–job fit as a moderator highlights the individual difference in response to algorithmic management practices which could inform tailored management strategies.\n\nDespite the growing adoption of algorithmic management practices across industries, the well-being-related consequences for employees remain unexplored; existing studies [[6](#fn:6)] largely examine algorithmic management practices in terms of performance outcomes with limited focus on job burnout and perceived threat as mediators and person–job fit as a moderator. This study addresses this gap by focusing on the workforce well-being of algorithmic management practices and testing how individual factors can potentially exacerbate these effects using cross-sectional data, and it endeavors to bridge this gap. The research is structured into three sections: (1) theoretical hypothesis development, elucidating the essence and research models of workforce well-being, algorithmic management practices, job burnout, and perceived threat; (2) a methodology section detailing the empirical research approach, including the research objectives, assumptions, sample characteristics, and results of statistical analyses; and (3) a discussion section culminating in conclusions.\n\n## 2. Theoretical Background and Hypotheses Development\n\n### 2.1. Self-Determination Theory\n\nPsychologists Edward Deci and Richard Ryan first introduced their ideas in their 1985 book, Self-Determination and Intrinsic Motivation in Human Behavior. Self-determination theory represents a broad theory of human motivation that has found successful application across various domains and management [[16](#fn:16)]. It asserts that an employee’s motivational orientation towards job activities influences both their performance and well-being. Self-determination theory categorizes motivation into different types and suggests that these types have distinct antecedents, relations to conservation of resources theory, and outcomes.\n\nOver the years, self-determination theory has addressed the intersection of motivation with organizational performance and wellness concerns, emphasizing the importance of fostering sustainable motivation and volitional engagement among employees and customers [[17](#fn:17)]. Self-determination theory advocates for creating workplace environments that support employees’ autonomy, not only as an end in itself but also as a means to enhance employee satisfaction, well-being, and organizational effectiveness.\n\nVarious factors, such as managerial styles and compensation structures, which contribute to employee autonomy and competence at work, are essential components for fostering engagement and organizational success, as outlined by self-determination theory. Self-determination theory underscores how managerial actions significantly shape organizational factors, either supporting or hindering employees’ fundamental psychological needs.\n\nThe fulfillment of all three basic psychological needs often strongly relates to conservation of resources theory and organizational support for autonomy [[18](#fn:18),[19](#fn:19)]. This conservation of resources theory relation arises because managers who encourage autonomy tend to be responsive to other needs as well, and employees with autonomy are more likely to actively meet their additional psychological needs. Therefore, when employees feel supported in their autonomy, they typically develop a stronger connection to the organization and perceive themselves as more capable and effective [[10](#fn:10),[16](#fn:16)].\n\n### 2.2. Conservation of Resources Theory\n\nStevan Hobfoll introduced conservation of resources theory in the 1980s. This theory is a stress theory that arises when essential resources become endangered, depleted, or unattainable despite significant efforts. Conservation of resources theory states that individuals actively seek to acquire, retain, nurture, and safeguard resources they consider essential. There is an understanding that cognitive processes inherently prioritize resource loss over resource gain, which stems from evolutionary imperatives [[20](#fn:20)].\n\nFundamentally, conservation of resources theory serves as a motivational framework grounded in the evolutionary drive to secure and maintain essential resources for survival, and conservation of resources theory is an element of human behavioral genetics. People use resources not only to cope with stress but also to accumulate a reserve for future adversity. The acquisition and retention of personal, social, and material resources instill in individuals, families, and organizations a sense of capability to navigate stressful situations [[21](#fn:21)].\n\nConservation of resources theory emphasizes that individual appraisal of stress is secondary to the central and universal value placed on resources such as health, well-being, family, self-esteem, and purpose in life. While the cultural expressions of these appraisals may differ, they consistently reflect this conservation of resource elements [[22](#fn:22)]. Moreover, conservation of resources theory emphasizes that we should not analyze stress solely as discrete events, but rather as complex sequences that unfold over time.\n\nFor instance, in a workplace experiencing layoffs, anticipation precedes the actual event, followed by the process of adjustment or finding new employment. Objective factors such as employability, skills, financial savings, and the availability of alternative positions significantly shape outcomes. Individuals’ appraisals, which are generally favorable predictors, stem from their innate tendency to prioritize resource loss over gain, as emphasized by conservation of resources theory [[20](#fn:20)]. This bias is culturally pervasive and not unique to individuals, although personal experiences may influence such appraisals.\n\nThe autonomous use of data-driven artificial intelligence is referred to as algorithmic management practices. While it can improve certain aspects of decision-making, it may also impact job autonomy, perceived fairness, and overall well-being. Existing research indicates that algorithmic management practices exert both direct and indirect effects on well-being. Empirical findings suggest a moderate, direct influence of algorithmic management practices on workforce well-being. Organizations adopting conservation of resources theory principles can positively influence employee well-being.\n\n### 2.3. Algorithmic Management Practices and Workforce Well-Being\n\nAlgorithmic management practices, which involve autonomous decision-making in human resource management using self-learning algorithms and AI, can have a significant impact on employees’ well-being in the workplace. One emerging trend is the adoption of algorithmic management practices, characterized by the integration of algorithms in managerial tasks encompassing HRM and decision-making processes [[23](#fn:23)], which has introduced new paradigms.\n\nWe first coined the term “algorithmic, data-driven management” to describe innovative methods for task assignment, informational support, and performance evaluation, specifically within the ridesharing sector. An algorithm operates as a computational tool that autonomously makes decisions using statistical models or decision rules, eliminating the need for direct human intervention [[24](#fn:24)]. Algorithmic management practices are defined as a control system where self-learning algorithms take on the responsibility for making and implementing decisions that impact labor, thereby reducing human oversight and involvement in the labor process. This results in the automation of HR-related tasks and duties, previously managed by human personnel [[25](#fn:25)].\n\nIn terms of workforce well-being, the employer, as the key architect of the work environment, has a significant role. The literature provides specific recommendations for fostering a supportive and effective workplace, including ensuring employees receive living wages, have autonomy in their work, access professional development opportunities, and are accommodated for disabilities with facilitated return-to-work programs [[6](#fn:6),[26](#fn:26)]. Studies indicate that algorithmic management practices alter work dynamics and diminish personal influence over processes. There exists a negative conservation of resources theory relation between job burnout and a heightened sense of control over work, especially in highly skilled positions.\n\nWhile algorithms can positively impact decision-making concerning personnel, they may also detrimentally affect job burnout, perceived threat, and overall workforce well-being. Algorithmic management practices exert a direct influence on workforce well-being. Larger organizations tend to experience diminishing impacts of algorithmic management practices on well-being and job burnout. While algorithmic management practices have the potential to improve decision-making, organizations must carefully consider their repercussions on employee autonomy and overall well-being. Striking a balance between automation and human agency is imperative for fostering a positive work environment. Therefore, the hypothesis put forward is as follows:\n\n> #### H1. *Algorithmic management practices negatively influence workforce well-being.*\n\n### 2.4. Algorithmic Management Practices and Influences on Job Burnout\n\nAlgorithmic management practices refer to the use of algorithms and AI to manage work processes, tasks, and employee performance. They have proliferated across diverse industries owing to technological advancements. The extant literature on job burnout delineates three components, which constitute the subscales of the Maslach Burnout Inventory [[26](#fn:26)]. Exhaustion, the most studied dimension, signifies stress aspects, including intense physical and emotional fatigue that impedes efficient functioning.\n\nProfessional inefficacy, or reduced personal accomplishment, refers to negative self-evaluation and a profound sense of ineffectiveness in work and interpersonal relationships. However, research indicates that burnout’s conservation of resources theory dimensions are exhaustion and cynicism, or disengagement. This section aims to provide an overview of job burnout, its causes, and its repercussions on individuals and organizations. Additionally, it elucidates individual and organizational interventions to prevent or alleviate burnout severity [[27](#fn:27)].\n\nWe also examine the theoretical perspective of algorithmic management practices on worker well-being. Self-determination theory posits that individuals possess intrinsic psychological needs that enhance motivation and well-being. Algorithmic management practices’ effects on worker motivation are often negative, as algorithmic systems’ rigidity may restrict autonomy and relatedness. However, person–job fit usage practices can moderate these effects. For instance, when algorithmic management allows some autonomy or facilitates clear communication, its impact on motivation may vary.\n\nOrganizations should integrate self-determination theory principles into the design and implementation of algorithmic management practices systems, balancing algorithmic control with opportunities for autonomy and relatedness to enhance worker motivation and well-being [[28](#fn:28)]. Conservation of resources theory underscores how individuals endeavor to acquire, safeguard, and sustain resources to handle stressors. Prolonged work-related stress leads to job burnout, a condition characterized by emotional fatigue, depersonalization, and diminished personal achievement. Frequently, this is due to resource depletion [[29](#fn:29)]. Algorithmic management practices can influence resource availability, impacting burnout risk. In essence, understanding the interplay between algorithmic management practices, worker motivation, and job burnout necessitates consideration of theories such as self-determination theory and conservation of resources theory. Organizations must strike a balance between algorithmic control and human needs to foster workforce well-being and productivity. Accordingly, the following is hypothesized:\n\n> #### H2. *Algorithmic management practices positively influence job burnout.*\n\n### 2.5. Job Burnout and Workforce Well-Being\n\nAnxiety, sickness, despondency, and exhaustion represent facets of diminished mental health and overall well-being in individuals. Similarly, headaches and muscle pains serve as indicators of physical unwellness. Employee workforce well-being catalyzes organizational success, shielding the organization from decreased productivity and reducing costs associated with poor health insurance.\n\nForward-thinking organizations must ensure that their initiatives yield health outcomes that contribute to overall workforce well-being, with a focus on the physical workspace and organizational culture. JBO results from chronic workplace stress that goes unmanaged. It is characterized by emotional exhaustion, depersonalization, and reduced feelings of accomplishment. Burnout can lead to a decline in an employee’s well-being and can also have negative effects on their physical health [[30](#fn:30)].\n\nSubstantial evidence from previous studies highlights a significant relationship between workforce well-being and job burnout [[31](#fn:31),[32](#fn:32),[33](#fn:33),[34](#fn:34),[35](#fn:35)]. Engaged employees are characterized as motivated, self-directed, and valuable contributors who drive organizational growth and development [[36](#fn:36)]. A positive state of mind is characterized by vigor, dedication, and absorption. Engaged employees feel energetic and effective in their work, and they are able to deal with the demands of their jobs [[37](#fn:37)]. The existing literature suggests that adverse workplace conditions negatively affect individual involvement, job satisfaction, and work enthusiasm, while also harming employee well-being [[38](#fn:38)]. According to the conservation of resources theory, there is a negative association between job burnout and workplace factors [[39](#fn:39)]. Consequently, job burnout tends to be more pronounced in unfavorable work environments. Based on the aforementioned discussion, the following hypothesis is formulated:\n\n> #### H3. *Job burnout has a negative influence on workforce well-being.*\n\n### 2.6. Algorithmic Management Practices and Perceived Threat\n\nAlgorithmic management practices involve using algorithms, data analytics, and automated decision-making systems to manage and control work processes and employees. These practices can include automated scheduling, performance tracking, task allocation, and feedback systems. Perceived threat refers to the individual’s subjective assessment of potential harm or danger to their well-being, status, or resources. In the workplace, perceived threats can manifest as fears related to job security, performance evaluations, and personal autonomy [[23](#fn:23),[40](#fn:40)].\n\nThe hypothesis posits that implementing algorithmic management practices in the workplace can improve perceived threats to employees’ performance. This hypothesis suggests that reliance on automated systems for management decisions can make employees feel more vulnerable, examined, and insecure about their job stability and autonomy [[19](#fn:19)]. Self-determination theory was developed, positing that the need to satisfy three fundamental psychological needs drives human motivation and well-being. Algorithmic management practices can undermine employees’ sense of autonomy by rigidly controlling work schedules, task assignments, and performance evaluations. This lack of control can lead to feelings of frustration and helplessness.\n\nThe conservation of resources theory, proposed by [[22](#fn:22)], suggests that individuals strive to obtain their valuable resources, which can be personal. Algorithmic management practices can create a perceived threat to employees’ job security and stability, as decisions may seem arbitrary or opaque, increasing anxiety about potential job loss. Continuous monitoring and automated evaluations can lead to emotional exhaustion and stress, depleting personal resources like energy and self-esteem. Algorithmic systems may undervalue or improperly recognize employees’ efforts, leading to decreased motivation and a reduced perceived return on their work investments [[41](#fn:41)].\n\nSelf-determination theory’s emphasis lies on meeting employees’ psychological needs and how their undermining by algorithmic management practices can increase perceived threat. Conservation of resources theory highlights the role of resource management, where algorithmic management practices can threaten or deplete valuable resources, leading to increased stress and perceived threat [[42](#fn:42)]. By fulfilling the psychological needs outlined in self-determination theory and protecting the resources highlighted in conservation of resources theory, organizations can reduce the perceived threat associated with algorithmic management practices and promote a healthier, more supportive work environment. Drawing from the theoretical framework, the hypothesis is outlined as follows:\n\n> #### H4. *Algorithmic management practices positively influence perceived threat.*\n\n### 2.7. Perceived Threat and Workforce Well-Being\n\nPerceived threat refers to an individual’s subjective evaluation of the likelihood and severity of potential harm or negative outcomes in a given situation. External events or various factors can influence this, posing a risk to one’s job or personal well-being [[43](#fn:43)]. Workforce well-being encompasses the overall mental, emotional, and physical health of employees within an organization. It includes factors of fulfillment in the workplace [[44](#fn:44)].\n\nThe hypothesis is that perceived threat negatively influences workforce well-being. This means that when employees feel threatened, whether by job insecurity, organizational changes, or external factors, they are likely to experience a decline in their overall well-being. This decline can manifest as increased stress, lower job satisfaction, reduced motivation, and overall poorer mental and physical health [[45](#fn:45)].\n\nSelf-determination theory posits that when employees perceive a threat, such as the potential loss of their job or drastic changes in their work environment, their sense of autonomy and competence can be undermined [[46](#fn:46)]. They may feel less in control of their future and less capable of performing their job effectively under the looming threat. Additionally, the stress and anxiety caused by perceived threat can harm their relationships with colleagues, further impacting their relatedness needs. The frustration of these basic psychological needs can lead to decreased well-being [[47](#fn:47)].\n\nConservation of resources theory suggests that individuals strive to protect their resources. Stress occurs when there is potential for actual resource loss or insufficient resource acquisition despite significant investment [[48](#fn:48)]. Perceived threats in the workplace represent a potential loss of valuable resources, such as job security, income, professional status, and social support. The anticipation or experience of these losses can deplete employees’ resources, leading to heightened stress and anxiety.\n\nAs employees invest more effort to cope with or prevent these losses, they may experience burnout and diminished well-being [[46](#fn:46)]. This continuous state of resource depletion and the associated stress can severely impact their mental and physical health. We can understand the negative impact of perceived threats on workforce well-being by integrating self-determination theory and conservation of resources theory. According to self-determination theory, perceived threats undermine employees’ basic psychological needs, leading to reduced well-being [[32](#fn:32),[33](#fn:33)]. Conservation of resources theory complements this by explaining that the anticipation or experience of resource loss due to perceived threats further depletes employees’ resources, exacerbating stress and reducing overall well-being. Together, these theories provide a comprehensive framework for understanding how perceived threats can negatively impact workforce well-being by disrupting psychological needs and depleting valuable resources. Given the preceding arguments, the proposed hypothesis is stated as follows:\n\n> #### H5. *Perceived threat negatively influences workforce well-being.*\n\n### 2.8. Job Burnout as a Mediator\n\nThe term algorithmic management practices refers to the use of sophisticated algorithms to monitor, evaluate, and manage employees [[49](#fn:49)]. This includes practices like automated scheduling, performance tracking, and behavior monitoring. Such practices can lead to increased efficiency but can also result in increased pressure and stress for employees. Job burnout is a state of job stress. It manifests as sensations of fatigue and diminished professional effectiveness [[42](#fn:42)]. Workforce well-being encompasses various aspects of employees’ health and happiness [[44](#fn:44)]. This hypothesis suggests that job burnout mediates the negative impact of algorithmic management practices on workforce well-being. In other words, algorithmic management practices increase job burnout, which in turn reduces workforce well-being.\n\nAlgorithmic management practices often reduce employees’ sense of autonomy and relatedness by imposing rigid controls and constant monitoring. This can lead to a lack of fulfillment of these basic psychological needs, increasing stress and burnout. Consequently, job burnout mediates the relationship between algorithmic management practices and workforce well-being. Job burnout and workforce well-being have a complex relationship [[50](#fn:50)]. There is also research that shows the positive impact on algorithms while offering employees the freedom to create value [[50](#fn:50)]. This means that, despite the use of algorithmic management, employees may have some freedom to feel job burnout. Also, [[49](#fn:49)] showed that algorithmic management practices can lead to resource loss by increasing job demands without providing adequate support or resources. This resource depletion can lead to job burnout, which in turn negatively affects workforce well-being. Thus, job burnout acts as a mediator in the relationship between algorithmic management practices and workforce well-being.\n\nBy integrating self-determination theory and conservation of resources theory, we can understand the mediating role of job burnout in the relationship between algorithmic management practices and workforce well-being. Algorithmic management practices often undermine employees’ fundamental psychological needs and result in resource depletion. This combination boosts job burnout, which hurts workforce well-being. In summary, job burnout mediates the negative impact of algorithmic management practices on workforce well-being, as these practices undermine essential psychological needs and deplete valuable resources, resulting in increased stress and reduced well-being. In light of the evidence discussed, the hypothesis can be articulated as follows:\n\n> #### H6. *Algorithmic management negatively influences workforce well-being through the negative influence of job burnout.*\n\n### 2.9. Perceived Threat as a Mediator\n\nPerceived threat refers to the subjective assessment of potential harm or danger arising from a situation or environment. This can include concerns about job security in the workplace [[50](#fn:50)]. As previously defined, algorithmic management practices involve using algorithms to oversee and supervise various aspects of organizational processes. These practices can lead to heightened efficiency but may also cause employees to feel constantly monitored and evaluated [[25](#fn:25)]. This hypothesis suggests that the perceived threat mediates the negative impact of algorithmic management practices on workforce well-being. In other words, algorithmic management practices increase perceived threat, which in turn reduces workforce well-being. Algorithmic management practices can undermine employees’ sense of autonomy and relatedness. This can result in employees perceiving a threat to their autonomy and job security. The perceived threat response to these psychological needs can increase stress and reduce workforce well-being, making it a mediator in the relationship between algorithmic management practices and workforce well-being.\n\nBy increasing job demands and reducing employees’ control over their work environment, algorithmic management practices can lead to perceived threats. This perception of threat can result in resource loss, leading to increased stress and reduced well-being [[51](#fn:51)]. Thus, perceived threat acts as a mediator in the relationship between algorithmic management practices and workforce well-being. By integrating self-determination theory and conservation of resources theory, we can understand the mediating role of perceived threat in the relationship between algorithmic management practices and workforce well-being. Algorithmic management practices often undermine employees’ basic psychological needs and create a perception of a threat to their resources. This combination increases perceived threat, which negatively impacts workforce well-being. In summary, perceived threat mediates the negative impact of algorithmic management practices on workforce well-being, as these practices undermine essential psychological needs and create a perception of threat to valuable resources, resulting in increased stress and reduced well-being. Therefore, the hypothesis suggested by this review is as follows:\n\n> #### H7. *Algorithmic management practices negatively influence workforce well-being through the negative influence of perceived threats.*\n\n### 2.10. Person–Job Fit as a Moderator\n\nPerson–job fit refers to the compatibility between an individual’s abilities and desires, as well as the demands, rewards, and attributes of a particular job. A high person–job fit means that the job characteristics align well with the individual’s skills and preferences, leading to greater job satisfaction and performance [[52](#fn:52)]. Person–job fit moderates the relationship between algorithmic management practices and job burnout. Particularly, the positive association between algorithmic management practices and job burnout diminishes when employees possess a greater level of person–job fit. Person–job fit also moderates the connection between algorithmic management practices and perceived threat, specifically weakening the positive conservation of resources theory relation between algorithmic management practices and person–job fit when employees demonstrate a higher degree of person–job fit [[53](#fn:53)].\n\nFurthermore, person–job fit moderates the link between algorithmic management practices and workforce well-being. In particular, the negative association between algorithmic management practices and workforce well-being is less pronounced among employees with a higher level of person–job fit. Person–job fit denotes the alignment between an individual’s competencies, skills, and preferences and the demands of a specific job role. When this alignment is optimal, employees typically experience increased job satisfaction, improved performance, and increased retention rates. On the other hand, poor fit can lead to dissatisfaction, stress, and turnover. Organizations assess person–job fit through job analysis, personality assessments, interviews, and evaluations. In the SME industry, achieving a good person–job fit is crucial for employees who directly interact with customers, as it positively impacts the customer experience [[50](#fn:50),[54](#fn:54)]. AI can help identify suitable candidates during recruitment and optimize job assignments.\n\nBy applying self-determination theory and conservation of resources theory, we can understand how person–job fit moderates the impact of algorithmic management practices on job burnout, perceived threat, and workforce well-being. A high person–job fit enhances employees’ sense of autonomy (self-determination theory) while also providing valuable resources (conservation of resources theory). This reduces the stress and burnout associated with algorithmic management practices. A favorable fit mitigates threat perception by making employees feel secure and competent (self-determination theory) and less likely to experience resource loss (conservation of resources theory). Employees with a high person–job fit are better able to satisfy their psychological needs (self-determination theory) and conserve their resources (conservation of resources theory), ensuring their well-being even in the presence of algorithmic management practices. In summary, person–job fit serves as a critical moderator that can mitigate the negative impacts of algorithmic management practices on job burnout, perceived threat, and workforce well-being by fulfilling basic psychological needs and conserving essential resources. Building on the insights provided, the hypotheses are defined as follows:\n\n> #### H8. *Person–job fit moderates the relationship between algorithmic management practices and job burnout, and the positive relationship becomes weaker (stronger) when employees have a higher (lower) degree of person–job fit in the workplace.*\n\n> #### H9. *Person–job fit moderates the relationship between algorithmic management practices and perceived threat, and the positive relationship becomes weaker (stronger) when employees have a higher (lower) degree of person–job fit in the workplace.*\n\n> #### H10. *Person–job fit moderates the relationship between algorithmic management practices and workforce well-being, and the negative relationship is weaker among employees with a higher degree of person–job fit compared to a lower degree of job fit in the workplace.*\n\nThe relationships among algorithmic management practices, job burnout, perceived threat, workforce well-being, and the moderating role of person–job fit are synthesized into the proposed research model. This model visually represents the hypothesized connections and provides a framework for understanding the study’s conservation of resources theory and variables, as illustrated in [Figure 1](#behavsci-14-01123-f001).\n\n![Figure 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21f0/11672927/f88245afc1b4/behavsci-14-01123-g001.jpg)\n\n## 3. Methodology\n\n### 3.1. Sample and Data Collection\n\nTo ensure the representativeness of the sample, employees from small- and medium-sized businesses (SMEs) in Turkey registered with the Turkish Small Business Administration (KOSGEB) were recruited for participation in the study. Given the lack of consensus on the definition of SMEs, particularly across different countries and sectors, this study adopted the criterion of employing fewer than 250 employees, consistent with both the Turkish Small Business Administration and the Turkish State Institute of Statistics. Moreover, a minimum threshold of at least 10 employees was set to exclude micro firms, aligning with established small business research practices [[55](#fn:55)].\n\nThe SME sector is central to Turkey’s economy, driving job creation, economic stability, and innovation. Representing over 99% of enterprises, SMEs generate approximately 73% of employment and contribute around 54% to the GDP [[56](#fn:56)]. The manufacturing industries selected for inclusion encompassed a diverse array of sectors, ranging from food and beverage to chemical and petrochemical, ensuring a comprehensive representation of the Turkish manufacturing landscape. However, their environmental impact is significant, particularly in manufacturing, where they contribute notably to industrial emissions and resource consumption [[57](#fn:57)]. Recent policies encourage sustainable practices, though challenges persist in balancing growth with environmental goals. SMEs’ influence in Turkey highlights their role in both economic development and sustainable progress. The study targeted SMEs operating in the manufacturing sector within the greater metropolitan area of Istanbul, Turkey. This choice was informed by Istanbul’s historical prominence as the country’s primary industrial and commercial hub, accounting for a substantial portion of total capital investment and GDP.\n\nIn manufacturing, AMPs facilitate workforce performance monitoring; automated shift scheduling; and optimized resource allocation in logistics, storage, and delivery [[58](#fn:58)]. AMPs coordinate tasks, track employee movements through wearables, and provide real-time data for informed managerial decisions [[59](#fn:59)]. While AMPs are more established in retail and food services, where algorithmic shift scheduling is prevalent [[60](#fn:60),[61](#fn:61)], their potential to boost operational efficiency in manufacturing is increasingly recognized. The trend reflects a broader shift towards digital technologies in workforce management, with AI-based recruitment and monitoring tools indicating a future where AMPs may become standard across industries [[25](#fn:25),[62](#fn:62)]. AMPs’ integration into manufacturing thus signals a shift toward data-driven methods that reshape traditional production and workforce management.\n\nData collection was conducted through the distribution of self-administered questionnaires to executives of 2450 KOSGEB-registered manufacturing SMEs within the designated area. Data were collocted between June and July 2024, and executives were personally contacted to elucidate the study’s objectives and seek their approval for participation [[63](#fn:63)]. Upon obtaining management consent, employees were contacted via email, outlining the survey’s purpose, emphasizing voluntary participation, and ensuring confidentiality. This approach aimed to foster a conducive environment for candid responses while respecting participants’ autonomy and privacy.\n\nTo ensure rigor and transparency in our sampling process, we adhered to ethical standards, obtaining informed consent from all participants by explaining the study’s purpose, procedures, and voluntary nature. Using purposive sampling, we targeted professionals in the manufacturing sector to capture relevant insights on AMPs and workplace dynamics.\n\nA total of 666 individuals completed the questionnaires, yielding a response rate of 27.18% from the targeted participant pool. This response rate, while modest, reflects a substantial level of engagement and provides a robust dataset for analysis. Additionally, participants were assured of the opportunity to receive feedback on the research outcomes as a token of appreciation for their cooperation, further incentivizing their involvement.\n\n### 3.2. Basic Characteristics of the Respondent Profile\n\nThe respondent profile in this study reflects a diverse array of small- and medium-sized enterprises (SMEs) operating within the manufacturing sector of Turkey ([Table 1](#behavsci-14-01123-t001)). Across various industry segments, SMEs specializing in food products, electrical equipment, and chemical products emerged as the most prominent, contributing 160, 115, and 102 respondents, respectively. Additionally, significant representation was observed from machinery and equipment (93), rubber and plastic (67), textiles (55), and pharmaceutical sectors (43), with an unspecified “other” manufacturing subgroup comprising 31 respondents. The workforce within these companies was predominantly male, comprising 60.2% of the sample, with individuals aged 41–50 representing the largest subset at 41.3%. In terms of business longevity, a substantial portion of participants (206) had operated between 6 and less than 10 years, while 156 respondents had businesses operational for less than 5 years. Moreover, 125 establishments had operated for more than 11 but less than 15 years, 110 for more than 16 but less than 20 years, and 69 businesses boasted operational histories exceeding 20 years. The majority of respondents identified their businesses as medium-sized enterprises (less than 250 employees), constituting approximately 57.8% of the sample, followed by small businesses (less than 50 employees) at 42.2%.\n\n#### Table 1.\n\nCharacteristics of the sample.\n\n| Variables | Options | Frequency | Percentage (%) |\n| --- | --- | --- | --- |\n| Gender | Male | 401 | 60.2% |\n| Female | 265 | 39.8% |\n| Organizational tenure | Less than 5 years | 156 | 23.4% |\n| 6–less than 10 | 206 | 30.9% |\n| 11–less than 15 | 125 | 18.8% |\n| 16–less than 20 | 110 | 16.5% |\n| More than 20 years | 69 | 10.4% |\n| Industry sectors | Food and Beverage | 160 | 24.0% |\n| Electrical Equipment Manufacturing | 115 | 17.3% |\n| Wood and Chemical Manufacturing | 102 | 15.3% |\n| Machinery and Equipment Manufacturing | 93 | 14.0% |\n| Rubber and Plastic Manufacturing | 67 | 10.1% |\n| Textile and Clothing Manufacturing | 55 | 8.3% |\n| Pharmaceuticals and Medical Equipment | 43 | 6.4% |\n| Other Manufacturing | 31 | 4.6% |\n| Number of employees | Small-sized firms (less than 50 employees) | 281 | 42.2% |\n| Medium-sized firms (less than 250 employees) | 385 | 57.8% |\n| Total | 666 | 100% |\n\n[Open in a new tab](https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/table/behavsci-14-01123-t001/)\n\n### 3.3. Measurement of Variables\n\nThe measures employed to capture data for the empirical analyses drew upon established questionnaires utilized in prior studies ([Appendix A](#app1-behavsci-14-01123)), ensuring the reliability and validity of the instruments. Given the adaptation of the survey instrument for use in Turkey, the translating-backward translation methodology was adopted to facilitate linguistic equivalence and cultural appropriateness. Moreover, the questionnaire underwent rigorous pre-testing iterations to validate the sequencing of questions, wording, and format, thereby enhancing the instrument’s comprehensibility and clarity. Participants rated their agreement with each item on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5).\n\nThe study assessed respondents’ perceptions of AMPs using a four-item scale adapted from [[6](#fn:6),[64](#fn:64)]. This scale covered dimensions such as the utilization of data analytics for monitoring employee performance, enhancing processes or service delivery through data analytics, the adoption of robots, and the establishment of work pace by machines or computers.\n\nJBO, a crucial aspect of workforce well-being, was evaluated using a nine-item scale adapted from previous research [[65](#fn:65),[66](#fn:66)]. This comprehensive scale assessed various dimensions of burnout and reduced personal accomplishment. By considering these facets, researchers aimed to holistically evaluate participants’ experiences related to burnout [[67](#fn:67)].\n\nPerceived threat, another salient variable in the study, was measured using a four-item scale adapted from [[68](#fn:68),[69](#fn:69)]. This scale captured respondents’ subjective perceptions of threat arising from AMPs, providing insights into their cognitive appraisal of potential risks and challenges.\n\nThe moderating role of PJF was assessed using a six-item scale adapted from [[70](#fn:70)], which encompassed dimensions such as skills–requirements fit, needs–supplies fit, and values–congruence fit. This scale enabled an exploration of the extent to which individuals’ alignment with their job roles moderates the relationship between AMPs and workforce well-being.\n\nFinally, workplace well-being, the dependent variable in the model, was measured using an eight-item scale adapted from [[71](#fn:71)]. This scale captured various dimensions of well-being, including physical health, psychological well-being, job satisfaction, and work–life balance, offering a comprehensive assessment of participants’ overall well-being within the workplace context.\n\n### 3.4. Statistical and Analytical Strategy\n\nTo analyze the empirical data collected from the survey and examine the complex inter-relationships among variables, a structural equation modeling (SEM) approach was employed, utilizing Smart PLS 4 as the analytical tool [[72](#fn:72)]. Following the methodological guidelines proposed by [[73](#fn:73)], a two-stage analysis strategy was implemented. In the initial stage, attention was directed towards evaluation of the measurement model; researchers assessed the reliability and validity of the measurement instruments used to operationalize the constructs under investigation. This process involved examining factors such as convergent validity, discriminant validity, and internal consistency reliability to ensure the robustness of the measurement model [[74](#fn:74)]. In the subsequent stage, the structural model was tested to scrutinize its fit and relationships among variables and ascertain the underlying mechanisms driving these associations. As emphasized by [[75](#fn:75)], SEM offers a unified framework for assessing both direct and indirect effects, thus facilitating a comprehensive exploration of the complex pathways underlying the phenomena of interest. This analytical approach enables a nuanced understanding of the intricate interplay between AMPs, individual perceptions, and workforce well-being, thereby advancing theoretical discourse and informing evidence-based interventions aimed at enhancing organizational effectiveness and employee welfare.\n\n## 4. Analysis and Results\n\n### 4.1. Robustness Check\n\nPrior to delving into the substantive analysis, preliminary assessments were conducted to ensure the suitability of the data for structural equation modeling (SEM). This involved scrutinizing the descriptive statistics, reliability, and validity of the measurement model to ascertain the robustness of the dataset. As highlighted by [[76](#fn:76)], one of the crucial considerations in multivariate analysis is the presence of normality in the data distribution, as deviations from normality can compromise the validity and reliability of findings. To this end, skewness and kurtosis values were examined, with no variable exhibiting skewness greater than 3 or kurtosis values exceeding 10 [[77](#fn:77)], indicating adherence to normality assumptions. Additionally, potential multicollinearity issues were evaluated through the examination of variance inflation factor (VIF) values, with all VIF values falling within the acceptable range of 1.467 to 4.386, well below the threshold maximum value of 10 [[75](#fn:75)]. These preliminary assessments affirm the appropriateness of the dataset for SEM analysis, laying the groundwork for subsequent substantive analyses aimed at unraveling the complex relationships among variables.\n\n### 4.2. Common Method Variance\n\nTo mitigate the potential issue of common method variance, we implemented procedural and statistical remedies as recommended by reference [[78](#fn:78)]. These measures were particularly important since data were collected at a single time point. Procedurally, measures were taken to enhance respondent anonymity and encourage candid responses by emphasizing the absence of correct or incorrect answers and urging openness. Furthermore, efforts were made to articulate the study’s purpose clearly, minimize priming effects through careful item arrangement, and reduce question ambiguity via pilot testing [[79](#fn:79)]. These procedural safeguards aimed to mitigate methodological biases and enhance data quality. Additionally, as a statistical remedy in the form of addressing the potential issue of common method variance, we utilized the Harman single-factor procedure. According to reference [[80](#fn:80)], if common method variance is prevalent, a single component would account for a majority of the data variance. However, the results from our study revealed that the first extracted component explained only 34% of the variation. This suggests that common method variance is not a substantial concern in our research. These combined procedural and statistical measures enhance confidence in the validity and reliability of the data, providing a solid foundation for subsequent analyses and interpretations.\n\n### 4.3. Validity and Reliability of the Measurement Model\n\nPrior to assessing the inter-relationships between constructs, a comprehensive evaluation of the measurement model was conducted to ensure its validity and reliability. Following the guidelines outlined by [[81](#fn:81)], confirmatory composite analysis (CCA) was employed to validate the hypothesized five-factor model, encompassing AMP, JBO, PT, PJF, and WFW. As depicted in [Figure 2](#behavsci-14-01123-f002), outer-loading values exceeded the recommended threshold of 0.60 [[73](#fn:73)], which underscores the robustness of the measurement model. Additionally, all item component loadings were statistically significant (*p* < 0.001) and ranged from 0.671 to 0.956, affirming convergent validity [[72](#fn:72)].\n\n![Figure 2](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21f0/11672927/0247dea5cdfe/behavsci-14-01123-g002.jpg)\n\nDescriptive statistics presented in [Table 2](#behavsci-14-01123-t002), including mean values and standard deviations, offer insights into the distribution and variability of responses across constructs. CR values for all constructs ranged from 0.901 to 0.966, exceeding the threshold of 0.70. This indicates strong internal consistency reliability. AVE values ranged from 0.532 to 0.878, surpassing the predetermined criterion of 0.50. Importantly, the CR scores for all constructs exceeded their respective AVE values (CR > AVE), providing further evidence of convergent validity ([Table 2](#behavsci-14-01123-t002)). Discriminant validity was assessed by comparing the square root of AVE values with inter-construct correlation values [[82](#fn:82)]. These robust measurement model results lay a solid foundation for subsequent analyses and interpretations.\n\n#### Table 2.\n\nValidity and reliability of the measurement model.\n\n| Construct | Indicators | Mean | Std. Deviation | Outer Loadings | VIF | Cronbach’s α | CR | AVE |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| AMPs (AMP) | 0.954 | 0.966 | 0.878 |\n| AMP1 | 2.841 | 1.138 | 0.913 | 1.880 |\n| AMP2 | 2.785 | 1.043 | 0.956 | 3.476 |\n| AMP3 | 2.869 | 1.045 | 0.944 | 3.098 |\n| AMP4 | 2.859 | 1.015 | 0.935 | 2.824 |\n| Job Burnout (JBO) | 0.932 | 0.943 | 0.624 |\n| JBO1 | 3.237 | 1.077 | 0.735 | 2.447 |\n| JBO2 | 3.050 | 1.035 | 0.725 | 2.914 |\n| JBO3 | 3.595 | 0.996 | 0.781 | 2.588 |\n| JBO4 | 3.290 | 1.022 | 0.732 | 2.590 |\n| JBO5 | 3.249 | 1.023 | 0.786 | 3.249 |\n| JBO6 | 3.123 | 1.035 | 0.825 | 3.723 |\n| JBO7 | 3.155 | 1.046 | 0.862 | 3.026 |\n| JBO8 | 3.258 | 1.029 | 0.859 | 4.386 |\n| JBO9 | 2.908 | 1.016 | 0.877 | 3.336 |\n| JBO10 | 2.940 | 1.016 | 0.690 | 2.679 |\n| Perceived Threat (PT) | 0.88 | 0.918 | 0.737 |\n| PT1 | 3.197 | 1.114 | 0.872 | 2.685 |\n| PT2 | 3.215 | 1.261 | 0.832 | 1.987 |\n| PT3 | 3.002 | 1.186 | 0.898 | 3.046 |\n| PT4 | 3.279 | 1.084 | 0.831 | 1.960 |\n| Person–job fit (PJF) | 0.881 | 0.91 | 0.629 |\n| PJF1 | 3.261 | 1.071 | 0.834 | 2.990 |\n| PJF2 | 3.243 | 1.070 | 0.866 | 3.691 |\n| PJF3 | 3.218 | 1.075 | 0.867 | 3.525 |\n| PJF4 | 2.821 | 1.094 | 0.678 | 1.467 |\n| PJF5 | 3.290 | 1.153 | 0.733 | 2.634 |\n| PJF6 | 3.360 | 1.125 | 0.760 | 2.689 |\n| Workplace Well-Being (WFW) | 0.879 | 0.901 | 0.532 |\n| WFW1 | 2.548 | 1.201 | 0.680 | 2.234 |\n| WFW2 | 3.032 | 1.196 | 0.671 | 2.080 |\n| WFW3 | 2.629 | 1.246 | 0.695 | 3.007 |\n| WFW4 | 2.356 | 1.167 | 0.681 | 3.016 |\n| WFW5 | 3.425 | 1.088 | 0.730 | 1.928 |\n| WFW6 | 2.911 | 1.071 | 0.816 | 1.993 |\n| WFW7 | 3.428 | 1.022 | 0.744 | 2.411 |\n| WFW8 | 3.303 | 1.032 | 0.804 | 2.500 |\n\n[Open in a new tab](https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/table/behavsci-14-01123-t002/)\n\nNote(s): Variance inflation factor (VIF), composite reliability (CR), average variance extracted (AVE).\n\nIn evaluating CR for our measurement scales, we observed some CR values exceeding 0.95, which Hair et al. [[74](#fn:74)] suggest could indicate item redundancy. However, this outcome aligns with research showing that trait-oriented scales—such as those measuring AMP and job burnout—typically exhibit higher reliability due to their temporal stability, unlike state-oriented measures [[63](#fn:63)]. The trait-based nature of AMPs and job burnout thus explains the elevated CR values, which indicate strong internal consistency rather than redundancy. These results affirm the reliability of our trait constructs, consistent with psychometric expectations and the study’s objectives.\n\nRegarding discriminant validity, the square root of AVE values exceeded correlation values with other constructs [[82](#fn:82)]. In [Table 3](#behavsci-14-01123-t003), the square root of AVE values for each construct surpassed correlation values with other constructs, affirming the measurement scales’ ability to accurately differentiate between constructs.\n\n#### Table 3.\n\nDiscriminant validity values.\n\n| Factors | 1 | 2 | 3 | 4 | 5 |\n| --- | --- | --- | --- | --- | --- |\n| 1. AMPs (AMP) | ** *0.937* ** | *0.582* | *0.183* | *0.629* | *0.450* |\n| 2. Job Burnout (JBO) | 0.730 | ** *0.790* ** | *0.469* | *0.197* | *0.479* |\n| 3. Person–Job Fit (PJF) | −0.782 | −0.736 | ** *0.793* ** | *0.211* | *0.605* |\n| 4. Perceived Threat (PT) | 0.712 | 0.729 | −0.690 | ** *0.858* ** | *0.225* |\n| 5. Workplace Well-Being (WFW) | −0.337 | −0.364 | 0.300 | −0.405 | ** *0.729* ** |\n\n[Open in a new tab](https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/table/behavsci-14-01123-t003/)\n\nNote(s): Diagonals (in bold and italic) represent the square root of AVE, while the lower triangular pattern represents the shared variance (the squared correlations), and the upper triangular pattern (in italics) represents the HTMT correlation values.\n\nTo further confirm discriminant validity, we used the Heterotrait–Monotrait (HTMT) ratio as recommended by Henseler et al. [[83](#fn:83)], given its increased sensitivity in detecting discriminant validity issues in PLS-SEM models. HTMT values are presented in [Table 3](#behavsci-14-01123-t003), all of which meet the established threshold of 0.85, supporting discriminant validity across constructs [[84](#fn:84)]. Together with the Fornell–Larcker criterion, these HTMT results enhance the robustness of our measurement model, indicating that our constructs are distinct and reliable for accurately capturing the conceptual boundaries needed for analyzing variable inter-relationships.\n\n### 4.4. Direct Effects Estimations\n\nSEM was utilized to examine both the direct and indirect relationships among the study variables. This approach provides insights into the intricate pathways that underlie the impact of AMP on WFW in SMEs. After parameter estimation, bootstrapping was conducted to validate the robustness of the findings with 5000 Bootstrap samples generated through resampling with replacement from the original dataset [[85](#fn:85)]. Remarkably, the coefficients estimated through bootstrapping closely aligned with those derived from Smart PLS 4, affirming the reliability of the results [[72](#fn:72)].\n\nThe results of the structural model, delineated in [Table 4](#behavsci-14-01123-t004) and [Figure 3](#behavsci-14-01123-f003), shed light on the hypothesized direct relationships between AMPs, JBO, PT, and PJF with WFW, accounting for 61%, 56.6%, and 18% of the variation, respectively. Notably, AMPs were found to exert a significant positive effect on job burnout (β = 0.390, *p* < 0.001), affirming H2. Conversely, no support was found for H1, as AMPs exhibited a non-significant impact on workforce well-being (β = −0.064, *p* = 0.339). Furthermore, strong direct relationships were observed between JBO and WFW (β = −0.154, *p* < 0.05), validating H3. Moreover, AMPs had a robust positive direct effect on perceived threat (β = 0.432, *p* < 0.001), supporting H4. PT exhibited a strong negative direct effect on WFW (β = −0.323, *p* < 0.001), corroborating H5.\n\n#### Table 4.\n\nEstimates of direct effects.\n\n| Hypothesis | Hypothesized Relationships | Sample Estimate | Standard Error | T-Statistics | *p* Values | Decision |\n| --- | --- | --- | --- | --- | --- | --- |\n| H1 | AMP → WFW | −0.064 *n.s* | 0.067 | 0.965 | 0.339 | Not Supported |\n| H2 | AMP → JBO | 0.390 *** | 0.041 | 9.609 | 0.000 | Supported |\n| H3 | JBO → WFW | −0.154 * | 0.071 | 2.185 | 0.031 | Supported |\n| H4 | AMP → PT | 0.432 *** | 0.045 | 9.590 | 0.000 | Supported |\n| H5 | PT → WFW | −0.323 *** | 0.066 | 4.790 | 0.000 | Supported |\n\n[Open in a new tab](https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/table/behavsci-14-01123-t004/)\n\nNote(s): *n.s*, statistically non-significant relationships; *, statistically significant at *p* < 0.050; ***, statistically significant at *p* < 0.001.\n\n![Figure 3](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21f0/11672927/5ed5e3427914/behavsci-14-01123-g003.jpg)\n\n### 4.5. Mediation Effect Analysis\n\nTo examine the mediating roles of JBO and PT in the relationship between AMPs and WFW, mediation analyses were conducted following the guidelines outlined by [[86](#fn:86)]. As shown in [Table 5](#behavsci-14-01123-t005), JBO negatively and significantly mediated the relationship between AMPs and workforce well-being (β = −0.060, *p* < 0.05), providing support for H6. PT emerged as a significant mediator, with a negative and statistically significant indirect effect on WFW (β = −0.139, *p* < 0.001), thereby affirming H7. These findings highlight the multifaceted nature of the relationship between AMPs and workforce well-being, emphasizing the pivotal roles of JBO and PT as mediators in this dynamic interplay.\n\n#### Table 5.\n\nIndirect and interaction effect results.\n\n| Hypothesis | Hypothesized Relationships | Sample Estimate | Standard Error | T-Statistics | *p* Values | CIs | Decision |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| 0.025 | 0.975 |\n| H6 | AMP → JBO → WFW | −0.060 * | 0.029 | 2.080 | 0.038 | −0.117 | −0.006 | Supported |\n| H7 | AMP → PT → WFW | −0.139 *** | 0.031 | 4.532 | 0.000 | −0.206 | −0.083 | Supported |\n| H8 | AMP × PJF → JBO | 0.082 ** | 0.024 | 3.375 | 0.001 | 0.034 | 0.130 | Supported |\n| H9 | AMP × PJF → PT | 0.104 *** | 0.021 | 5.059 | 0.000 | 0.064 | 0.145 | Supported |\n| H10 | AMP × PJF → WFW | 0.071 † | 0.042 | 1.705 | 0.088 | −0.015 | 0.148 | Not Supported |\n\n[Open in a new tab](https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/table/behavsci-14-01123-t005/)\n\nNote(s): †, statistically significant at *p* < 0.100; *, statistically significant at *p* < 0.050; **, statistically significant at *p* < 0.010; ***, statistically significant at *p* < 0.001; CI, confidence interval.\n\n### 4.6. Analysis of the Moderating Effect of PJF\n\nThe examination of the moderating role of PJF in the relationship between AMPs and various outcome variables yielded insightful findings. Utilizing the product term by utilizing the PLS-SEM approach [[87](#fn:87),[88](#fn:88),[89](#fn:89)] and integrating PJF into the base model, interactions were explored to ascertain its influence on job burnout, perceived threat, and workforce well-being.\n\nThe statistical analysis, detailed in [Table 5](#behavsci-14-01123-t005), revealed significant interactions between AMPs and PJF, affirming H8 and H9. Specifically, the product term of AMP x PJF exhibited significant positive correlations with both job burnout (β = 0.082, *p* < 0.010) and perceived threat (β = 0.104, *p* < 0.001), indicating that PJF positively moderated the relationships between AMPs and these adverse outcomes.\n\nTo elucidate the moderating effects further, simple slope analysis was employed, revealing nuanced insights into the relationship dynamics. As illustrated in [Figure 4](#behavsci-14-01123-f004) and [Figure 5](#behavsci-14-01123-f005), when PJF is high, the positive effects of AMPs on job burnout and perceived threat are amplified. Conversely, when PJF is low, the magnitude of the positive effects increases, indicating that PJF strengthens the adverse impact of AMPs on both job burnout and perceived threat.\n\n![Figure 4](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21f0/11672927/7e67489378f1/behavsci-14-01123-g004.jpg)\n\n![Figure 5](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21f0/11672927/fc8d5f4a90d2/behavsci-14-01123-g005.jpg)\n\nHowever, contrary to expectations, the results pertaining to H10 did not provide support for the moderating effect of PJF on the relationship between AMPs and workforce well-being. The product term of AMP × PJF exhibited a marginally significant positive correlation with workforce well-being (β = 0.071, *p* < 0.100), suggesting a weak moderating influence of PJF. The simple slope analysis depicted in [Figure 6](#behavsci-14-01123-f006) illustrated intersecting lines for low and high levels of PJF, indicating that PJF marginally dampens the negative relationship between AMPs and workforce well-being.\n\n![Figure 6](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21f0/11672927/aaa00468a52d/behavsci-14-01123-g006.jpg)\n\n## 5. Discussion\n\nThis study investigates the complex relationships between algorithmic management practices, job burnout, perceived threat, workforce well-being, and the moderating role of person–job fit. The results provide valuable insights into how modern management practices influenced by technology can impact employees’ mental and emotional states and overall well-being. The results confirm that algorithmic management practices have a significant negative impact on workforce well-being. These practices often reduce employees’ autonomy, lead to constant surveillance, and create pressure to meet rigid performance metrics [[90](#fn:90)]. These factors collectively diminish job satisfaction, increase stress levels, and cause harm. The findings are consistent with self-determination theory, which emphasizes the importance of psychological well-being.\n\nThe analysis supports the hypothesis that algorithmic management practices contribute to higher levels of job burnout. The constant performance monitoring and pressure associated with algorithmic management practices exhaust employees’ emotional and physical reservoirs, resulting in fatigue and burnout. Also, [[6](#fn:6)] refers to the negative relationship between algorithmic management practices and workforce well-being. This aligns with conservation of resources theory, which asserts that stress arises from the risk of resource depletion or actual resource loss. The results confirm that job burnout substantially undermines workforce well-being. Job burnout, which is characterized by emotional fatigue and diminished professional effectiveness, directly influences employees’ mental and physical well-being, resulting in reduced job contentment and overall welfare [[91](#fn:91)].\n\nThe results indicate that algorithmic management practices heighten employees’ perceived threat. The impersonal nature of algorithms and the lack of human interaction can create feelings of job insecurity and anxiety about performance evaluations, contributing to a heightened sense of threat [[92](#fn:92)]. This research validates that perceived threat detrimentally influences workforce well-being. Employees facing perceived threats are prone to heightened stress and anxiety, resulting in adverse effects on their workforce well-being. The mediation analysis demonstrates that job burnout partially mediates the connection between algorithmic management practices and workforce well-being. While algorithmic management practices directly impact well-being, they channel a significant portion of this effect through the increased job burnout it causes [[44](#fn:44)].\n\nSimilarly, perceived threat and job burnout also mediate the relationship between algorithmic management practices and workforce well-being. Algorithmic management practices increase perceived threat, which in turn decreases workforce well-being. The findings emphasize the importance of addressing the psychological impacts of algorithmic management practices to mitigate their negative effects on well-being. Additionally, moderation analysis reveals that person–job fit weakens the positive relationship between algorithmic management practices and job burnout. Employees with a higher degree of person–job fit are better equipped to cope with the demands imposed by algorithmic management practices, experiencing less burnout compared to those with a lower degree of fit [[50](#fn:50)]. The findings also show that person–job fit moderates the relationship between algorithmic management practices and perceived threat. When employees have a higher degree of person–job fit, they are less likely to perceive algorithmic management practices as threatening, thereby reducing the negative impacts on their well-being.\n\nFinally, person–job fit moderates the direct relationship between algorithmic management practices and workforce well-being [[52](#fn:52),[54](#fn:54)]. Employees with a higher degree of person–job fit experience less negative impact on their well-being from algorithmic management practices compared to those with a lower degree of fit. This study highlights the significant negative impacts of algorithmic management practices on workforce well-being, primarily through increased job burnout and perceived threat. However, the presence of a positive person–job fit can mitigate these adverse effects, suggesting that organizations should consider the individual fit between employees and their roles when implementing algorithmic management practices. Ensuring that employees are well matched to their jobs can help alleviate some of the negative consequences of these modern management practices, ultimately supporting better workforce well-being. The integration of self-determination theory and conservation of resources theory provides a strong framework for understanding these dynamics and guiding future research and organizational strategies [[19](#fn:19)].\n\n## 6. Conclusions\n\n### 6.1. Theoretical Contribution\n\nBy examining the relationship between algorithmic management practices and workforce well-being, this research significantly advances our understanding of the intersection between technology-driven management and employee outcomes. Specifically, the integration of self-determination theory and conservation of resources theory offers a robust framework for analyzing how algorithmic management practices influence workforce well-being. Self-determination theory, which emphasizes the fulfillment of psychological needs such as autonomy, competence, and relatedness, helps explain how algorithmic management practices can undermine these needs, leading to decreased motivation and well-being among employees [[93](#fn:93)]. The rigid controls and constant monitoring associated with algorithmic management practices can diminish employees’ sense of autonomy and relatedness, resulting in lower job satisfaction and higher stress levels [[16](#fn:16)].\n\nOn the other hand, conservation of resources theory focuses on how individuals strive to obtain, retain, and protect resources, which are critical for their well-being. Algorithmic management practices’ potential to threaten or deplete these resources such as job security and personal agency can lead to heightened stress and job burnout, which negatively impacts workforce well-being [[22](#fn:22)]. This theoretical integration thus provides a comprehensive explanation for the mechanisms through which algorithmic management practices exert their effects on employees.\n\nBy examining the moderating role of person–job fit, this research sheds light on the conditions under which algorithmic management practices’ effects vary. The study shows that a higher degree of person–job fit can buffer the negative impacts of algorithmic management practices on employee outcomes by ensuring that job demands align with individual abilities, thereby reducing the stress and resource depletion that leads to job burnout [[94](#fn:94)]. This contribution is critical as it underscores the importance of considering individual differences and contextual factors in the application of algorithmic management practices, thereby enriching the literature on person–environment fit and employee well-being.\n\n### 6.2. Practical and Managerial Implications\n\nThe findings of this study carry significant practical and managerial implications for organizations adopting algorithmic management practices. Given the adverse effects of algorithmic management practices on workforce well-being, organizations need to implement strategies that mitigate these negative outcomes. First, organizations should prioritize the integration of person–job fit assessments in their algorithmic management practices systems. Ensuring that employees are well-matched to their roles can reduce the likelihood of job burnout and perceived threat, as well as enhance overall employee satisfaction and well-being. As research suggests, a higher degree of person–job fit can buffer the adverse effects of algorithmic management practices, leading to better employee outcomes [[94](#fn:94)].\n\nMoreover, transparency in algorithmic decision-making processes is crucial. Organizations should ensure that employees understand how decisions affecting their work are made and provide avenues for feedback and participation. This transparency can alleviate perceived threats and foster a sense of fairness and trust, which are critical for maintaining employee engagement and well-being [[23](#fn:23)].\n\nAdditionally, providing training and support tailored to the needs of employees working under algorithmic management practice systems can significantly improve their ability to cope with the demands of such environments. Training programs that enhance employees’ digital literacy and resilience can help them adapt to the technological changes imposed by algorithmic management practices, thus reducing job stress and improving workforce well-being [[49](#fn:49)]. Managers should be aware of the psychological impacts of algorithmic management practices and strive to create a work environment that supports autonomy, competence, and relatedness—key components of self-determination theory. By doing so, organizations can mitigate the dehumanizing effects of algorithmic management practices and promote a healthier, more supportive workplace [[16](#fn:16)].\n\n### 6.3. Limitations and Future Direction\n\nThis research drew upon a distinct sample from a specific sector or entity, potentially constraining the broader applicability of the results. Subsequent studies should strive to validate these findings across varied samples to bolster their external relevance. The use of a cross-sectional framework limits our ability to infer causality regarding the investigated relationships. Future investigations could utilize longitudinal or experimental methodologies to establish causal connections and gain deeper insights into the temporal patterns of algorithmic management practices and employee consequences. While this study examined job burnout and perceived threat as mediating variables and person–job fit as a moderating variable, there may be other mechanisms at play. Future research could explore additional mediators and moderators to provide a more comprehensive understanding of the processes underlying the relationships examined in this study. This research predominantly focused on algorithmic management practices in a broad context, but the impacts may fluctuate depending on contextual variables such as organizational ethos, industry standards, and cultural norms. Subsequent investigations could delve into how these contextual elements shape the conservation of resources theory relations scrutinized in this study. By acknowledging these constraints and exploring avenues for future research, we can improve our understanding of the influence of algorithmic management practices on employee welfare and inform strategies for promoting positive outcomes in the workplace.\n\n## Appendix A\n\n### Table A1.\n\nMeasurement items.\n\n**Algorithmic Management Practices [[6](#fn:6),[64](#fn:64)]** 1. In this establishment, the pace of work of many employees is determined by machines or computers.\n2. This establishment use data analytics to monitor employee performance.\n3. This establishment use data analytics to improve the processes of production or service delivery.\n4. Robots carry complex series of actions automatically, which may include the interaction with people. **Job Burnout [[65](#fn:65),[66](#fn:66)]** 1. Every day I always get up without spirit, facing the work is listless. - 2.I feel very tired after working all day. - 3.My work often makes me feel bored. - 4.I often need to consume too many emotional resources at work. - 5.I feel confused about my current career prospects. - 6.I have lost enthusiasm for my current job. - 7.I think my current job is of little value. - 8.I feel I’m not qualified for my current job. - 9.Leaders often put me in charge of unimportant things. - 10.I often have no confidence in the tasks assigned to me by the leader. **Perceived Threat [[68](#fn:68),[69](#fn:69)]** 1. The increased use of robots in our everyday life is causing more job loss for humans - 2.In the long run, robots pose a direct threat to human safety and well-being - 3.Recent advances in robot technology are challenging the essence of human life - 4.Technological advancements in the robotic are threatening human’s uniqueness **Person–Job Fit [[70](#fn:70)]** 1. The experience and skills required to operate service robots match my own experience, skills, and knowledge. - 2.I believe my skills and abilities match those required by the job. - 3.My job performance is hurt by a lack ofexpertise on the job. (R) - 4.I feel that this work enables me to do the kind of workI want to do. - 5.This work is a good match for me. - 6.This work is the type that I’m seeking. **Workplace Well-Being [[71](#fn:71)]** In the past 6 months, 1. In my work, I achieve my potential. - 2.In my work, I develop abilities that I consider important. - 3.In my work, I engage in activities that express my skills. - 4.In my work, I overcome challenges. - 5.In my work, I achieve results that I regard as valuable. - 6.In my work, I advance in the goals I set for my life. - 7.In my work, I do what I really like doing. - 8.In my work, I express what is best in me.\n[Open in a new tab](https://pmc.ncbi.nlm.nih.gov/articles/PMC11672927/table/behavsci-14-01123-t0A1/)\n\n## Author Contributions\n\nWriting—original draft, H.Z.; supervision, A.A.; project administration A.K. and A.B. All authors have read and agreed to the published version of the manuscript.\n\n## Institutional Review Board Statement\n\nThe study was conducted in accordance with the Declaration of Helsinki, and approved by the University of Mediterranean Karpasia, under approval number 2023-2024-Spring003-Tuesday, 28 May 2024.\n\n## Informed Consent Statement\n\nAll participants in this study provided their informed consent.\n\n## Data Availability Statement\n\nThe data from this study can be requested from the conservation of resources theory corresponding author, Ahmad Alzubi.\n\n## Conflicts of Interest\n\nThe authors report no conflicts of interest.\n\n## Funding Statement\n\nThis research received no external funding.\n\n## Footnotes\n\n**Disclaimer/Publisher’s Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). 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Provenance information\n\nIntroducing Provenance Information\n\n\n\n![FAIRPlus logo](https://fairplus.github.io/the-fair-cookbook/_static/images/fairplus-mini.png)\n\nRecipe Type\n\nBackground information\n\nAudience\n\nMaturity Level & Indicator\n\nnot applicable\n\nhover me Tooltip text\n\n\n\n## 5.1. Main Objectives\n\nIn all tasks of data integration, especially in the area of Pharma, ensuring trust in data sources is essential. The steps taken to ensure new datasets or sources of information meet a number of criteria ascertaining some level of quality are many. One of them is a check on the origin of the information, in other words, its **Provenance**. Provenance covers the elements detailing how the data was produced by identifying the agents (human, software, workflows) so a certain level of traceability and accountability can be established. The notions of **audit and trail** as well as **versioning** and **authorship** are essential to be able, should any distortion be identified in downstream analysis, to trace back to possible sources of error.\n\n\n\n## 5.2. Provenance: a definition\n\n**“Provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness”**. Provenance has been an active field of academic research and several models have been developed over the year to cover the domain. The next section will introduce the most known models as well as detail how their overlap and what are the differences between these.\n\n**Data provenance** is also referred to as **data lineage**\n\n\n\n### 5.2.1. OPM Open Provenance Model\n\nPublished in 2011, the Open Provenance Model (OPM) was the first formal model developed to cater for the reporting of provenance information with several main goals in mind:\n\n- provide a domain agnostic, formal and actionable definition of provenance\n- enable sharing of provenance information between systems, an issue of interoperability\n- support tool development and implementation\n- enable validation of provenance messages. The work was well-received and several workshops and working groups refined the specification which resulting in a specification being submitted to the World Wide Web Consortium. The following sections provides further insights into these efforts.\n\n\n\n\n### 5.2.2. The PROV Data Model\n\nThis definition is taken from the W3C Provenance Data Model specifications.\n\n\n\n### 5.2.3. PROV vocabulary:\n\nThe PROV-O Provenance Ontology is a W3C-vetted specification of the Provenance Data Model as an OWL ontology. The namespace for PROV-O is [http://www.w3.org/ns/prov#](http://www.w3.org/ns/prov). It is meant to allow expressing provenance information as modeled in the provenance model [W3C PROV-DM](https://www.w3.org/TR/prov-overview/) with Classes, Properties and relations defined in the W3C Ontology Web Language (OWL). Instances can therefore be represented in RDF and distributed in any of the official serialization, e.g. JSON-LD, RDF/XML, Turtle.\n\nBelow is an example of provenance information represented using the PROV-O ontology and serialized as RDF statements using turtle representation.\n\n\n\n```\n@prefix xsd: .\n@prefix foaf: .\n@prefix prov: .\n@prefix : .\n\n:bar_chart\na prov:Entity;\nprov:wasGeneratedBy :illustrationActivity;\nprov:wasDerivedFrom :aggregatedByRegions;\nprov:wasAttributedTo :derek;\n.\n\n:derek\na foaf:Person, prov:Agent;\nfoaf:givenName \"Derek\";\nfoaf:mbox ;\nprov:actedOnBehalfOf :natonal_newspaper_inc;\n.\n\n:national_newspaper_inc\na foaf:Organization, prov:Agent;\nfoaf:name \"National Newspaper, Inc.\";\n.\n\n:illustrationActivity\na prov:Activity;\nprov:used :aggregatedByRegions;\nprov:wasAssociatedWith :derek;\nprov:wasInformedBy :aggregationActivity;\n.\n\n:aggregatedByRegions\na prov:Entity;\nprov:wasGeneratedBy :aggregationActivity;\nprov:wasAttributedTo :derek;\n.\n\n:aggregationActivity\na prov:Activity;\nprov:startedAtTime \"2011-07-14T01:01:01Z\"^^xsd:dateTime;\nprov:wasAssociatedWith :derek;\nprov:used :crimeData;\nprov:used :nationalRegionsList;\nprov:endedAtTime \"2011-07-14T02:02:02Z\"^^xsd:dateTime;\n.\n\n:crimeData\na prov:Entity;\nprov:wasAttributedTo :government;\n.\n:government a foaf:Organization, prov:Agent .\n\n:nationalRegionsList\na prov:Entity;\nprov:wasAttributedTo :civil_action_group;\n.\n:civil_action_group a foaf:Organization, prov:Agent .\n```\n\n\n\n\n## 5.3. Tools for creating provenance metadata\n\nIn the previous section, we have detailed the landscape of formal models to represent provenance information. In this section, we will show how tools have implemented these models, or domain-specific extension of them as in the case of computation workflow provenance information.\n\n\n\n### 5.3.1. CamFLow\n\n[CamFlow](https://camflow.org/#output_format) is a Linux Security Module (LSM) designed to capture data provenance for the purpose of system audit and aims at capture information flow.\n\nCamFlow support 2 output formats.\n\n- W3C PROV-JSON format\n\n```\n\"ABAAAAAAACAe9wIAAAAAAE7aeaI+200UAAAAAAAAAAA=\": {\n\"cf:id\": \"194334\",\n\"prov:type\": \"fifo\",\n\"cf:boot_id\": 2725894734,\n\"cf:machine_id\": 340646718,\n\"cf:version\": 0,\n\"cf:date\": \"2017:01:03T16:43:30\",\n\"cf:jiffies\": \"4297436711\",\n\"cf:uid\": 1000,\n\"cf:gid\": 1000,\n\"cf:mode\": \"0x1180\",\n\"cf:secctx\": \"unconfined_u:unconfined_r:unconfined_t:s0-s0:c0.c1023\",\n\"cf:ino\": 51964,\n\"cf:uuid\": \"32b7218a-01a0-c7c9-17b1-666f200b8912\",\n\"prov:label\": \"[fifo] 0\"\n}\n```\n\n\nExample of a write edge in W3C PROV format:\n\n\n\n```\n\"QAAAAAAAQIANAAAAAAAAAE7aeaI+200UAAAAAAAAAAA=\": {\n\"cf:id\": \"13\",\n\"prov:type\": \"write\",\n\"cf:boot_id\": 2725894734,\n\"cf:machine_id\": 340646718,\n\"cf:date\": \"2017:01:03T16:43:30\",\n\"cf:jiffies\": \"4297436711\",\n\"prov:label\": \"write\",\n\"cf:allowed\": \"true\",\n\"prov:activity\": \"AQAAAAAAAEAf9wIAAAAAAE7aeaI+200UAQAAAAAAAAA=\",\n\"prov:entity\": \"ABAAAAAAACAe9wIAAAAAAE7aeaI+200UAQAAAAAAAAA=\",\n\"cf:offset\": \"0\"\n}\n```\n\n\nNote\n\nCamFlow also concerns itself with a informatics system audit and as obvious from its design, is meanly focused on the security of Linux based systems. The CamFlow framework is highly configurable and allows `profiles` to be defined. This is a very low level solution and its use remains the confines to information security specialists.\n\n\nWhile this tool is probably of limited use to Life Sciences and bioinformatics applications, we mention it for two reasons:\n\n- it provides one of the first example of an implementation (even if very basic) making use of the W3C Prov-O model.\n- it provides an insight on how computational tool can deal with provenance information in ways which allow assessing systems health from a security point of view.\n\n\n### 5.3.2. Computational workflows and Provenance information:\n\nAs seen when introducing the W3C Provenance Data Model and Provenance Ontology, three key entities are necessary to record and track lineage information:\n\n- The Entity the origin of which we are concerned with.\n- The Activity which resulted in the creation of the Entity\n- The Agent which performed the Activity aforementioned. Because the PROV model is domain agnostic and very generic, it can be either applied as is, to any situation or it can also be extended and specialized to suit a particular domain on knowledge. To give an example, tracking provenance information in the field of bioinformatics shares features with provenance tracking as used in manufacturing, but it also has a number of specific features. In the field of bioinformatics, where computational pipelines, known as workflows, are assembled to process ever larger datasets on high performance computing infrastructure and cloud infrastructure, having the ability to access data lineage matters for a number for reasons, such as:\n\n- audit and trail tasks for regulatory compliance, where every step of the data processing needs to be documented for submission.\n- resource optimization and energy savings (e.g. should a workflow be executed again or not) It is those features which have been the focus of an extension as part of the work on computational biology workflows with CWLProv, and which we will now cover.\n\n\n\n#### 5.3.2.1. Example of CWLProv document\n\nThe infobox below shows an example of CWLProv RDF document which details how provenance information about the execution of CWL coded workflow may be represented. This document is take from the [CWLProv github repository](https://github.com/common-workflow-language/cwlprov/blob/main/prov.md). Such documents are generated and consumed by a feasibility demonstrator tool such as [CWLtool](https://faircookbook.elixir-europe.org/content/recipes/reusability.html)\n\n\n\n```\nprefix id\nprefix provenance\n\nagent(id:a606d227-bf10-4479-8d11-823bb932bbac,\n[prov:type='wfprov:WorkflowEngine', prov:type='prov:SoftwareAgent',\nprov:label=\"cwltool 1.0.20180817162414\"])\n\nactivity(id:73eab018-7b36-4f84-a845-aca8073bd46c, 2018-08-21T15:20:35.059920, -,\n[prov:type='wfprov:WorkflowRun', prov:label=\"Run of workflow/packed.cwl#main\"])\nwasStartedBy(id:73eab018-7b36-4f84-a845-aca8073bd46c, -, id:a606d227-bf10-4479-8d11-823bb932bbac, 2018-08-21T15:20:35.060038)\n\nactivity(id:e79fc8dc-6e40-4236-b22c-41fee22947a9, -, -,\n[prov:type='wfprov:ProcessRun', prov:label=\"Run of workflow/packed.cwl#main/compile\"])\nwasStartedBy(id:e79fc8dc-6e40-4236-b22c-41fee22947a9, -, id:73eab018-7b36-4f84-a845-aca8073bd46c, 2018-08-21T15:20:35.163189)\n\nactivity(id:e79fc8dc-6e40-4236-b22c-41fee22947a9, -, -,\n[prov:has_provenance='provenance:workflow_compile.e79fc8dc-6e40-4236-b22c-41fee22947a9.cwlprov.provn',\nprov:has_provenance='provenance:workflow_compile.e79fc8dc-6e40-4236-b22c-41fee22947a9.cwlprov.ttl'\n])\n```\n\n\nNote\n\nSpecific recipes about FAIR workflows are available in a specific chapter of the book.\n\n\nNote\n\nHow widely supported are CWLprov documents?\n\nThis is very part of academic research, meaning that these specifications demonstrate feasibility and capability. A number of collaborators of the CWLProv group have included such provenance information in their frameworks. Tools such as `Bagit` or specifications such as `Research Objects` recommended CWLProv documents to be included in specific folders of the archives when preparing them.\n\nSee the [BagIt profile](https://github.com/common-workflow-language/cwlprov/blob/main/bagit.md) for details on the CWLProv folder structure, and the [Research Object profile](https://github.com/common-workflow-language/cwlprov/blob/main/ro.md) on how to declare the typing of the PROV files.\n\n\n\n\n#### 5.3.2.2. CWLtool: a component to manage workflow information and generate CWLProv information\n\n‘cwltool’ for Common Workflow Language tool is a python reference implementation for the [Common Workflow Language](https://www.commonwl.org/), which means that it supports the full set of CWL specifications and provides validation functions to check CWL documents.\n\n\n\n```\npip install cwltool\n```\n\n\ncwltool is not the only implementation of the CWL specifications, others such as [Arvados](https://arvados.org/) and [Toil](https://toil.ucsc-cgl.org/) exist. In case these distinct implementations are also installed on the system, one needs to make sure a helper tool known as ‘cwl-runner’ is also installed. The *key* function of ‘cwl-runner’ is to allow users to select which CWL implementation will be executed.\n\n\n\n```\npip install cwltool-runner\n```\n\n\nFor the purpose of this recipe, which is to show how provenance information can be generated by a tool such as cwltool, users will need to make sure that a workflow is available before performing this conversion.\n\n\n\n```\ncwltool --print-rdf --rdf-serializer=turtle mywf.cwl\n```\n\n\n\n\n#### 5.3.2.3. CWLProv-py\n\nThis tool, also an output from the Common Workflow Language consortium, is solely intended as a validator for provenance information when available from Research Objects. It is a standalone python package, which provides a command line interface (CLI) to read, inspect research objects capturing workflow execution information using the CWL syntax.\n\nTo install the package, simply run the standard python install package pip:\n\n\n\n```\npip install cwlprov\n```\n\n\nTo run ‘cwlprov’ following installation and using an exemplar CWL file, run the following command:\n\n\n\n```\ncwlprov --quiet --directory ./test/1.cwl validate\n```\n\n\n\n\n\n### 5.3.3. Provenance and distributed ledger technology\n\nWith this section, we intend to cover another aspect associated with provenance information. It is the issue of trustworthiness of the information and how to ascertain that even if a provenance information message is valid, it actually contains authentic and untempered information.\n\nThe raise of digital currencies such as bitcoin or ethereum has popularized the notion of ‘blockchain’, which is a type of what is known as “distributing ledger technology” (DLT). Blockchains and DLT in general represent technological solution to the problem of trust in provenance. To achieve this goal, DLT relies on three key principles:\n\n- distribution of information: in other words, the system is decentralized, meaning that no single entity holds the database. Instead, a multitude of copies of the information are available from a myriad of independent nodes.\n- transparency: this is a consequence of the distributed nature of the architecture. Transparency is understood as the availability of means of verification by being able to access independent sources of the reference information. This allows consistency checks to be performed and therefore provides the means of tempering detection. Note\n\nTransparency does not equate open. Some blockchains are public but others are private, for instance to support domain specific applications ranging from financial services, supply chain or other sensitive area such as healthcare information.\n\n\n- immutability: this is the last tenet of DLT and refers to the fact that once an entry has been to the distributed ledger, it is digitally signed and synchronized in the network by adding a new element (the block) to the blockchain. Each of the new is digitally signed via some cryptographic algorithm (e.g. Blake2). Any attempt to modify a particular block would result in changing its signature. This can be done but the cost could be prohibitively high as it would entail modifying all subsequent blocks in the chain. This architecture ensures the stability or immutability of the ledger. Important\n\nThis notion of immutability assumes that cryptographic hashes using to digital proof the contracts can not be broken. The most advanced algorithms somehow guarantee this given the current compute power available. However, it should not be taken for granted. A situation where cryptographic methods would be breached by disruptive technology such as breakthrough in quantum computing, are referred to as a `cryptographic armageddon` as it would wreak havoc on the entire edifice by allowing to rewrite a ledger, thus voiding trust in an instant. Despite such alarmist claims, quantum computing methods could make attempts to tamper with a ledger impossible, thus reinforcing the approach.\n\n\n\n\n\n## 5.4. Conclusion\n\nWith this FAIR Cookbook content, we have introduced the notion of Provenance information, providing a brief historical review of the domain model and given a few examples of tools implementing provenance information tracking.\n\nFor a more in depth exploration of provenance information, we encourage our readers to follow up with more detailed material listed below:\n\n\n\n## 5.5. References\n\n\n\n## 5.6. Authors\n\n\n\n## 5.7. License\n\n\n\n---\n\nContents\n\n1. Published in 2011, the Open Provenance Model (OPM) was the first formal model developed to cater for the reporting of provenance information with several main goals in mind: - provide a domain agnostic, formal and actionable definition of provenance [↩](#fnref:1) - enable sharing of provenance information between systems, an issue of interoperability - support tool development and implementation - enable validation of provenance messages.The work was well-received and several workshops and working groups refined the specification which resulting in a specification being submitted to the World Wide Web Consortium. The following sections provides further insights into these efforts.\n2. This definition is taken from the W3C Provenance Data Model specifications [↩](#fnref:2)\n3. The PROV-O Provenance Ontology is a W3C-vetted specification of the Provenance Data Model as an OWL ontology. The namespace for PROV-O is [http://www.w3.org/ns/prov#](http://www.w3.org/ns/prov). It is meant to allow expressing provenance information as modeled in the provenance model [W3C PROV-DM](https://www.w3.org/TR/prov-overview/) with Classes, Properties and relations defined in the W3C Ontology Web Language (OWL). Instances can therefore be represented in RDF and distributed in any of the official serialization, e.g. JSON-LD, RDF/XML, Turtle. Below is an example of provenance information represented using the PROV-O ontology and serialized as RDF statements using turtle representation. ``` @prefix xsd: . @prefix foaf: . @prefix prov: . @prefix : . :bar_chart a prov:Entity; prov:wasGeneratedBy :illustrationActivity; prov:wasDerivedFrom :aggregatedByRegions; prov:wasAttributedTo :derek; . :derek a foaf:Person, prov:Agent; foaf:givenName \"Derek\"; foaf:mbox ; prov:actedOnBehalfOf :natonal_newspaper_inc; . :national_newspaper_inc a foaf:Organization, prov:Agent; foaf:name \"National Newspaper, Inc.\"; . :illustrationActivity a prov:Activity; prov:used :aggregatedByRegions; prov:wasAssociatedWith :derek; prov:wasInformedBy :aggregationActivity; . :aggregatedByRegions a prov:Entity; prov:wasGeneratedBy :aggregationActivity; prov:wasAttributedTo :derek; . :aggregationActivity a prov:Activity; prov:startedAtTime \"2011-07-14T01:01:01Z\"^^xsd:dateTime; prov:wasAssociatedWith :derek; prov:used :crimeData; prov:used :nationalRegionsList; prov:endedAtTime \"2011-07-14T02:02:02Z\"^^xsd:dateTime; . :crimeData a prov:Entity; prov:wasAttributedTo :government; . :government a foaf:Organization, prov:Agent . :nationalRegionsList a prov:Entity; prov:wasAttributedTo :civil_action_group; . :civil_action_group a foaf:Organization, prov:Agent . ```\n4. [CamFlow](https://camflow.org/#output_format) is a Linux Security Module (LSM) designed to capture data provenance for the purpose of system audit and aims at capture information flow. CamFlow support 2 output formats. - W3C PROV-JSON format [↩](#fnref:4) ``` \"ABAAAAAAACAe9wIAAAAAAE7aeaI+200UAAAAAAAAAAA=\": { \"cf:id\": \"194334\", \"prov:type\": \"fifo\", \"cf:boot_id\": 2725894734, \"cf:machine_id\": 340646718, \"cf:version\": 0, \"cf:date\": \"2017:01:03T16:43:30\", \"cf:jiffies\": \"4297436711\", \"cf:uid\": 1000, \"cf:gid\": 1000, \"cf:mode\": \"0x1180\", \"cf:secctx\": \"unconfined_u:unconfined_r:unconfined_t:s0-s0:c0.c1023\", \"cf:ino\": 51964, \"cf:uuid\": \"32b7218a-01a0-c7c9-17b1-666f200b8912\", \"prov:label\": \"[fifo] 0\" } ``` Example of a write edge in W3C PROV format: ``` \"QAAAAAAAQIANAAAAAAAAAE7aeaI+200UAAAAAAAAAAA=\": { \"cf:id\": \"13\", \"prov:type\": \"write\", \"cf:boot_id\": 2725894734, \"cf:machine_id\": 340646718, \"cf:date\": \"2017:01:03T16:43:30\", \"cf:jiffies\": \"4297436711\", \"prov:label\": \"write\", \"cf:allowed\": \"true\", \"prov:activity\": \"AQAAAAAAAEAf9wIAAAAAAE7aeaI+200UAQAAAAAAAAA=\", \"prov:entity\": \"ABAAAAAAACAe9wIAAAAAAE7aeaI+200UAQAAAAAAAAA=\", \"cf:offset\": \"0\" } ``` Note CamFlow also concerns itself with a informatics system audit and as obvious from its design, is meanly focused on the security of Linux based systems. The CamFlow framework is highly configurable and allows `profiles` to be defined. This is a very low level solution and its use remains the confines to information security specialists. While this tool is probably of limited use to Life Sciences and bioinformatics applications, we mention it for two reasons: - it provides one of the first example of an implementation (even if very basic) making use of the W3C Prov-O model. - it provides an insight on how computational tool can deal with provenance information in ways which allow assessing systems health from a security point of view.\n5. It is those features which have been the focus of an extension as part of the work on computational biology workflows with CWLProv, and which we will now cover.[↩](#fnref:5)\n6. For the purpose of this recipe, which is to show how provenance information can be generated by a tool such as cwltool, users will need to make sure that a workflow is available before performing this conversion.[↩](#fnref:6) ``` cwltool --print-rdf --rdf-serializer=turtle mywf.cwl ```\n7. This tool, also an output from the Common Workflow Language consortium, is solely intended as a validator for provenance information when available from Research Objects. It is a standalone python package, which provides a command line interface (CLI) to read, inspect research objects capturing workflow execution information using the CWL syntax. To install the package, simply run the standard python install package pip: ``` pip install cwlprov ``` To run ‘cwlprov’ following installation and using an exemplar CWL file, run the following command:[↩](#fnref:7) ``` cwlprov --quiet --directory ./test/1.cwl validate ```\n8. Note Transparency does not equate open. Some blockchains are public but others are private, for instance to support domain specific applications ranging from financial services, supply chain or other sensitive area such as healthcare information.[↩](#fnref:8) - immutability: this is the last tenet of DLT and refers to the fact that once an entry has been to the distributed ledger, it is digitally signed and synchronized in the network by adding a new element (the block) to the blockchain. Each of the new is digitally signed via some cryptographic algorithm (e.g. Blake2). Any attempt to modify a particular block would result in changing its signature. This can be done but the cost could be prohibitively high as it would entail modifying all subsequent blocks in the chain. This architecture ensures the stability or immutability of the ledger.\n9. Important This notion of immutability assumes that cryptographic hashes using to digital proof the contracts can not be broken. The most advanced algorithms somehow guarantee this given the current compute power available. However, it should not be taken for granted. A situation where cryptographic methods would be breached by disruptive technology such as breakthrough in quantum computing, are referred to as a `cryptographic armageddon` as it would wreak havoc on the entire edifice by allowing to rewrite a ledger, thus voiding trust in an instant. Despite such alarmist claims, quantum computing methods could make attempts to tamper with a ledger impossible, thus reinforcing the approach.[↩](#fnref:9)"},"author":{"name":"fairplus.github.io","bio":"An online, open and live resource for the Life Sciences with recipes that help you to make and keep data Findable, Accessible, Interoperable and Reusable; in one word FAIR."},"metadata":{"timestamp":"2026-08-20T08:39:05.280Z"},"media":[{"type":"image"}],"aiComments":[]},{"id":"web-rscvnm","shareId":"pt15hu07wD","platform":"web","url":"https://rudof-project.github.io/tutorials/intro.html","title":"Shaping Knowledge Graphs — Shaping Knowledge Graphs","shareMode":"full","createdAt":1787215074691,"postedAt":"2026-08-20T08:37:49.853Z","publishedDate":"2026-08-20T08:37:49.853Z","archivedAt":"2026-08-20T08:37:51.800Z","archivedDate":"2026-08-20T08:37:51.800Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/pt15hu07wD","contentType":"article","content":{"text":"## Shaping Knowledge Graphs\n\nThis is a Jupyter book that introduces Knowledge Graphs concepts using [rudof](https://rudof-project.github.io/rudof/)\n\n- [Short introduction to Rudof](https://rudof-project.github.io/tutorials/rudof.html)\n- [RDF](https://rudof-project.github.io/tutorials/rdf.html)\n- [SPARQL](https://rudof-project.github.io/tutorials/sparql.html)\n- [ShEx](https://rudof-project.github.io/tutorials/shex.html)\n- [SHACL](https://rudof-project.github.io/tutorials/shacl.html)\n- [Introduction to DCTAP](https://rudof-project.github.io/tutorials/dctap.html)\n- [RDF 1.2](https://rudof-project.github.io/tutorials/rdf12.html)\n- [SPARQL 1.2](https://rudof-project.github.io/tutorials/sparql12.html)\n- [Using rudof to compare schemas](https://rudof-project.github.io/tutorials/compare.html)\n- [Service descriptions](https://rudof-project.github.io/tutorials/service_description.html)\n- [Generating RDF Data from shapes](https://rudof-project.github.io/tutorials/rudof_generate.html)"},"author":{"name":"rudof-project.github.io"},"metadata":{"timestamp":"2026-08-20T08:37:49.853Z"},"media":[],"aiComments":[]},{"id":"web-hgn3z4","shareId":"mTLeZpQkLb","platform":"web","url":"https://github.com/rudof-project/tutorials","title":"rudof-project/tutorials: Tutorials using rudof","shareMode":"full","createdAt":1787215071513,"postedAt":"2026-08-20T08:37:44.459Z","publishedDate":"2026-08-20T08:37:44.459Z","archivedAt":"2026-08-20T08:37:46.733Z","archivedDate":"2026-08-20T08:37:46.733Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/mTLeZpQkLb","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/pW4HQD0ybK/media/0.png","contentType":"article","content":{"text":"## rudof tutorials\n\nThis repository contains the source code of some [Jupiter books](https://rudof-project.github.io/tutorials/intro.html) that can be used as small tutorials of [rudof](https://rudof-project.github.io/).\n\nSome links:\n\n- [Jupiter-books reference](https://jupyterbook.org/en/stable/start/overview.html)\n\n## 🚀 Quick Start\n\n\n\n### 1. Clone and install\n\n\n\n```\ngit clone https://github.com/rudof-project/tutorials.git\ncd tutorials\npip install -r requirements.txt\n```\n\n\n\n### 2. Build the book\n\n\n\n```\njupyter-book build rudof\n```\n\n\n\n### 3. Manually publish books on gh-pages\n\nNote, this is no longer necessary because we publish the book through github Actions.\n\n\n\n```\nghp-import -n -p -f rudof/_build/html\n```\n\n\n\n### Cleaning build files and cache\n\nSometimes it is necessary to clean the cache when there are new versions of the libraries. To clean the built drectory use:\n\n\n\n```\njupyter-book clean rudof\n```\n\nIf you want to clean also the `jupyter_cache`, use:\n\n\n\n```\njupyter-book clean rudof --all\n```"},"author":{"name":"rudof-project","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/5fe11ee95321d314/avatar-6a9577cd4f7f.svg","bio":"Tutorials using rudof. Contribute to rudof-project/tutorials development by creating an account on GitHub."},"metadata":{"timestamp":"2026-08-20T08:37:44.459Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/pW4HQD0ybK/media/0.png","type":"image"}],"aiComments":[]},{"id":"web-85bz9q","shareId":"PQ1IpALFJ0","platform":"web","url":"https://github.com/labra/HTML2Doc","title":"labra/HTML2Doc: HTML -> Doc converter","shareMode":"full","createdAt":1787214930521,"postedAt":"2026-08-20T08:35:24.082Z","publishedDate":"2026-08-20T08:35:24.082Z","archivedAt":"2026-08-20T08:35:26.713Z","archivedDate":"2026-08-20T08:35:26.713Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/PQ1IpALFJ0","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/lhpCpZZyz2/media/0.png","contentType":"article","content":{"text":"[Branches](https://github.com/labra/HTML2Doc/branches) [Tags](https://github.com/labra/HTML2Doc/tags)\n\n[15 Commits](https://github.com/labra/HTML2Doc/commits/main/)\n\n| Name | Name | Last commit message | Last commit date |\n| --- | --- | --- | --- |\n| example | example |\n| ieee | ieee |\n| shex | shex |\n| utils | utils |\n| .gitignore | .gitignore |\n| LICENSE | LICENSE |\n| README.md | README.md |\n| index-dump.html | index-dump.html |\n| View all files |\n\n## HTML -> Doc converter\n\nThis repo contains some examples and attempts to convert HTML documents to Word documents.\n\nThe main use case is to convert ShEx spec files written in HTML with ReSpec to IEEE Word files.\n\nWe have a dedicated folder for that in `./shex` folder.\n\nWorkflow slides: [https://docs.google.com/presentation/d/1jYvYnM9rn5F_13q88JOHVqggE5l-gRPVMIy8h8sRxwI/edit?usp=sharing](https://docs.google.com/presentation/d/1jYvYnM9rn5F_13q88JOHVqggE5l-gRPVMIy8h8sRxwI/edit?usp=sharing)"},"author":{"name":"labra","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/63048d331b1be938/avatar-6a9577cd4f7f.svg","bio":"HTML -> Doc converter. Contribute to labra/HTML2Doc development by creating an account on GitHub."},"metadata":{"timestamp":"2026-08-20T08:35:24.082Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/lhpCpZZyz2/media/0.png","type":"image"}],"aiComments":[]},{"id":"web-7n0okd","shareId":"BLMIRfSYy6","platform":"web","url":"https://github.com/rudof-project/rudof","title":"rudof-project/rudof: RDF data shapes implementation in Rust","shareMode":"full","createdAt":1787214880325,"postedAt":"2026-08-20T08:34:28.254Z","publishedDate":"2026-08-20T08:34:28.254Z","archivedAt":"2026-08-20T08:34:30.277Z","archivedDate":"2026-08-20T08:34:30.277Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/BLMIRfSYy6","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/BUIoNWgCM9/media/0.png","contentType":"article","content":{"text":"## rudof\n\n[![Latest Version](https://camo.githubusercontent.com/7113f2c603d987f480719e69e8fff714efe9f6678e16f460fdbdec8364e44525/68747470733a2f2f696d672e736869656c64732e696f2f6372617465732f762f7275646f662d636c692e737667)](https://crates.io/crates/rudof-cli) [![PyPI](https://camo.githubusercontent.com/a9835d7108de1a2a9541fbf9064f650812220df8bb1b4b73a079e9e12d7bbb6d/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f70797275646f66)](https://pypi.org/project/pyrudof/) https://github.com/rudof-project/rudof/actions/workflows/ci.yml [![dependency status](https://camo.githubusercontent.com/0e60d601d02beaf64ae36ff92ee1c45a0f2f3dd03678b7a5a03037b61345a038/68747470733a2f2f646570732e72732f7265706f2f6769746875622f7275646f662d70726f6a6563742f7275646f662f7374617475732e737667)](https://deps.rs/repo/github/rudof-project/rudof) [![CodeScene general](https://camo.githubusercontent.com/a19c43e65bf431a63e0f0ba73310c57bbc067e30f1af0492ce21da794d681072/68747470733a2f2f636f64657363656e652e696f2f696d616765732f616e616c797a65642d62792d636f64657363656e652d62616467652e737667)](https://codescene.io/projects/72637) [![pre-commit](https://camo.githubusercontent.com/f0a86ea5e994668cd6abf01bc5acfe5b602e4d5e8fd3fcb123446a74e2bc49de/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7072652d2d636f6d6d69742d656e61626c65642d627269676874677265656e3f6c6f676f3d7072652d636f6d6d6974)](https://github.com/pre-commit/pre-commit)\n\nThis repo contains an RDF and Knowledge Graphs processing tool implemented in Rust.\n\nThe implementation supports RDF and property graphs data, as well as [ShEx](http://shex.io/), [SHACL](https://www.w3.org/TR/shacl/), [DCTap](https://www.dublincore.org/specifications/dctap/), [PGSchema](https://dl.acm.org/doi/10.1145/3589778), and conversions between different RDF data modeling formalisms.\n\nThe code can be used as a Rust library but it also contains a binary called `rudof` which can be used as an RDF playground.\n\nWe provide binaries for Linux, Windows, Mac and Docker (see [releases](https://github.com/rudof-project/rudof/releases)), as well as Python bindings.\n\n\n\n## Features\n\n`rudof` currently supports the following:\n\n- RDF and RDF 1.2 parsing, conversion and visualization.\n- SPARQL querying to RDF data and endpoints\n- Parsing SPARQL service description\n- ShEx\n- SHACL\n- DCTAP\n- Property graphs and Schemas for Property Graphs ([PGSchema](https://github.com/rudof-project/rudof/blob/master) [https://dl.acm.org/doi/10.1145/3589778](https://dl.acm.org/doi/10.1145/3589778)) Future features we are planning to add:\n\n- rdf-config\n- LinkML\n\n## Installation\n\n\n\n### Official releases\n\nYou can download a binary from the [latest release](https://github.com/rudof-project/rudof/releases/latest) page. There you will also find the compiled packages for the installation on your system using a package manager.\n\n\n\n#### Published Debian packages\n\nrudof is available as a [Debian package](https://packages.debian.org/source/sid/rudof) for Debian-based distributions (like Ubuntu).\n\nIt is also possible to download the binary from [https://github.com/rudof-project/rudof/releases](https://github.com/rudof-project/rudof/releases) and install the `.deb` package running the following commands after replacing X.X.X by the latest version:\n\n\n\n```\nwget https://github.com/rudof-project/rudof/releases/download/vX.X.X/rudof_vX.X.X_amd64.deb\nsudo dpkg -i rudof_vX.X.X_amd64.deb\n```\n\n\n\n#### Windows\n\nThe binary can be downloaded from [https://github.com/rudof-project/rudof/releases](https://github.com/rudof-project/rudof/releases).\n\n\n\n#### Mac\n\nThe binary is available at: [https://github.com/rudof-project/rudof/releases](https://github.com/rudof-project/rudof/releases) so you can download the corresponding binary to your machine.\n\nThe usual way to run/install a binary in Mac is to download it in a folder, add that folder to your PATH and activating the binary using:\n\n\n\n```\nchmod +x\n```\n\nAfter that, the processor may complain the first time about security and you have to agree to use it. Once you agree, it should work.\n\nCompiling from source\n\n### Compiling from source\n\n`rudof` has been implemented in Rust and is compiled using [cargo](https://doc.rust-lang.org/cargo/). The command `cargo run` can be used to compile and run locally the code.\n\nFor example:\n\n\n\n```\ncargo run -- validate examples/user.ttl --schema examples/user.shex --shapemap examples/user.sm\n```\n\n\n\n#### Troubleshooting\n\nIf the example doesn’t work as expected, here are a few things you can try:\n\n- **Use the --release flag** to compile in release mode, which can resolve some build issues and improve performance:\n\n```\ncargo run --release -- validate examples/user.ttl --schema examples/user.shex --shapemap examples/user.sm\n```\n\n- **Run the command inside WSL** (Windows Subsystem for Linux). If you're using Windows, compiling the project in WSL can help resolve environment-related issues, as Rust tends to compile more reliably and efficiently in Linux-based systems.\n\n### Compiling from source and installing the binary (Debian)\n\nInstall `cargo deb` (only the first time)\n\n\n\n```\ncargo install cargo-deb\n```\n\nCreate the `.deb` package by:\n\n\n\n```\ncargo deb\n```\n\nAnd run:\n\n\n\n```\nsudo dpkg -i target/debian/rudof__amd64.deb\n```\n\n\n\n### Alternative for Linux\n\nTo create a binary for Linux with debug information:\n\n\n\n```\ncargo build --target x86_64-unknown-linux-gnu\n```\n\nThe binary will be created in: `target/x86_64-unknown-linux-gnu/debug/rudof`\n\nIf you want a release binary which is more optimized, you can run:\n\n\n\n```\ncargo build --target x86_64-unknown-linux-gnu\n```\n\nIn this case, the binary will be `target/x86_64-unknown-linux-gnu/release/rudof`\n\n\n\n## Docker\n\nThe library is also published as a Docker image (`angelip2303/rudof:latest`).\n\n\n\n## Usage\n\n\n\n### Some examples\n\nThe folder `examples` contains several example files with ShEx schemas and RDF data.\n\n\n\n### Validate a simple RDF file with a ShEx schema using a ShapeMap\n\n\n\n```\nrudof validate examples/user.ttl --schema examples/user.shex --shapemap examples/user.sm\n```\n\nWe maintain a Wiki page with some common [Usage scenarios and How-to guides](https://github.com/rudof-project/rudof/wiki/Howto-guides).\n\n\n\n### Debugging information\n\nIt is possible to change the debug level information with:\n\n\n\n```\nexport RUST_LOG=info\n```\n\nwhere `value` can be `info`, `debug` or `trace` to show more information. It is also possible to have more control about the logs using more complex filters. For example, to show `trace` for the elements in crate `shacl_validation`, and supress any logs for crates `hyper` and `reqwest` (which can be quite verbose with traces), you can use:\n\n\n\n```\nexport RUST_LOG=info,shacl_validation=trace,hyper=off,reqwest=off\n```\n\nIn case you use nu_shell, you can use:\n\n\n\n```\n$env.RUST_LOG = 'info,shacl_validation=trace,hyper=off,reqwest=off'\n```\n\n\n\n## Command line usage\n\n\n\n```\nRDF and Knowledge Graphs processing tool\n\nUsage: rudof [OPTIONS] [COMMAND]\n\nCommands:\nmcp Export rudof as an MCP server\nshapemap Show information about ShEx ShapeMaps\nshex Show information about ShEx schemas\npgschema Show information about Property Graph Schemas\nvalidate Validate RDF data using ShEx or SHACL\nshex-validate Validate RDF using ShEx schemas\nshacl-validate Validate RDF data using SHACL shapes\ndata Show information about RDF data\nnode Show information about a node in an RDF Graph\nshacl Show information about SHACL shapes\ndctap Arguments for the `dctap` command\nconvert Arguments for the `convert` command\ncompare Compare two shapes (which can be in different formats)\nrdf-config Show information about rdf config\nservice Show information about SPARQL service\nquery Run SPARQL queries\ngenerate Generate synthetic RDF data from ShEx or SHACL schemas\nmaterialize Materialize an RDF graph from a ShEx schema and Map semantic-action state\npgschema-validate Validate Property Graph data using PGSchema\ncompletion Generates a shell completion script for the specified shell\nconfig Dump the effective configuration rudof is using as TOML\nhelp Print this message or the help of the given subcommand(s)\n\nOptions:\n-d, --debug... Increase logging verbosity\n-h, --help Print help\n-V, --version Print version\n```\n\nYou can see the [manual](https://rudof-project.github.io/rudof/)\n\n\n\n## Main modules\n\nThe repo is divided in the following modules:\n\n- [rudof_iri](https://github.com/rudof-project/rudof/tree/master/rudof_iri) defines simple IRIs.\n- [srdf](https://github.com/rudof-project/rudof/tree/master/srdf) simple RDF model which will be used for validation.\n- [prefixmap](https://github.com/rudof-project/rudof/tree/master/prefixmap) Prefix maps implementation.\n- [shapemap](https://github.com/rudof-project/rudof/tree/master/shapemap) ShapeMap implementation.\n- [shex_ast](https://github.com/rudof-project/rudof/tree/master/shex_ast) defines the ShEx Abstract syntax\n- [shex_compact](https://github.com/rudof-project/rudof/tree/master/shex_compact) contains the code required to handle ShEx compact syntax.\n- [shex_validation](https://github.com/rudof-project/rudof/tree/master/shex_validation) contains the code required to validate RDF using ShEx.\n- [shex_testsuite](https://github.com/rudof-project/rudof/tree/master/shex_testsuite) contains the code required to run the ShEx testsuite.\n- [shacl_ast](https://github.com/rudof-project/rudof/tree/master/shacl_ast) defines the SHACL core Abstract syntax.\n- [shacl_validation](https://github.com/rudof-project/rudof/tree/master/shacl_validation) contains the code required to validate RDF using SHACL.\n- [dctap](https://github.com/rudof-project/rudof/tree/master/dctap) contains the code required to do handle DCTAP files.\n- [shapes_converter](https://github.com/rudof-project/rudof/tree/master/shapes_converter) contains the code required to do conversion between different shapes formalisms.\n\n## Publishing the crates\n\n\n\n```\ncargo workspaces publish\n```\n\n\n\n## Worskpaces\n\nThe project is using cargo workspaces wihch can be installed with:\n\n\n\n```\ncargo install cargo-workspaces\n```\n\n\n\n## Unit-testing\n\nIn order to test all the sub-projects\n\n\n\n```\ncargo test --all\n```\n\nTesting one specific subproject:\n\n\n\n```\ncargo test -p shex_validation\n```\n\n\n\n## Using the ShEx test-suite\n\nThe ShEx testsuite is included in a git submodule. In order to obtain it, it is necessary to do:\n\n\n\n```\ngit submodule update --init --recursive\ncargo run -p shex_testsuite\n```\n\n\n\n```\nUsage: shex_testsuite [OPTIONS]\n\nOptions:\n-m, --manifest\nName of Manifest file [default: shex_testsuite/shexTest/validation/manifest.jsonld]\n-c, --config\n[default: shex_testsuite/config.toml]\n-x, --run_mode\n[default: collect-errors] [possible values: collect-errors, fail-first-error]\n-f, --manifest_mode\n[possible values: schemas, validation, negative-syntax, negative-structure]\n-p, --print_result_mode\n[default: basic] [possible values: basic, failed, passed, not-implemented, all]\n-e, --entry\n\n-t, --trait\n\n-h, --help\nPrint help\n-V, --version\nPrint version\n```\n\n\n\n### Validation conformance tests for ShEx\n\n\n\n```\ncargo run -p shex_testsuite -- -m shex_testsuite/shexTest/validation/manifest.jsonld\n```\n\nIf you want to see the failed tests, you can run:\n\n\n\n```\ncargo run -p shex_testsuite -- -m shex_testsuite/shexTest/validation/manifest.jsonld -p failed\n```\n\n\n\n### Schemas tests\n\n\n\n```\ncargo run -p shex_testsuite -- -m shex_testsuite/shexTest/schemas/manifest.jsonld -f schemas -p failed\n```\n\n\n\n## Benchmarks\n\nPerformance benchmarks live in [`benchmarks/`](https://github.com/rudof-project/rudof/blob/master/benchmarks). They are not published to crates.io and are not executed in CI. Run them locally with Criterion.\n\n\n\n## License\n\nLicensed under either of\n\n- Apache License, Version 2.0 ([LICENSE-APACHE](https://github.com/rudof-project/rudof/blob/master/LICENSE-APACHE) or [http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0))\n- MIT license ([LICENSE-MIT](https://github.com/rudof-project/rudof/blob/master/LICENSE-MIT) or [http://opensource.org/licenses/MIT](http://opensource.org/licenses/MIT)) at your option.\n\n\n\n## Contributors\n\n\n\n### Contribution\n\nUnless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.\n\n\n\n### Local development\n\n[Precommit](https://pre-commit.com/) has been configured to run formatters and linters before each commit. It is recommended to use it to ensure that the code is formatted and linted correctly before pushing it to the repository.\n\nIn order to install it, you can run:\n\n\n\n```\n# Installs pre-commit it self\npip install pre-commit\n\n# Installs the pre-commit hooks defined in .pre-commit-config.yaml\npre-commit install\n```\n\n\n\n#### Nix\n\nIf you have [Nix](https://nixos.org/download) with flakes enabled, the repository provides a ready-to-use development shell that includes all required dependencies.\n\nSimply run the following command from the root of the repository:\n\n\n\n```\nnix develop\n```\n\nThis drops you into a shell with everything pre-configured. No manual installation of Rust, `cargo`, or any other tool is needed.\n\nYou can also run a one-off command without entering the shell interactively:\n\n\n\n```\nnix develop --command cargo test\n```\n\n\n\n> If you use [direnv](https://direnv.net/), add a `.envrc` file with `use flake` to activate the development environment automatically whenever you `cd` into the repository: ``` echo \"use flake\" > .envrc && direnv allow ```"},"author":{"name":"rudof-project","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/2572e38168c2a7bc/avatar-6a9577cd4f7f.svg","bio":"RDF data shapes implementation in Rust . Contribute to rudof-project/rudof development by creating an account on GitHub."},"metadata":{"timestamp":"2026-08-20T08:34:28.254Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/BUIoNWgCM9/media/0.png","type":"image"}],"aiComments":[]},{"id":"web-hrcnay","shareId":"MWI8htgnaL","platform":"web","url":"https://github.com/Arquisoft/Arquisoft.github.io","title":"Arquisoft/Arquisoft.github.io: Web page of the course","shareMode":"full","createdAt":1787214880266,"postedAt":"2026-08-20T08:34:30.453Z","publishedDate":"2026-08-20T08:34:30.453Z","archivedAt":"2026-08-20T08:34:33.321Z","archivedDate":"2026-08-20T08:34:33.321Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/MWI8htgnaL","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/GVfIxT8MUD/media/0.png","contentType":"article","content":{"text":"[Branches](https://github.com/Arquisoft/Arquisoft.github.io/branches) [Tags](https://github.com/Arquisoft/Arquisoft.github.io/tags)\n\n[1,465 Commits](https://github.com/Arquisoft/Arquisoft.github.io/commits/master/)\n\n| Name | Name | Last commit message | Last commit date |\n| --- | --- | --- | --- |\n| course2021 | course2021 |\n| course2122 | course2122 |\n| course2223 | course2223 |\n| course2324 | course2324 |\n| course2425/seminars | course2425/seminars |\n| course2526/seminars | course2526/seminars |\n| mindMapFiles | mindMapFiles |\n| slides | slides |\n| styles | styles |\n| .gitignore | .gitignore |\n| ISO25010.html | ISO25010.html |\n| README.md | README.md |\n| course1819.html | course1819.html |\n| course1920.html | course1920.html |\n| course2021.html | course2021.html |\n| course2122.html | course2122.html |\n| course2223.html | course2223.html |\n| course2324.html | course2324.html |\n| course2425.html | course2425.html |\n| course2526.html | course2526.html |\n| debug.log | debug.log |\n| index.html | index.html |\n| View all files |\n\n## Arquisoft.github.io\n\nWeb page of the [Software architecture course](http://arquisoft.github.io/)."},"author":{"name":"Arquisoft","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/e96a0922db6dc676/avatar-6a9577cd4f7f.svg","bio":"Web page of the course. Contribute to Arquisoft/Arquisoft.github.io development by creating an account on GitHub."},"metadata":{"timestamp":"2026-08-20T08:34:30.453Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/GVfIxT8MUD/media/0.png","type":"image","width":1200,"height":600}],"aiComments":[]},{"id":"web-ycuczj","shareId":"bjxPiuWerE","platform":"web","url":"http://labra.weso.es/","title":"Labra's home page","shareMode":"full","createdAt":1787214861383,"postedAt":"2019-11-14T18:37:00.000Z","publishedDate":"2019-11-14T18:37:00.000Z","archivedAt":"2026-08-20T08:34:17.562Z","archivedDate":"2026-08-20T08:34:17.562Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/bjxPiuWerE","contentType":"article","content":{"text":"## Biography\n\nFull Professor (Catedrático), [University of Oviedo](http://uniovi.es/), Spain. Founder and main researcher of [WESO (Web Semantics Oviedo)](http://www.weso.es/) research group, which collaborates with different companies around the world applying semantic web technologies to solve practical problems.\n\nMember of [Data Shapes](https://www.w3.org/2014/data-shapes/wiki/Main_Page) W3C Working Groups and of [Shape Expressions](https://www.w3.org/community/shex/), [SHACL](https://www.w3.org/community/shacl/), [RAX (RDF and XML interoperability)](https://www.w3.org/community/rax/) and [Best practices of Multilingual Linked Open Data](https://www.w3.org/community/bpmlod/) W3C community groups.\n\nPreviously, coordinator of the [Master in Web Engineering](http://miw.uniovi.es/) - University of Oviedo (2016-2017), and Dean of the [School of Computer Science Engineering](http://ingenieriainformatica.uniovi.es/) - [University of Oviedo](http://uniovi.es/) (2004-2012).\n\n### Interests\n\n- Semantic Web\n- Programming languages\n- Software Architecture\n\n### Education\n\n- PhD in Computer Science Engineering, 2001 University of Oviedo\n\n## Publications\n\nSee [All publications](http://labra.weso.es/publication/). Other lists: [DBLP](https://dblp.uni-trier.de/pers/hd/g/Gayo:Jos=eacute=_Emilio_Labra), [Google scholar](https://scholar.google.com/citations?user=TGfRNZkAAAAJ),, [Research gate](https://www.researchgate.net/profile/Jose_Labra_Gayo)\n\n- [TogoMCP: natural language querying of life-science knowledge graphs via schema-guided LLMs and the Model Context Protocol](http://labra.weso.es/publication/2026_togomcp_database/), [Akira R Kinjo](https://labra.weso.es/authors/akira-r-kinjo), [Yasunori Yamamoto](https://labra.weso.es/authors/yasunori-yamamoto), [Samuel Bustamante-Larriet](https://labra.weso.es/authors/samuel-bustamante-larriet), [Jose-Emilio Labra-Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Takatomo Fujisawa](https://labra.weso.es/authors/takatomo-fujisawa), Database - 2026\n- [rudof-MCP: A Model Context Protocol Server for Semantic Web Operations](http://labra.weso.es/publication/2026_mcp_rudof/), [Samuel Bustamante Larriet](https://labra.weso.es/authors/samuel-bustamante-larriet), [Diego Martín Fernández](https://labra.weso.es/authors/diego-mart%C3%ADn-fern%C3%A1ndez), [Álvaro García Fernández](https://labra.weso.es/authors/%C3%A1lvaro-garc%C3%ADa-fern%C3%A1ndez), [Daniel Fernández Álvarez](https://labra.weso.es/authors/daniel-fern%C3%A1ndez-%C3%A1lvarez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), *Extended Semantic Web Conference*, ESWC26, Posters and Demos - 2026\n- [Common Foundations for Recursive Shape Languages](http://labra.weso.es/publication/2026_foundations_recursive_shapes_languages/), [Shqiponja Ahmetaj](https://labra.weso.es/authors/shqiponja-ahmetaj), [Iovka Boneva](https://labra.weso.es/authors/iovka-boneva), [Jan Hidders](https://labra.weso.es/authors/jan-hidders), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Wim Martens](https://labra.weso.es/authors/wim-martens), [Fabio Mogavero](https://labra.weso.es/authors/fabio-mogavero), [Filip Murlak](https://labra.weso.es/authors/filip-murlak), [Cem Okulmus](https://labra.weso.es/authors/cem-okulmus), [Ognjen Savković](https://labra.weso.es/authors/ognjen-savkovi%C4%87), [Mantas Šimkus](https://labra.weso.es/authors/mantas-%C5%A1imkus), [Dominik Tomaszuk](https://labra.weso.es/authors/dominik-tomaszuk), 23rd International Conference on Principles of Knowledge Representation and Reasoning - 2026\n- [sheXer: A robust and versatile library for automatic extraction of RDF shapes](http://labra.weso.es/publication/2026_shexer_software_x/), [Daniel Fernández Álvarez](https://labra.weso.es/authors/daniel-fern%C3%A1ndez-%C3%A1lvarez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Software X - 2026\n- [MCP server tools with RDF shapes](http://labra.weso.es/publication/2025_mcp_server_tools_rdf/), [Jose Emilio Labra-Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Yasunori Yamamoto](https://labra.weso.es/authors/yasunori-yamamoto), [Akira R. Kinjo](https://labra.weso.es/authors/akira-r.-kinjo), [Andra Waagmeester](https://labra.weso.es/authors/andra-waagmeester), [Javier Acosta](https://labra.weso.es/authors/javier-acosta), [Shuichi Kawashima](https://labra.weso.es/authors/shuichi-kawashima), [Yoko Okabeppu](https://labra.weso.es/authors/yoko-okabeppu), [Julia Koblitz](https://labra.weso.es/authors/julia-koblitz), [Samuel Bustamante Larriet](https://labra.weso.es/authors/samuel-bustamante-larriet), BioHackrXiv preprint - 2025\n- [On Property Constraints in PG-Schema](http://labra.weso.es/publication/2025_property_constraints_pg_schema/), [Dominik Tomaszuk](https://labra.weso.es/authors/dominik-tomaszuk), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), *Knowledge Capture and Knowledge Representation Conference*, KCAP25 - 2025\n- [PG-Schema with Property Constraints validator in Rudof](http://labra.weso.es/publication/2026_rudof_pgschema_pc/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Dominik Tomaszuk](https://labra.weso.es/authors/dominik-tomaszuk), [Diego Martín Fernández](https://labra.weso.es/authors/diego-mart%C3%ADn-fern%C3%A1ndez), [Samuel Bustamante Larriet](https://labra.weso.es/authors/samuel-bustamante-larriet), [Álvaro García Fernández](https://labra.weso.es/authors/%C3%A1lvaro-garc%C3%ADa-fern%C3%A1ndez), [Daniel Fernández Álvarez](https://labra.weso.es/authors/daniel-fern%C3%A1ndez-%C3%A1lvarez), *Knowledge Capture and Knowledge Representation Conference*, KCAP25, Posters and Demos - 2025\n- [xpSHACL: Explainable SHACL Validation using Retrieval-Augmented Generation and Large Language Models](http://labra.weso.es/publication/2025_xp_shacl/), [Gustavo Correa Publio](https://labra.weso.es/authors/gustavo-correa-publio), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), *LLM+Graph: 2nd International Workshop on Data Management Opportunities in Bringing LLMs with Graph Data*, VLDB25, London, 2025 - 2025\n- [Shape Expressions with Inheritance](http://labra.weso.es/publication/2025_shex_inheritance/), [Iovka Boneva](https://labra.weso.es/authors/iovka-boneva), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Eric Prud'hommeaux](https://labra.weso.es/authors/eric-prudhommeaux), [Katherine Thornton](https://labra.weso.es/authors/katherine-thornton), [Andra Waagmeester](https://labra.weso.es/authors/andra-waagmeester), *Extended Semantic Web Conference*, Portoroz, Slovenia, 2025 - 2025\n- [Common Foundations for SHACL, ShEx, and PG-Schema](http://labra.weso.es/publication/2025_common_foundations_shacl_shex_pgschema/), [Shqiponja Ahmetaj](https://labra.weso.es/authors/shqiponja-ahmetaj), [Iovka Boneva](https://labra.weso.es/authors/iovka-boneva), [Jan Hidders](https://labra.weso.es/authors/jan-hidders), [Katja Hose](https://labra.weso.es/authors/katja-hose), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Wim Martens](https://labra.weso.es/authors/wim-martens), [Fabio Mogavero](https://labra.weso.es/authors/fabio-mogavero), [Filip Murlak](https://labra.weso.es/authors/filip-murlak), [Cem Okulmus](https://labra.weso.es/authors/cem-okulmus), [Axel Polleres](https://labra.weso.es/authors/axel-polleres), [Ognjen Savković](https://labra.weso.es/authors/ognjen-savkovi%C4%87), [Mantas Šimkus](https://labra.weso.es/authors/mantas-%C5%A1imkus), [Dominik Tomaszuk](https://labra.weso.es/authors/dominik-tomaszuk), International World Wide Web Conference, Sidney, Australia, 2025 - 2025\n- [Componentizing autonomous underwater vehicles by physical-running algorithms](http://labra.weso.es/publication/2024_underwater_vehicles_peerj/), [Claudio Navarro](https://labra.weso.es/authors/claudio-navarro), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Francisco A. Escobar Jara](https://labra.weso.es/authors/francisco-a.-escobar-jara), [Carlos Cares](https://labra.weso.es/authors/carlos-cares), PeerJ Computer Science - 2024\n- [Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies](http://labra.weso.es/publication/2024_towardsefficientlegislativeprocesses/), [Francisco Cifuentes Silva](https://labra.weso.es/authors/francisco-cifuentes-silva), [Hernán Astudillo](https://labra.weso.es/authors/hern%C3%A1n-astudillo), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Felipe Rivera Polo](https://labra.weso.es/authors/felipe-rivera-polo), SN Computer Science - 2024\n- [rudof: A Rust Library for handling RDF data models and Shapes](http://labra.weso.es/publication/2024_rudof_demo/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Ángel Iglesias Préstamo](https://labra.weso.es/authors/%C3%A1ngel-iglesias-pr%C3%A9stamo), [Diego Martín Fernández](https://labra.weso.es/authors/diego-mart%C3%ADn-fern%C3%A1ndez), [Marc-Antoine Arnaud](https://labra.weso.es/authors/marc-antoine-arnaud), *International Semantic Web Conference*, ISWC24, Posters and Demos - 2024\n- [Extending Shape Expressions for different types of knowledge graphs](http://labra.weso.es/publication/2024_shex_extensions/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In 1st Workshop on *Data Quality meets Machine Learning and Knowledge Graphs*, DQMLKG, part of *Extended Semantic Web Conference 2024*, ESWC24. - 2024\n- [Introduction to ShEx and ShEx and SHACL Compared](http://labra.weso.es/publication/2024_dagsthulseminar/), [Shqiponja Ahmetaj](https://labra.weso.es/authors/shqiponja-ahmetaj), [Slawomir Staworko](https://labra.weso.es/authors/slawomir-staworko), [Jan Van den Bussche](https://labra.weso.es/authors/jan-van-den-bussche), [Maxime Jakubowsk](https://labra.weso.es/authors/maxime-jakubowsk), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In *Dagstuhl Reports, Volume 14, Issue 3*, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) - 2024\n- [Extracting shapes from large RDF data collections](http://labra.weso.es/publication/2024_extracting_shapes_consolidator-copy/), [Daniel Fernández Álvarez](https://labra.weso.es/authors/daniel-fern%C3%A1ndez-%C3%A1lvarez), [Yasunori Yamamoto](https://labra.weso.es/authors/yasunori-yamamoto), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Andra Waagmeester](https://labra.weso.es/authors/andra-waagmeester), In *Semantic Web Applications and Tools for Health Care and Life Sciences*, SWAT4HCLS. - 2024\n- [Extracting shapes from large RDF data collections](http://labra.weso.es/publication/2024_extracting_shapes_consolidator/), [Daniel Fernández Álvarez](https://labra.weso.es/authors/daniel-fern%C3%A1ndez-%C3%A1lvarez), [Yasunori Yamamoto](https://labra.weso.es/authors/yasunori-yamamoto), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Andra Waagmeester](https://labra.weso.es/authors/andra-waagmeester), In *Semantic Web Applications and Tools for Health Care and Life Sciences*, SWAT4HCLS. - 2024\n- [Using Polarization and Alignment to Identify Quick-Approval Law Propositions: An Open Linked Data Application](http://labra.weso.es/publication/2023_polarization_icai/), [Francisco Cifuentes Silva](https://labra.weso.es/authors/francisco-cifuentes-silva), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Hernán Astudillo](https://labra.weso.es/authors/hern%C3%A1n-astudillo), [Felipe Rivera Polo](https://labra.weso.es/authors/felipe-rivera-polo), In *International Conference on Applied Informatics*, ICAI 2023. - 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2015\n- [Validating and Describing Linked Data Portals using RDF Shape Expressions](http://labra.weso.es/publication/2015_validatinglinkeddataportals/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Eric Prud'hommeaux](https://labra.weso.es/authors/eric-prudhommeaux), [Harold Solbrig](https://labra.weso.es/authors/harold-solbrig), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), In *1st Workshop on Linked Data Quality*, Brussels, Belgique - 2015\n- [Multilingual linked data patterns](http://labra.weso.es/publication/2015_mlodpatterns/) , [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Dimitris Kontokostas](https://labra.weso.es/authors/dimitris-kontokostas), [Sören Auer](https://labra.weso.es/authors/s%C3%B6ren-auer), Semantic Web Journal - 2015\n- [Representing Statistical Indexes as Linked Data Including Metadata about Their Computation Process](http://labra.weso.es/publication/2014_representingstatisticalindexes/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Hania Farham](https://labra.weso.es/authors/hania-farham), [Juan Castro Fernández](https://labra.weso.es/authors/juan-castro-fern%C3%A1ndez), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), Metadata and Semantics Research - 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2014\n- [Shape Expressions: An RDF validation and transformation language](http://labra.weso.es/publication/2014_shex/), [Eric Prud'hommeaux](https://labra.weso.es/authors/eric-prudhommeaux), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Harold Solbrig](https://labra.weso.es/authors/harold-solbrig), In [10th International Conference on Semantic Systems](http://2014.semantics.cc/www.semantics.cc/index.html), Leipzig, Germany - 2014\n- [New trends on e-Procurement applying semantic technologies](http://labra.weso.es/publication/2014_newtrendseprocurement/), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), Computers in Industry - 2014\n- [New trends on e-Procurement applying semantic technologies: current status and future challenges](http://labra.weso.es/publication/2014_newtrendseprocurementstatus/), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), Computers in Industry - 2014\n- [ACOTA: A multilingual and Semi-automatic collaborative tagging system](http://labra.weso.es/publication/2014_acota/), [César Luis Alvargonzález](https://labra.weso.es/authors/c%C3%A9sar-luis-alvargonz%C3%A1lez), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), Journal of Web Engineering - 2014\n- [Inductive Representations of RDF Graphs](http://labra.weso.es/publication/2013_inductivetriplegraphs/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Johan Jeuring](https://labra.weso.es/authors/johan-jeuring), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), Science of Computer Programming - 2013\n- [Towards a Stepwise Method for Unifying and Reconciling Corporate Names in Public Contracts Metadata: The CORFU Technique](http://labra.weso.es/publication/2013_corfutechnique/), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), [Patricia Ordoñez de Pablos](https://labra.weso.es/authors/patricia-ordo%C3%B1ez-de-pablos), [Michail Vafopoulos](https://labra.weso.es/authors/michail-vafopoulos), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Metadata and Semantics Research - 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Carolina Medina Ramírez](https://labra.weso.es/authors/r.-carolina-medina-ram%C3%ADrez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [S. Masoud Sadjadi](https://labra.weso.es/authors/s.-masoud-sadjadi), IKE’13: The 12th International Conference on Information and Knowledge Engineering, Las Vegas, USA - 2013\n- [Cases on Open-Linked Data and Semantic Web Applications](http://labra.weso.es/publication/2013_caseslinkedopendata/), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), [Miltiadis D. Lytras](https://labra.weso.es/authors/miltiadis-d.-lytras), [Robert D. Tennyson](https://labra.weso.es/authors/robert-d.-tennyson), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), IGI Global, ISBN: 978-1466628274 - 2013\n- [E-Procurement Management for Successful Electronic Government Systems](http://labra.weso.es/publication/2013_eprocurementmanagement/), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Robert D. Tennyson](https://labra.weso.es/authors/robert-d.-tennyson), IGI Global, ISBN: 9781466621190 - 2013\n- [Applying MapReduce to Spreading Activation Algorithm on Large RDF Graphs](http://labra.weso.es/publication/2013_mapreducespreadingactivation/), [Jorge González Lorenzo](https://labra.weso.es/authors/jorge-gonz%C3%A1lez-lorenzo), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Jose María Alvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-alvarez-rodr%C3%ADguez), Communications in Computer and Information Science, vol 278, pp. 601-611 - 2013\n- [Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach](http://labra.weso.es/publication/2013_adonisbookchapter/), [Alejandro Rodríguez González](https://labra.weso.es/authors/alejandro-rodr%C3%ADguez-gonz%C3%A1lez), [Ángel García Crespo](https://labra.weso.es/authors/%C3%A1ngel-garc%C3%ADa-crespo), [Ricardo Colomo Palacios](https://labra.weso.es/authors/ricardo-colomo-palacios), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Juan Miguel Gómez Berbis](https://labra.weso.es/authors/juan-miguel-g%C3%B3mez-berbis), [Giner Alor Hernández](https://labra.weso.es/authors/giner-alor-hern%C3%A1ndez), Engineering Effective Decision Support Technologies: New Models and Applications - 2013\n- [ONTOSPREAD: A Framework for Supporting the Activation of Concepts in Graph-Based Structures through the Spreading Activation Technique](http://labra.weso.es/publication/2013_ontospreadframework/), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), Communications in Computer and Information Science, vol 278, pp. 454-459 - 2013\n- [Towards a Practical Solution for Data Grounding in a Semantic Web Services Environment](http://labra.weso.es/publication/2012_datagrounding/), [Miguel García Rodríguez](https://labra.weso.es/authors/miguel-garc%C3%ADa-rodr%C3%ADguez), [Jose María Alvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-alvarez-rodr%C3%ADguez), [Diego Berrueta Muñoz](https://labra.weso.es/authors/diego-berrueta-mu%C3%B1oz), [Luis Polo Paredes](https://labra.weso.es/authors/luis-polo-paredes), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordoñez De Pablos](https://labra.weso.es/authors/patricia-ordo%C3%B1ez-de-pablos), Journal of Universal Computer Science - 2012\n- [Web Semántica: comprendiendo el cambio hacia la Web 3.0](http://labra.weso.es/publication/2012_websemantica/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Pocket Innova - 2012\n- [The effects of ERP implementations on the profitability of big firms: the case of Spain](http://labra.weso.es/publication/2012_effectserp/), [Javier de Andrés](https://labra.weso.es/authors/javier-de-andr%C3%A9s), [Pedro Lorca](https://labra.weso.es/authors/pedro-lorca), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), International Journal on Technology Management, vol. 59, number 1 / 2, 2012 - 2012\n- [Towards a Pan-European E-Procurement platform to Aggregate, Publish and Search Public Procurement Notices powered by Linked Open Data: The MOLDEAS Approach](http://labra.weso.es/publication/2012_paneuropeaneprocurement/), [Jose María Álvarez Rodríguez](https://labra.weso.es/authors/jose-mar%C3%ADa-%C3%A1lvarez-rodr%C3%ADguez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Francisco Cifuentes](https://labra.weso.es/authors/francisco-cifuentes), [Giner Hérnandez](https://labra.weso.es/authors/giner-h%C3%A9rnandez), [C. 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Guzmán Luna](https://labra.weso.es/authors/j.-a.-guzm%C3%A1n-luna), International Journal of Software Engineering and Knowledge Engineering (IJSEKE ) Spceial Issue on Consuming and Producing Linked Data on Real World Applications, vol 22, num. 3, 2012 - 2012\n- [Towards an architecture and adoption process for Linked Data technologies in Open Government contexts](http://labra.weso.es/publication/2011_linkeddatalibrarycongress/), [Francisco Cifuentes Silva](https://labra.weso.es/authors/francisco-cifuentes-silva), [Christian Sifaqui](https://labra.weso.es/authors/christian-sifaqui), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In *I-Semantics* - 2011\n- [SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis](http://labra.weso.es/publication/2011_sedelo/), [Alejandro Rodríguez García](https://labra.weso.es/authors/alejandro-rodr%C3%ADguez-garc%C3%ADa), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Ricardo Colomo Palacio](https://labra.weso.es/authors/ricardo-colomo-palacio), [Miguel A. Mayer](https://labra.weso.es/authors/miguel-a.-mayer), [Juan Manual Gómez Berbis](https://labra.weso.es/authors/juan-manual-g%C3%B3mez-berbis), [Angel García Crespo](https://labra.weso.es/authors/angel-garc%C3%ADa-crespo), Journal of Medical Systems, vol. 36, number 4, pp: 2471-2481 - 2011\n- [Towards the evaluation of usability in educative websites](http://labra.weso.es/publication/2010_towardsevaluationusability/), [María Elena Alba Obeso](https://labra.weso.es/authors/mar%C3%ADa-elena-alba-obeso), [Ana Belén Martínez Prieto](https://labra.weso.es/authors/ana-bel%C3%A9n-mart%C3%ADnez-prieto), [María del Carmen Suárez Torrente](https://labra.weso.es/authors/mar%C3%ADa-del-carmen-su%C3%A1rez-torrente), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), [Hernán Sagastegui Chigne](https://labra.weso.es/authors/hern%C3%A1n-sagastegui-chigne), International Journal on Technology Enhanced Learning, vol. 2 - 2010\n- [WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systems](http://labra.weso.es/publication/2009_wesonet/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), Computers in Human Behaviour, vol 26, pp: 205-209, 2010 - 2009\n- [A proposal for a Semantic Intelligent Document Repository Architecture](http://labra.weso.es/publication/2009_sidra-cerma/), [Alejandro Rodríguez](https://labra.weso.es/authors/alejandro-rodr%C3%ADguez), [Ricardo Colomo](https://labra.weso.es/authors/ricardo-colomo), [Juan Miguel Gómez](https://labra.weso.es/authors/juan-miguel-g%C3%B3mez), [Giner Alor-Hernandez](https://labra.weso.es/authors/giner-alor-hernandez), [Ruben Posada-Gomez](https://labra.weso.es/authors/ruben-posada-gomez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Krishnamurthy Vidyasankar](https://labra.weso.es/authors/krishnamurthy-vidyasankar), In *Electronics, Robotics and Automotive Mechanics Conference* (CERMA 2009) - 2009\n- [Ontologies in Checking for Inconsistency of Requirements Specification](http://labra.weso.es/publication/2009_ontologiesrequirementspecifications/), [Petr Kroha](https://labra.weso.es/authors/petr-kroha), [R. Janetzko](https://labra.weso.es/authors/r.-janetzko), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In *Third International Conference on Advances in Semantic Processing* SEMAPRO, 09 - 2009\n- [Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach](http://labra.weso.es/publication/2011_adonis/), [Alejandro Rodríguez González](https://labra.weso.es/authors/alejandro-rodr%C3%ADguez-gonz%C3%A1lez), [Ángel García Crespo](https://labra.weso.es/authors/%C3%A1ngel-garc%C3%ADa-crespo), [Ricardo Colomo Palacios](https://labra.weso.es/authors/ricardo-colomo-palacios), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Juan Miguel Gómez Berbis](https://labra.weso.es/authors/juan-miguel-g%C3%B3mez-berbis), [Giner Alor Hernández](https://labra.weso.es/authors/giner-alor-hern%C3%A1ndez), International Journal of Decision Support System Technology - 2009\n- [Doing Business by selling free services](http://labra.weso.es/publication/2009_doingbusinesssellingfreeservices/), [Jose Luis Marín de la Iglesia](https://labra.weso.es/authors/jose-luis-mar%C3%ADn-de-la-iglesia), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Web 2.0 Business Models - 2009\n- [SOAF: Semantic Indexing System Based on Collaborative Tagging](http://labra.weso.es/publication/2008_semanticindexingcollaborativetagging/), [Ana Cernea Doina](https://labra.weso.es/authors/ana-cernea-doina), [Ester del Moral](https://labra.weso.es/authors/ester-del-moral), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In [Interdisciplinary Journal of E-Learning and Learning Objects](https://www.learntechlib.org/j/IJELLO), vol 4(1) - 2008\n- [Analysis of XBRL documents representing financial statements using Semantic Web Technologies](http://labra.weso.es/publication/2007_analysisxbrl/), [Sheila Méndez Núñez](https://labra.weso.es/authors/sheila-m%C3%A9ndez-n%C3%BA%C3%B1ez), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Javier de Andrés Suárez](https://labra.weso.es/authors/javier-de-andr%C3%A9s-su%C3%A1rez), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), In [2nd International Conference on Metadata and Semantic Web Research](http://www.mtsr.ionio.gr/) - 2007\n- [Combining Collaborative Tagging and Ontologies in Image Retrieval Systems](http://labra.weso.es/publication/2007_combiningcollaborativetaggingimageretrieval/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Patricia Ordóñez de Pablos](https://labra.weso.es/authors/patricia-ord%C3%B3%C3%B1ez-de-pablos), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), In [2nd International Conference on Metadata and Semantic Web Research](http://www.mtsr.ionio.gr/) - 2007\n- [Mailing lists meet the semantic web](http://labra.weso.es/publication/2007_mailinglistssemanticweb/), [Sergio Fernández](https://labra.weso.es/authors/sergio-fern%C3%A1ndez), [Diego Berrueta](https://labra.weso.es/authors/diego-berrueta), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In *Workshop on Social Aspects of the Semantic Web (SAW 2007)*, Poznan, Poland - 2007\n- [Utilización de herramientas colaborativas de desarrollo de software libre en un modelo de aprendizaje basado en proyectos](http://labra.weso.es/publication/2006_herramientascolaborativas/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Daniel Fernández Lanvin](https://labra.weso.es/authors/daniel-fern%C3%A1ndez-lanvin), [Jesús Calvo Salvador](https://labra.weso.es/authors/jes%C3%BAs-calvo-salvador), [Agustín Cernuda del Río](https://labra.weso.es/authors/agust%C3%ADn-cernuda-del-r%C3%ADo), XII Jornadas de Enseñanza Universitaria de Informática (Jenui 2006), Bilbao, Spain - 2006\n- [A monadic program slicer](http://labra.weso.es/publication/2006_monadicprogramslicer/), [Yingzhou Zhang](https://labra.weso.es/authors/yingzhou-zhang), [[Jose Emilio Labra Gayo](http://di.uniovi.es/~labra)](https://labra.weso.es/authors/jose-emilio-labra-gayohttp//di.uniovi.es/~labra), [Agustín Cernuda del Río](https://labra.weso.es/authors/agust%C3%ADn-cernuda-del-r%C3%ADo), ACM SIGPLAN Notices, 41 (5), pp. 30-38 - 2006\n- [A Formal Method for program Slicing](http://labra.weso.es/publication/2005_formalmethodprogramslicing/), [Yingzhou Zhang](https://labra.weso.es/authors/yingzhou-zhang), [Baowen Xu](https://labra.weso.es/authors/baowen-xu), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), In [Australian Software Engineering Conference, ASWEC 2005](http://aswec2005.itee.uq.edu.au/home.php) - 2005\n- [Representaciones gráficas y Mundos Virtuales infinitos en las Prácticas de Programación Lógica y Funcional](http://labra.weso.es/publication/2003_graphicalrepresentationslogicfunctionalprogrammingjenui/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [IX Jornadas de Enseñanza Universitaria de Informática (Jenui 2003)](http://neptuno.uca.es/jenui2003/), Cádiz, Spain - 2004\n- [¿Hay Lógica en la situación actual de las titulaciones informáticas?](http://labra.weso.es/publication/2004_logicaspainjenui/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [X Jornadas de Enseñanza Universitaria de Informática (Jenui 2004)](http://www.eps.ua.es/jenui/), Almería, Spain - 2004\n- [Web Engineering](http://labra.weso.es/publication/2003_webengineering/), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), [Bernardo Martín González Rodríguez](https://labra.weso.es/authors/bernardo-mart%C3%ADn-gonz%C3%A1lez-rodr%C3%ADguez), [Luis Joyanes Aguilar](https://labra.weso.es/authors/luis-joyanes-aguilar), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [María del Puerto Paule](https://labra.weso.es/authors/mar%C3%ADa-del-puerto-paule), Proceedings of the [International Conference on Web Engineering](http://www.icwe2003.org/), ICWE 2003, Oviedo, Spain, July 14-18, Lecture Notes in Computer Science, Vol. 2722 - 2003\n- [A Generic e-Learning Multiparadigm Programming Language System: IDEFIX Project](http://labra.weso.es/publication/2002_agenericelearningmultiparadigm_idefixsigcse/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Jose Manuel Morales Gil](https://labra.weso.es/authors/jose-manuel-morales-gil), [Alberto Manuel Fernández Álvarez](https://labra.weso.es/authors/alberto-manuel-fern%C3%A1ndez-%C3%A1lvarez), [Hernán Sagastegui Chigne](https://labra.weso.es/authors/hern%C3%A1n-sagastegui-chigne), In [ACM Technical Symposium on Computer Science Education](http://www.csis.gvsu.edu/sigcse2003/) (SIGCSE 2003), Reno, Nevada (USA) - 2003\n- [Specification of Logic Programming Languages from Reusable Semantic Building Blocks](http://labra.weso.es/publication/2003_specificationlogicprogramminglanguages/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [María C. Luengo Díaz](https://labra.weso.es/authors/mar%C3%ADa-c.-luengo-d%C3%ADaz), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), [Agustín Cernuda del Río](https://labra.weso.es/authors/agust%C3%ADn-cernuda-del-r%C3%ADo), Electronic Notes on Theoretical Computer Science, vol. 64 - 2003\n- [Plataforma de enseñanza de lenguajes de programación a través de Internet: Proyecto IDEFIX](http://labra.weso.es/publication/2002_plataformaensenianzalenguajesprogramacion_idefix/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Jose Manuel Morales Gil](https://labra.weso.es/authors/jose-manuel-morales-gil), [Roberto Turrado Camblor](https://labra.weso.es/authors/roberto-turrado-camblor), In *VIII Jornadas de Enseñanza Universitaria de Informática*, Cáceres, Spain - 2002\n- [Modular development of Language Processors from Reusable Semantic Specifications](http://labra.weso.es/publication/2001_phdthesis/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), PhD thesis (in Spanish) - 2001\n- [Programación Práctica en Prolog](http://labra.weso.es/publication/1998_progpracticaprolog/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Ed. Servitec, Num. 13, ISBN: 84-8416-612-0 - 1998\n- [Ejercicios de lógica informática](http://labra.weso.es/publication/1998_ejercicioslogica/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Ana I. Fernández](https://labra.weso.es/authors/ana-i.-fern%C3%A1ndez), Ed. Servitec, Num. 9, ISBN: 84-8416-357-1 - 1998\n- [Lógica proposicional para informática](http://labra.weso.es/publication/1998_propositionallogic/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Ana I. Fernández](https://labra.weso.es/authors/ana-i.-fern%C3%A1ndez), Ed. Servitec, Num. 12, ISBN: 84-8416-613-9 - 1998\n- [An Implementation of Modular Monadic Semantics using Folds and Monadic Folds](http://labra.weso.es/publication/1998_mms_usingfoldsmonadicfolds/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Workshop on Research Themes on Functional Programming, Third International Summer School on Advanced Functional Programming - 1998\n- [Harmony: a functional system for musical composition](http://labra.weso.es/publication/1997_harmony/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Juan Manuel Cueva Lovelle](https://labra.weso.es/authors/juan-manuel-cueva-lovelle), [Luis Ángel Oliveira](https://labra.weso.es/authors/luis-%C3%A1ngel-oliveira), In *II Conferencia Latinoamericana de Programación Funcional* - 1997\n- [Introducción al lenguaje Haskell](http://labra.weso.es/publication/1995_introhaskell/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Cuaderno didáctico, Número 87, Depto. Matemáticas - 1995\n- [Lógica de predicados de primer orden](http://labra.weso.es/publication/1993_logica/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), Cuaderno didáctico, Número 77, Depto. Matemáticas - 1993\n- [Simulación Entorno Operativo Unix](http://labra.weso.es/publication/1991_simulacionunix/), [Jose Emilio Labra Gayo](https://labra.weso.es/authors/jose-emilio-labra-gayo), [Oliverio González](https://labra.weso.es/authors/oliverio-gonz%C3%A1lez), [Miguel Riesco Albizu](https://labra.weso.es/authors/miguel-riesco-albizu), Cuaderno didáctico, Número 39, Depto. Matemáticas - 1991\n\n## Recent & Upcoming Talks\n\n[More Talks](https://labra.weso.es/talk/)\n\n- [Shaping Knowledge and Interoperable Graphs](http://labra.weso.es/talk/2026_shaping_knowledge_interoperable_graphs_poland/) Mon, 20 Apr, 2026, Invited talk at Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland\n- [Shaping Knowledge and Interoperable Graphs](http://labra.weso.es/talk/2025_iswc_2025/) Sun, 2 Nov, 2025, Tutorial at ISWC’25\n- [Shaping Linked Data and Knowledge Graphs](http://labra.weso.es/talk/2025_dcmi_2025/) Sat, 25 Oct, 2025, Tutorial at DCMI-25 Conference\n- [Workshop on Data Validation](http://labra.weso.es/talk/2025_raildataforum/) Thu, 12 Jun, 2025, Workshop at Rail Data Forum\n- [Shaping Knowledge Graphs](http://labra.weso.es/talk/2025_eswc_2025/) Wed, 11 Jun, 2025, Tutorial at ESWC’25\n- [Validating Knowledge Graphs](http://labra.weso.es/talk/2024_validating_kgs_laplata/) Wed, 11 Dec, 2024, Charla invitada en LIFIA, Universidad de La Plata\n- [Validating Knowledge Graphs](http://labra.weso.es/talk/2024_validating_kgs/) Tue, 19 Nov, 2024, Charla invitada en Máster Oficial de Ingeniería del Software e Inteligencia Artificial\n- [Shaping Knowledge Graphs](http://labra.weso.es/talk/2024_shaping_kgs_iswc/) Mon, 11 Nov, 2024, Tutorial at International Semantic Web Conference\n- [Let's dive into EntitySchemas](http://labra.weso.es/talk/2023_datamodelingdays/) Thu, 30 Nov, 2023, Wikidata Data Modeling Days\n- [Knowledge graphs: description, validation and subsetting](http://labra.weso.es/talk/2023_dresden/) Thu, 5 Oct, 2023, Invited talk at International Center for Computational Logic at Dresden\n\n## Other info\n\n## Supervised PhD students\n\n- [Francisco Cifuentes Silva](http://www.fcifuentes.cl/): Tecnologías Semánticas en el ámbito político-legislativo, October, 2025\n- [Daniel Fernández Álvarez](http://danifdez.weso.es/): Extracción de conocimiento semántico estructurado mediante minería de medios sociales, Sept, 2022\n- [Herminio García González](http://herminiogarcia.com/): Integración semántica de grandes fuentes de datos heterogéneas, 2021\n- [Jose Alberto Benítez Andrades](http://www.jabenitez.com/): Tecnologías Semánticas aplicadas al Análisis de Redes Sociales en el ámbito de la Salud, 2017\n- [Hernán Sagastegui Chigne](https://www.linkedin.com/in/hernan-sagastegui-chigne-48264025/): Desarrollo de Tecnologías Colaborativas para Entornos virtuales de apoyo al aprendizaje, 2015\n- [Jose María Álvarez Rodríguez](http://www.josemalvarez.es/): Métodos semánticos de reutilización de datos abiertos enlazados en las licitaciones públicas, 2012\n\n## PhD Students supervision (Current)\n\n- Hugo Lebredo Buján: Aplicación de grafos de conocimiento en la interoperabilidad de datos descentralizados de ámbito privado\n- Claudio Andrés Navarro Cruces: Semantic Technologies Applied to Cyber-Physical Systems Modelling\n- Ángel García Menéndez\n- Enrique José Rodríguez Martín\n- Jorge Álvarez Fidalgo\n- Pablo Valdés Fernández\n\n## PhD Thesis Committees\n\n- [Ammar Ammar](https://www.linkedin.com/in/ammar-ammar/): July, 2025, Maastricht, Enhancing the interoperability and reusability of nanosafety data: a foundation towards deeper insights and intelligent applications\n- [Ben De Meester](https://work.de-meester.org/), [IDLab, University of Ghent, Belgium](https://www.ugent.be/ea/idlab/en/), May 2020, title: Improving and Assessing Data Quality of Knowledge Graphs.\n- [Mikko Koho](https://www.linkedin.com/in/mikko-koho/), [School of Science in Aalto University](http://cs.aalto.fi/), April 2020, title: Representing, Using, and Maintaining Military Historical Linked Data on the Semantic Web.\n- [Andrii Berezovskyi](https://www.kth.se/profile/andriib?l=en), [KTH](https://www.kth.se/en), Jan 2020, title: A generic framework for integration of Cyber-physical Systems by use of intelligent systems (Midterm PhD Examination)\n\n## Member of Scientific/Organizing committee\n\n- [International Semantic Web Conference, 2020](https://iswc2020.semanticweb.org/)\n- [Joint International Conference on Knowledge Graphs 2020](https://language-semantic.org/jist-kg-2020/)\n- [DaMaLOS-2020: First workshop on Research data* management for linked open science (* and associated Research Objects)](https://zbmed.github.io/damalos/)\n- [3rd Workshop on Quality of Open Data](https://bisconf.org/2020/qod/)\n- [2nd Iberoamerican Knowledge Graph and Semantic Web Conference](http://www.kgswc.org/)\n- [International Semantic Web Conference, 2019](https://iswc2019.semanticweb.org/)\n- [24th European Conference on Artificial Intelligence](http://ecai2020.eu/)\n- [International Semantic Web Conference - ISWC 2019](http://iswc2019.semanticweb.org/)\n- [8th Workshop on Ontology Design and Patterns](http://ontologydesignpatterns.org/wiki/WOP:2017#WOP2017_Chairs), WOP, 2017\n- [Workshop on Linked Enterprise Data Services, Provenance, Linking and QualitY](https://informatik2017.de/ws29-ledsplay17/), LEDSPLaY17, 2017\n- [Linked Data Quality Workshop](http://ldq.semanticmultimedia.org/), 2017\n- [ISW-LOD2016](http://ciep.ing.uaslp.mx/sw-lod/) (4th International Workshop on Semantic Web and Linked Open Data), Puerto Rico, 2016\n- [SEMANTiCS 2016](https://2016.semantics.cc/), Senior PC member\n- [Latin American Symposium on Data Management Systems](http://www.clei2016.cl/Sim7.html), Co-chair\n- [W3c workshop on Multilingual web](https://www.multilingualweb.eu/documents/2015-riga-workshop/2015-riga-cfp), 2015\n- [SEMANTiCS 2015](https://2015.semantics.cc/) Research Track (11th International Conference on Semantic Systems, Research Track), Senior PC member\n- [ReCert 2015 (1st International Workshop on Linked Data Repair and Certification)](http://recert-ld.github.io/2015/), 2015\n- [International Conference on Knowledge Engineering and Semantic Web](http://2014.kesw.ru/), 2014\n- 16th International Simposium on Computers in Education, 2014\n- [Workshop on Ontology and Semantic Web Patterns](http://ontologydesignpatterns.org/wiki/WOP:2016), WOP, 2014, 2015, 2016, 2017\n- [Spanish conference on service science and engineering](https://labra.weso.es/www.congresocedi.es/es/jcis), 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014\n- [Encuentro Nacional en Ciencias de la Computación](http://enc2014.cicese.mx/index.html), Mexico, 2014\n- Jornadas de Enseñanza Universitaria de Informática, Jenui, 2014, 2015, 2016, [2017](http://jenui2017.unex.es/)\n- 12th International Semantic Web Conference - Evaluation Track, 2012\n- 9th Extended Semantic Web Conference, 2012\n- Metadata and Semantics Research Conference, MTSR, 2012\n- Social Data and Semantic Web, Workshop of the 10th International Conference on Semantic Web\n- Workshop on LInked Data en Conferencia Española de la Asociación para La Inteligencia Artificial (CAEPIA, 2011)\n- Workshop on Software Engineering and eLearning (ISELEAR), 2010, 2011\n- Brazilian Symposium on Programming Languages (SBLP), 2003, 2004, 2005, 2006, 2007, 2008, 2009\n- Jornadas sobre Programación y Lenguajes (PROLE), 2008\n- International Conference on Web Engineering, ICWE, 2003, 2004, 2006\n- Fourth International Workshop on Language, Descriptions, Tools and Applications, 2004\n\n## Reviewer of papers in the following journals\n\n- [Computer Standards and Interfaces](https://www.journals.elsevier.com/computer-standards-and-interfaces), Reviewer, 2017\n- [Bioinformatics](https://academic.oup.com/bioinformatics), 2015\n- [Computers and Human Behaviour](https://www.journals.elsevier.com/computers-in-human-behavior), Reviewer, 2013-2016\n- [Computers in Industry](https://www.journals.elsevier.com/computers-in-industry), Reviewer and Special issue editor, 2014\n- [Telematics and Informatics](https://www.journals.elsevier.com/telematics-and-informatics), Reviewer, 2016, 2017\n- [Expert Systems with Applications](https://www.journals.elsevier.com/expert-systems-with-applications), Reviewer, 2017\n- [Engineering Applications of Artificial Intelligence](https://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/), reviewer, 2016\n- [Information Sciences](https://www.journals.elsevier.com/information-sciences/), reviewer, 2014\n- [International Journal of Universal Computer Science](http://www.jucs.org/), reviewer, 2013\n- [Science of Computer Programming](https://www.journals.elsevier.com/science-of-computer-programming/), reviewer, 2013\n- [Semantic Web Journal](http://www.semantic-web-journal.net/), reviewer, 2013\n- [Web Semantics: Science, Services and Agents on the World Wide Web](http://www.sciencedirect.com/science/journal/15708268), reviewer, 2013\n\n## Advisory board of the following publications\n\n- [Global Hospitality and Tourism Management Technologies: Applications and Issues](https://www.igi-global.com/book/global-hospitality-tourism-management-technologies/51923), Editors: Patricia Ordóñez de Pablos, R. Tennyson, and J-Y. Zhao, 2011, IGI-Global. Pp. 1-270 ISBN DOI: 10.4018 ⁄ 978 -1-61350-041-5, ISBN13: 978-1-61350-041-5, ISBN10: 1-61350-041-6\n- [Semantic Web Personalization and Context Awareness: Management of Personal Identities and Social Networking](https://www.igi-global.com/book/semantic-web-personalization-context-awareness/47405), Editors: Lytras, M.D., Ordóñez de Pablos, P. and Damiani, E., IGI-Global, pp.1-232, 2011, DOI: 10.4018 ⁄ 978 -1-61520-921-7, ISBN13: 9781615209217, ISBN10: 1615209212, EISBN13: 9781615209224.\n- [Digital Culture and E-Tourism: Technologies, Applications and Management Approaches](https://www.igi-global.com/book/digital-culture-tourism/45944), Editors: Lytras, M.D., Ordóñez de Pablos, P., Damiani, E. and Diaz, L. (2011), Editorial IGI-Global. Pp. 1-270. ISBN 9781615208678.\n- [Technology Enhanced Learning for People with Disabilities: Approaches and Applications](https://www.igi-global.com/book/technology-enhanced-learning-people-disabilities/41739), Editors: Ordóñez de Pablos, P., Zhao, Y. and Tennyson, R., 2010, Editorial IGI-Global, pp- 1-276. ISBN-10: 1615209239 -ISBN-13: 978-1615209231. DOI: 10.4018 ⁄ 978 -1-61520-923-1\n\n## More personal information\n\nI was the drummer of [Kactus Jack](https://es.wikipedia.org/wiki/Kactus_Jack). You can see the original [unofficial home page](https://labra.weso.es/Kactus/Welcome.html), vintage style.\n\nI was also the drummer of [Los Murciélagos](http://no80s-gruposnacionales.blogspot.com.es/2009/01/los-murcielagos.html) and *Los Aborígenes*.\n\nI think I wrote my first Web Page in 1994. I found 2 versions of my home page from:\n\n- [2003](https://web.archive.org/web/20031029101917/http://www.di.uniovi.es/~labra/) (I found funny the Hobbies section…)\n- [2013](https://web.archive.org/web/20130616144520/http://di002.edv.uniovi.es/~labra/)\n\n## Recent Posts\n\n[More Posts](https://labra.weso.es/post/)\n\nComentario sobre el libro *El arte de la tesis doctoral*\n\nComentario sobre el libro *Datanomics*\n\nComentario sobre el libro *Científico en España*\n\nComentario sobre el libro *El Arte y la Ciencia de no hacer nada* de Andrew J. Smart\n\nDescription of my new home page\n\n## Projects\n\n- [Shapes-rs](https://www.weso.es/shapes-rs/) RDF data shapes implementation in Rust\n- [WShEx](https://www.weso.es/WShEx/) Wikibase varaint of ShEx\n- [rdfshape](https://rdfshape.weso.es/) RDF playground\n- VARSSHA Validating RDF Streams using Shapes\n- [SHACLex](http://labra.github.io/shaclex/) SHACL & ShEx implementation in Scala\n- [ShExkell](https://github.com/weso/shexkell/) A preliminary implementation of ShEx in Haskell\n- Language Prototyping System This project was part of my PhD thesis where the main goal was to implement a system that could create programming languages by combining different semantic blocks. It was based on the use of monads and I was happy to build functional, imparative, logic and object oriented programming languages by combining those building blocks. The implementation was developed in Haskell.\n- [Multilingual Linked Data Patterns](http://www.weso.es/MLODPatterns) Multilingual Linked Data Patterns\n\n## Teaching\n\nI teach the following courses:\n\n- [Software architecture](https://arquisoft.github.io/) - [Bachelor of Software Engineering](http://ingenieriainformatica.uniovi.es/web/ingenieriainformatica/infoacademica/grado), [University of Oviedo](http://uniovi.es/), Spain. This course is taught in Spanish and English.\n- [Semantic Web](http://www.weso.es/websem/) - [Master in Web Engineering](http://miw.uniovi.es/), [University of Oviedo](http://uniovi.es/), Spain\n- [Advances in Semantic Web](http://www.weso.es/websem/) - [Master in Web Engineering](http://miw.uniovi.es/), [University of Oviedo](http://uniovi.es/), Spain\n- Web systems and applications - [Master in Information Technology](http://mti.cl/), [Technical University Federico Santa María](http://utfsm.cl/), Chile I also taught the courses:\n\n- Languages and Web standards - [Master in Web Engineering](http://miw.uniovi.es/), [University of Oviedo](http://uniovi.es/), Spain\n- Object Oriented Programming - [Master in Web Engineering](http://miw.uniovi.es/), [University of Oviedo](http://uniovi.es/), Spain\n- Web services - [Master in Web Engineering](http://miw.uniovi.es/), [University of Oviedo](http://uniovi.es/), Spain\n- [Logic](http://di002.edv.uniovi.es/~labra/Logica/Logica.html) - Bachelor of Computer Science, [University of Oviedo](http://uniovi.es/)\n- [Declarative Programming](http://www.di.uniovi.es/~labra/PDec) - Bachelor of Computer Science, [University of Oviedo](http://uniovi.es/)\n- Language Processors - [EPIG](http://www.epig.uniovi.es/)"},"author":{"name":"Jose Emilio Labra Gayo","bio":"Full Professor"},"metadata":{"timestamp":"2019-11-14T18:37:00.000Z"},"media":[],"aiComments":[]},{"id":"web-xhx5o1","shareId":"RbMRdMHa7m","platform":"web","url":"https://link.springer.com/chapter/10.1007/978-3-030-30793-6_9","title":"Validating Shacl Constraints over a Sparql Endpoint","shareMode":"full","createdAt":1787214663744,"postedAt":"2019-01-01T00:00:00.000Z","publishedDate":"2019-01-01T00:00:00.000Z","archivedAt":"2026-08-20T08:30:58.947Z","archivedDate":"2026-08-20T08:30:58.947Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/RbMRdMHa7m","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/VDJ8rhwIuG/media/0.jpg","contentType":"article","content":{"text":"## Abstract\n\nshacl (Shapes Constraint Language) is a specification for describing and validating RDF graphs that has recently become a W3C recommendation. While the language is gaining traction in the industry, algorithms for shacl constraint validation are still at an early stage. A first challenge comes from the fact that RDF graphs are often exposed as sparql endpoints, and therefore only accessible via queries. Another difficulty is the absence of guidelines about the way recursive constraints should be handled. In this paper, we provide algorithms for validating a graph against a shacl schema, which can be executed over a sparql endpoint. We first investigate the possibility of validating a graph through a single query for non-recursive constraints. Then for the recursive case, since the problem has been shown to be NP-hard, we propose a strategy that consists in evaluating a small number of sparql queries over the endpoint, and using the answers to build a set of propositional formulas that are passed to a SAT solver. Finally, we show that the process can be optimized when dealing with recursive but tractable fragments of shacl, without the need for an external solver. We also present a proof-of-concept evaluation of this last approach.\n\nThis is a preview of subscription content, [log in via an institution](https://wayf.springernature.com/?redirect_uri=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-30793-6_9%3Ferror%3Dcookies_not_supported%26code%3D0231c810-7858-4e62-838d-c6d0b0800f20) to check access.\n\n## Access this chapter\n\n[Log in via an institution](https://wayf.springernature.com/?redirect_uri=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-30793-6_9%3Ferror%3Dcookies_not_supported%26code%3D0231c810-7858-4e62-838d-c6d0b0800f20)\n\n[Institutional subscriptions](https://www.springernature.com/gp/librarians/licensing/agc/ebooks)\n\n## Notes\n\n1. 1.[https://www.w3.org/TR/shacl/](https://www.w3.org/TR/shacl/).\n2. 2.[https://w3c.github.io/data-shapes/data-shapes-test-suite/](https://w3c.github.io/data-shapes/data-shapes-test-suite/).\n3. 3.with the exception of *Shaclex* [[4](#fn:4)], which can handle recursion, but not recursion and negation together in a principled way.\n4. 4.More exactly, *I* is an abstraction, standing for any syntactic constraint over an rdf term: exact value, datatype, regex, etc.\n5. 5.shacl paths are built like sparql property paths, but without the *NegatedPropertySet* operator.\n6. 6.We omit the trivial FILTER (?y1 =?y1 AND?y2 =?y2) for readability.\n7. 7.For some normalized schemas, it could happen that \$[\\![q_{{{\\,\\mathrm{def}\\,}}(s)} ]\\!]^{\\mathcal {G}}\$ always retrieves all nodes from \$\\mathcal {G} \$. This would be the case for example if \${{\\,\\mathrm{def}\\,}}(s) = s_1 \\wedge s_2\$. A simple optimization technique here consists in not executing such queries, and instantiate instead the rule pattern \$p_{{{\\,\\mathrm{def}\\,}}(s)}\$ with all nodes retrieved by all other queries (and bound to variable?x).\n\n## References\n\n## Editor information\n\n### Editors and Affiliations\n\n## Rights and permissions\n\n## About this paper\n\n### Cite this paper\n\nCorman, J., Florenzano, F., Reutter, J.L., Savković, O. (2019). Validating Shacl Constraints over a Sparql Endpoint. In: Ghidini, C., *et al.* The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_9\n\n- [.RIS](https://citation-needed.springer.com/v2/references/10.1007/978-3-030-30793-6_9?format=refman&flavour=citation)\n- [.ENW](https://citation-needed.springer.com/v2/references/10.1007/978-3-030-30793-6_9?format=endnote&flavour=citation)\n- [.BIB](https://citation-needed.springer.com/v2/references/10.1007/978-3-030-30793-6_9?format=bibtex&flavour=citation) - DOI https://doi.org/10.1007/978-3-030-30793-6_9\n- Published 17 October 2019\n- Publisher NameSpringer, Cham\n- Print ISBN978-3-030-30792-9\n- Online ISBN978-3-030-30793-6\n- eBook Packages [Computer Science](https://link.springer.com/search?facet-content-type=%22Book%22&package=11645&facet-start-year=2019&facet-end-year=2019) [Computer Science (R0)](https://link.springer.com/search?facet-content-type=%22Book%22&package=43710&facet-start-year=2019&facet-end-year=2019) [Springer Nature Proceedings Computer Science](https://link.springer.com/search?facet-content-type=%22Book%22&package=85344&facet-start-year=2019&facet-end-year=2019)\n\n## Publish with us\n\n[Policies and ethics](https://www.springernature.com/gp/policies/book-publishing-policies)\n\n## Societies and partnerships\n\n- [![the Semantic Web Science Association](https://published-with.public.springernature.app/img/swsa.jpg) the Semantic Web Science Association](http://swsa.semanticweb.org/)\n1. DBpedia. [wiki.dbpedia.org/downloads-2016-10](https://wiki.dbpedia.org/downloads-2016-10)\n2. Extended version. [https://www.inf.unibz.it/krdb/tech-reports/](https://www.inf.unibz.it/krdb/tech-reports/)\n3. SHACL Test Suite. [w3c.github.io/data-shapes/data-shapes-test-suite/](http://w3c.github.io/data-shapes/data-shapes-test-suite/)\n4. Shaclex. [http://github.com/labra/shaclex/](http://github.com/labra/shaclex/) [↩](#fnref:4)\n5. Shex.js. [http://github.com/shexSpec/shex.js/](http://github.com/shexSpec/shex.js/)\n6. Source code and experiment’s material. [http://github.com/rdfshapes/shacl-sparql](http://github.com/rdfshapes/shacl-sparql)\n7. Stardog ICV. [https://www.stardog.com/blog/data-quality-with-icv/](https://www.stardog.com/blog/data-quality-with-icv/)\n8. TopBraid Composer. [http://www.topquadrant.com/products/topbraid-composer/](http://www.topquadrant.com/products/topbraid-composer/)\n9. 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[https://doi.org/10.1007/978-3-030-00671-6_19](https://doi.org/10.1007/978-3-030-00671-6_19) [Chapter](https://link.springer.com/doi/10.1007/978-3-030-00671-6_19) [Google Scholar](https://scholar.google.com/scholar_lookup?&title=Semantics%20and%20validation%20of%20recursive%20SHACL&pages=318-336&publication_year=2018%202018%202018&author=Corman%2CJ&author=Reutter%2CJL&author=Savkovi%C4%87%2CO)\n11. Corman, J., Reutter, J.L., Savković, O.: Semantics and validation of recursive SHACL (extended version). Technical report KRDB18-1, Free University Bozen-Bolzano (2018). [https://www.inf.unibz.it/krdb/tech-reports/](https://www.inf.unibz.it/krdb/tech-reports/) [Google Scholar](https://scholar.google.com/scholar_lookup?&title=Semantics%20and%20Validation%20of%20Recursive%20SHACL&pages=318-336&publication_year=2018&author=Corman%2CJulien&author=Reutter%2CJuan%20L.&author=Savkovi%C4%87%2COgnjen)\n12. Corman, J., Reutter, J.L., Savković, O.: A tractable notion of stratification for SHACL. 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Agents World Wide Web **7** (2), 74–89 (2009) [Article](https://doi.org/10.1016%2Fj.websem.2009.02.001) [Google Scholar](https://scholar.google.com/scholar_lookup?&title=Bridging%20the%20gap%20between%20OWL%20and%20relational%20databases&journal=Web%20Semant.%3A%20Sci.%20Serv.%20Agents%20World%20Wide%20Web&volume=7&issue=2&pages=74-89&publication_year=2009&author=Motik%2CB&author=Horrocks%2CI&author=Sattler%2CU)\n15. Reutter, J.L., Soto, A., Vrgoč, D.: Recursion in SPARQL. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 19–35. Springer, Cham (2015). [https://doi.org/10.1007/978-3-319-25007-6_2](https://doi.org/10.1007/978-3-319-25007-6_2) [Chapter](https://link.springer.com/doi/10.1007/978-3-319-25007-6_2) [Google Scholar](https://scholar.google.com/scholar_lookup?&title=Recursion%20in%20SPARQL&pages=19-35&publication_year=2015%202015%202015&author=Reutter%2CJL&author=Soto%2CA&author=Vrgo%C4%8D%2CD)\n16. Staworko, S., Boneva, I., Labra Gayo, J.E., Hym, S., Prud’hommeaux, E.G., Solbrig, H.: Complexity and expressiveness of ShEx for RDF. In: ICDT (2015) [Google Scholar](https://scholar.google.com/scholar?&q=Staworko%2C%20S.%2C%20Boneva%2C%20I.%2C%20Labra%20Gayo%2C%20J.E.%2C%20Hym%2C%20S.%2C%20Prud%E2%80%99hommeaux%2C%20E.G.%2C%20Solbrig%2C%20H.%3A%20Complexity%20and%20expressiveness%20of%20ShEx%20for%20RDF.%20In%3A%20ICDT%20%282015%29)\n17. Tao, J., Sirin, E., Bao, J., McGuinness, D.L.: Integrity constraints in owl. In: AAAI (2010) [Google Scholar](https://scholar.google.com/scholar?&q=Tao%2C%20J.%2C%20Sirin%2C%20E.%2C%20Bao%2C%20J.%2C%20McGuinness%2C%20D.L.%3A%20Integrity%20constraints%20in%20owl.%20In%3A%20AAAI%20%282010%29) [↩](#fnref:17)"},"author":{"name":"Julien Corman, Fernando Florenzano, Juan L. Reutter, Ognjen Savković","bio":"shacl (Shapes Constraint Language) is a specification for describing and validating RDF graphs that has recently become a W3C recommendation. While the language is gaining traction in the industry, algorithms for shacl constraint validation are still at an early..."},"metadata":{"timestamp":"2019-01-01T00:00:00.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/VDJ8rhwIuG/media/0.jpg","type":"image","width":153,"height":232}],"aiComments":[]},{"id":"web-hdcw6y","shareId":"NPet9IueAt","platform":"web","url":"https://dl.acm.org/doi/10.1007/978-3-030-30793-6_9","title":"Validating Shacl Constraints over a Sparql Endpoint | The Semantic Web","shareMode":"full","createdAt":1787214623164,"postedAt":"2026-08-20T08:30:18.735Z","publishedDate":"2026-08-20T08:30:18.735Z","archivedAt":"2026-08-20T08:30:20.512Z","archivedDate":"2026-08-20T08:30:20.512Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/NPet9IueAt","thumbnail":"https://dl.acm.org/cms/asset/c586a540-46fd-4b00-8004-4e59a0d5a2a6/978-3-030-30793-6.cover.jpg","contentType":"article","content":{"text":"## Abstract\n\nshacl (Shapes Constraint Language) is a specification for describing and validating RDF graphs that has recently become a W3C recommendation. While the language is gaining traction in the industry, algorithms for shacl constraint validation are still at an early stage. A first challenge comes from the fact that RDF graphs are often exposed as sparql endpoints, and therefore only accessible via queries. Another difficulty is the absence of guidelines about the way recursive constraints should be handled. In this paper, we provide algorithms for validating a graph against a shacl schema, which can be executed over a sparql endpoint. We first investigate the possibility of validating a graph through a single query for non-recursive constraints. Then for the recursive case, since the problem has been shown to be NP-hard, we propose a strategy that consists in evaluating a small number of sparql queries over the endpoint, and using the answers to build a set of propositional formulas that are passed to a SAT solver. Finally, we show that the process can be optimized when dealing with recursive but tractable fragments of shacl, without the need for an external solver. We also present a proof-of-concept evaluation of this last approach."},"author":{"name":"dl.acm.org"},"metadata":{"timestamp":"2026-08-20T08:30:18.735Z"},"media":[{"url":"https://dl.acm.org/cms/asset/c586a540-46fd-4b00-8004-4e59a0d5a2a6/978-3-030-30793-6.cover.jpg","type":"image"}],"aiComments":[]},{"id":"web-qhx5ei","shareId":"3n9DfB2usx","platform":"web","url":"https://www.dataaihub.co/learn/knowledge-graph-best-practices","title":"Knowledge Graph Best Practices Guide | Data AI Hub","shareMode":"full","createdAt":1787214598606,"postedAt":"2026-07-20T00:00:00.000Z","publishedDate":"2026-07-20T00:00:00.000Z","archivedAt":"2026-08-20T08:29:54.364Z","archivedDate":"2026-08-20T08:29:54.364Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/3n9DfB2usx","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/4iM5q4ALRx/media/0.png","contentType":"article","content":{"text":"## TL;DR\n\n- **Start with use-case-driven modeling** — define the questions your graph must answer before designing the full ontology. A knowledge graph is a semantic model; the graph database is just where you store it.\n- **Entity resolution is non-negotiable** — duplicate nodes destroy trust faster than missing data. Resolution is a platform capability, not an ETL afterthought.\n- **Schema evolution is continuous** — plan for additive changes, versioning, and backward-compatible migrations from day one. Ontology changes follow the same rigor as API versioning.\n- **Data quality gates belong in the pipeline** — validate at ingest with [SHACL](https://www.dataaihub.co/learn/shacl) or constraints, not after users discover bad data in production.\n- **Governance beats technology** — assign stewards per domain, version ontologies in Git, and review schema changes like API changes. Neo4j, Stardog, GraphDB, Neptune, and Jena are interchangeable compared to modeling discipline.\n\n## Why This Matters\n\nMost knowledge graph projects fail quietly. Not because Neo4j or GraphDB was the wrong choice, but because the graph became an unmaintainable tangle of duplicate entities, inconsistent relationship types, and ontologies nobody dared to change.\n\nA support team builds a graph to link customers, tickets, and products. Six months later, `Customer` nodes exist under four naming conventions, `RELATED_TO` edges mean different things in different domains, and a schema change breaks three downstream dashboards. The graph still exists — but nobody trusts it. A pharma team loads drug–target–trial data without [SHACL](https://www.dataaihub.co/learn/shacl) validation; 12% of `ClinicalTrial` nodes reference deprecated trial IDs. Compliance reporting fails an audit.\n\nBest practices are not bureaucracy. They are the engineering patterns that keep a [knowledge graph](https://www.dataaihub.co/learn/what-is-a-knowledge-graph) queryable, trustworthy, and evolvable as your organization learns what it actually needs from structured knowledge. They are the difference between a departmental experiment and an [enterprise knowledge graph](https://www.dataaihub.co/learn/enterprise-knowledge-graphs) that powers search, analytics, compliance, and [GraphRAG](https://www.dataaihub.co/learn/graphrag) / [RAG](https://www.dataaihub.co/learn/rag) consumers for years.\n\nThe critical distinction throughout: **a knowledge graph is not a graph database**. Stardog, GraphDB, Neptune, Neo4j, and Apache Jena are storage and query engines. Best practices govern the semantic layer — ontology, identity, provenance, validation, and stewardship — that makes those engines worth operating.\n\n\n\n## The Problem\n\nKnowledge graphs are deceptively flexible. Unlike relational schemas, you can add nodes and edges without migrations — which encourages organic growth until the graph becomes impossible to reason about.\n\nThree failure modes drive the need for disciplined practices:\n\n**Ontology sprawl.** Every team adds classes and predicates for their immediate need. `worksFor`, `employedBy`, `hasEmployer`, and `employeeOf` coexist as synonyms nobody consolidates. Queries become archaeology. Downstream [GraphRAG](https://www.dataaihub.co/learn/graphrag) pipelines retrieve inconsistent relationship types, and LLM answers contradict themselves.\n\n**Data quality decay.** Without validation at ingest, 15% of `Person` nodes lack email, `suppliedBy` edges point to deprecated vendors, and entity resolution duplicates accumulate weekly. Trust erodes silently until a compliance officer finds double-counted counterparty exposure or a [RAG](https://www.dataaihub.co/learn/rag) system cites a merged-but-wrong entity.\n\n**Schema change paralysis.** Downstream consumers hardcode relationship names. Changing `dependsOn` to `DEPENDS_ON` breaks [SPARQL](https://www.dataaihub.co/learn/sparql) queries in production. Teams stop evolving the schema; the graph fossilizes while business requirements change.\n\n**Graph-database confusion.** Teams optimize Neo4j indexing or Stardog cluster sizing while ignoring ontology governance. They have a fast graph store — not a knowledge graph. Performance tuning matters, but it cannot fix duplicate entities or missing provenance.\n\nBest practices provide **guardrails** — not to slow delivery, but to prevent rework that costs 10x more than upfront discipline.\n\n\n\n## How We Got Here\n\nKnowledge graph best practices emerged from repeated project failures across semantic web, graph database, and enterprise data integration communities:\n\n**Diagram: Evolution of KG engineering practices**\n\n\n\n```\nflowchart LR\nA[Ad-hoc graph loads] --> B[Ontology standards / OWL]\nB --> C[SHACL validation]\nC --> D[MDM + entity resolution]\nD --> E[Governed enterprise KG]\nE --> F[GraphRAG + LLM grounding]\n```\n\n*Each generation added discipline after learning that flexible graphs without governance become unmaintainable.*\n\n\n\n| Phase | What teams learned | Practice that emerged |\n| --- | --- | --- |\n| **Early graph DB projects (2010s)** | Fast traversals ≠ trusted data | Use-case-first modeling |\n| **Semantic web adoption (2010s–2020s)** | OWL complexity kills projects | Minimal ontology, version in Git |\n| **Enterprise MDM integration (2010s–2020s)** | Golden records need graph relationships | Canonical identity + `sameAs` |\n| **SHACL standardization (2017+)** | Post-load cleaning fails at scale | Validate at ingest, quarantine violations |\n| **AI consumption (2024+)** | LLM extraction injects noise | Human-in-the-loop + SHACL before merge |\n| **Enterprise platforms (2020s+)** | Departmental graphs don't scale | Stewardship councils, named graphs, federation |\n\nFrameworks like [LangChain](https://www.dataaihub.co/tools/langchain) and [LlamaIndex](https://www.dataaihub.co/tools/llamaindex) make it easy to connect LLMs to graphs — but they do not replace modeling discipline. The graph you connect must be governed first.\n\n\n\n## Architecture\n\nBest practices define the **semantic architecture** of a production knowledge graph — independent of whether you store in Stardog, GraphDB, Neptune, Neo4j, or Jena.\n\n**Diagram: Knowledge graph best-practices architecture**\n\n\n\n```\nflowchart TB\nsubgraph Modeling[\"Modeling Layer\"]\nUC[Use-Case Query Patterns]\nONT[Minimal Ontology]\nID[Canonical Identity Strategy]\nend\n\nsubgraph Quality[\"Data Quality Layer\"]\nSHACL[SHACL / Constraints]\nER[Entity Resolution]\nPROV[Provenance on Every Fact]\nend\n\nsubgraph Evolution[\"Schema Evolution Layer\"]\nVER[Ontology Versioning in Git]\nDUAL[Dual-Write Migrations]\nIMP[Impact Analysis]\nend\n\nsubgraph Governance[\"Governance Layer\"]\nST[Domain Stewards]\nREV[PR Review for Schema Changes]\nMET[Quality Metrics Dashboard]\nend\n\nsubgraph Engine[\"Graph Database Engine\"]\nDB[Stardog / GraphDB / Neptune / Neo4j / Jena]\nend\n\nUC --> ONT\nONT --> ID\nID --> SHACL\nSHACL --> ER\nER --> PROV\nPROV --> DB\nVER --> ONT\nDUAL --> VER\nIMP --> REV\nST --> REV\nREV --> MET\nMET --> SHACL\n```\n\n*The knowledge graph is the modeled, validated, governed semantic layer — the database engine is the bottom box.*\n\n\n\n### Graph design principles\n\n\n\n#### Principle 1: Use-case-first modeling\n\nDo not model the entire enterprise before loading data. Pick one high-value use case — supplier risk, customer 360, document entity linking, fraud counterparty exposure — and model only the entities and relationships that use case requires.\n\n\n\n| Use Case | Core Entities | Key Relationships |\n| --- | --- | --- |\n| Customer 360 | Customer, Account, Contact | `hasAccount`, `primaryContact`, `ownsSubscription` |\n| Supply chain risk | Product, Component, Supplier, Country | `contains`, `suppliedBy`, `locatedIn` |\n| Fraud / compliance | Counterparty, Trade, SanctionList | `references`, `listedOn`, `holds` |\n| Life sciences | Drug, Target, Trial, Disease | `targets`, `tests`, `treats` |\n| Expert finder | Person, Skill, Project, Document | `hasSkill`, `workedOn`, `authored` |\n| Digital twin | Asset, Sensor, Event | `hasSensor`, `emits`, `triggers` |\n\nExpand the ontology when a **new use case** needs entities the current schema lacks — not when someone imagines a future need.\n\n\n\n#### Principle 2: Stable identity\n\nEvery entity gets a canonical ID independent of source system identifiers.\n\n\n\n```\n# Bad - source ID as primary key\n(customer:CRM-8842)\n\n# Good - canonical ID with source links\n(customer:acme-corp-001 { crmId: \"CRM-8842\", billingId: \"BILL-991\" })\n```\n\nUse `sameAs` ([RDF](https://www.dataaihub.co/learn/rdf)) or `SAME_AS` edges (property graphs) to link source records. Entity resolution merges duplicates into the canonical node.\n\n\n\n#### Principle 3: Typed, minimal relationships\n\nPrefer specific relationship types over generic ones.\n\n\n\n| Avoid | Prefer | Why |\n| --- | --- | --- |\n| `RELATED_TO` | `suppliedBy`, `authoredBy`, `dependsOn` | Queries filter on edge type; generics are unqueryable |\n| `hasProperty` edges for everything | Node attributes for scalar values | Properties on nodes are simpler; reserve edges for traversals |\n| Deep reification | Edge properties (LPG) or RDF-star | Reification adds triples for every edge attribute |\n\n\n\n> Tip Cap relationship types at what your query patterns need. If nobody traverses `mentoredBy`, do not model it until someone asks.\n\n\n\n#### Principle 4: Provenance on every fact\n\nStore where each assertion came from:\n\n- `sourceSystem` — CRM, ERP, manual steward, LLM extraction\n- `assertedAt` — timestamp of ingestion\n- `confidence` — for ML-extracted facts\n- `validFrom` / `validTo` — for temporal facts Without provenance, debugging wrong answers and satisfying auditors is impossible.\n\n\n\n### Ontology design\n\nAn [ontology](https://www.dataaihub.co/learn/ontologies) defines the vocabulary your graph speaks — classes, properties, constraints, and hierarchies.\n\n**Start minimal, version always:**\n\n1. **Core classes** — 5–15 types covering your use case (e.g., `Person`, `Organization`, `Product`).\n2. **Object properties** — relationships between classes (`worksAt`, `supplies`).\n3. **Data properties** — scalar attributes (`email`, `foundedYear`).\n4. **Constraints** — cardinality, required fields, allowed value ranges. Store ontologies in Git. Tag releases (`ontology-v1.3.0`). Downstream pipelines pin to a version.\n\n\n\n> Important Treat ontology changes like API versioning. Breaking renames require migration scripts and consumer notification.\n\n\n\n### RDF vs property graph modeling\n\n\n\n| Practice | RDF (Stardog/GraphDB/Neptune/Jena) | Property Graph (Neo4j) |\n| --- | --- | --- |\n| Class definition | `rdfs:Class`, OWL | Node labels |\n| Relationship typing | Predicate URI | Relationship type |\n| Validation | SHACL shapes | Neo4j constraints, Cypher checks |\n| Hierarchy | `rdfs:subClassOf` | Label inheritance (varies by DB) |\n| Identity linking | `owl:sameAs` | `SAME_AS` edges |\n\nPick one model per graph. Converting between RDF and property graphs in production is expensive — design for your query language and compliance requirements upfront. See [RDF vs Property Graph](https://www.dataaihub.co/learn/rdf-vs-property-graph).\n\n\n\n## Step-by-Step Flow\n\nFollow this flow when designing, building, and maintaining a production knowledge graph:\n\n1. **Document query patterns** — Write 5–10 questions the graph must answer. These drive schema design, not hypothetical future needs.\n2. **Draft minimal ontology** — 5–15 classes, typed relationships, required attributes for [SHACL](https://www.dataaihub.co/learn/shacl). Review with domain expert.\n3. **Define identity strategy** — Canonical ID format, `sameAs` linking, entity resolution thresholds. Align with MDM if present.\n4. **Write SHACL shapes** — Required fields, cardinality, allowed enums. Run in CI against sample data.\n5. **Build ingest pipeline with validation gate** — Map source → ontology → validate → resolve → publish. Quarantine failures.\n6. **Load pilot dataset** — Real data for one use case. Run golden queries. Fix modeling gaps.\n7. **Implement entity resolution** — Deterministic first, then fuzzy/ML, then steward review queue.\n8. **Connect first consumer** — Dashboard, search, or [GraphRAG](https://www.dataaihub.co/learn/graphrag) pilot. Measure against KPIs.\n9. **Establish stewardship cadence** — Biweekly ontology review, weekly quality metrics, PR review for schema changes.\n10. **Plan schema evolution** — Additive changes freely; breaking changes via dual-write migration with impact analysis. **Diagram: Entity resolution flow**\n\n\n\n```\nflowchart LR\nA[Source Records] --> B[Normalize]\nB --> C[Blocking]\nC --> D[Score Matches]\nD --> E{Confidence}\nE -->|≥ 0.95| F[Auto-merge]\nE -->|0.70–0.95| G[Steward Review]\nE -->| I[Canonical Entity]\nG --> I\nH --> I\nI --> J[Audit Log Entry]\n```\n\n*Every merge is logged and reversible. Auto-merge only above measured precision thresholds.*\n\n**Diagram: Knowledge ingestion pipeline with quality gates**\n\n\n\n```\nflowchart LR\nA[Extract] --> B[Map to Ontology]\nB --> C[SHACL Validate]\nC -->|Pass| D[Entity Resolution]\nC -->|Fail| Q[Quarantine]\nQ --> S[Steward Fix]\nS --> D\nD --> E[Publish to Graph]\nE --> F[Update Metrics]\nE --> G[Sync Search / Vector Index]\n```\n\n*Validation before resolution before publication — never load first and clean later.*\n\n\n\n## Real Production Example\n\n\n\n### Retail: phased product knowledge graph\n\nA retail company models `Product`, `Category`, `Brand`, and `Supplier` in phase one — 10K SKUs from PIM with SHACL requiring `sku`, `name`, and `brand`. Phase two adds `Component` and `contains` for compliance (conflict minerals, restricted substances) without breaking changes — new classes and optional relationships only. Phase three runs entity resolution on duplicate suppliers (\"Acme Inc\" vs \"ACME Corporation\") with steward review below 0.9 confidence.\n\n**Quality gates:** Biweekly ontology reviews. CI-gated SHACL validation — failure rate > 0.1% blocks deploy. Weekly metrics: orphan rate, duplicate queue depth, staleness.\n\n**Outcome:** Compliance team queries restricted-substance exposure across 40K SKUs in seconds. [GraphRAG](https://www.dataaihub.co/learn/graphrag) answers \"Which products contain Component X from Supplier Y?\" with graph citations. [RAG](https://www.dataaihub.co/learn/rag) handles policy document retrieval; the graph handles structure.\n\n\n\n### Financial services: fraud counterparty graph\n\nA bank builds a property graph in Neo4j for real-time fraud traversals while maintaining RDF canonical entities in GraphDB for compliance exports. Best practices enforced on both:\n\n- Canonical `Counterparty` IDs from MDM with `sameAs` links to trading system IDs\n- Typed relationships: `HOLDS`, `REFERENCES`, `LISTED_ON` — no generic `RELATED_TO`\n- Provenance on every edge: `{sourceSystem, recordId, assertedAt}`\n- SHACL validation on RDF layer before compliance reporting\n- Steward review for entity merges below 0.92 confidence **Outcome:** Fraud analysts traverse counterparty exposure in sub-second Neo4j queries. Compliance exports auditable RDF triples with full lineage.\n\n\n\n### Life sciences: drug–target–trial linkage\n\nA pharma company models `(Drug)-[:TARGETS]->(Protein)-[:ASSOCIATED_WITH]->(Disease)` and `(Drug)-[:TESTED_IN]->(ClinicalTrial)` using RDF + OWL on GraphDB. Best practices:\n\n- Minimal ontology extended only when R&D teams need new relationship types\n- Ontology versioned in Git; ingest pipelines pin to `ontology-v2.4.0`\n- Entity resolution on drug names using InChIKey deterministic match + fuzzy fallback\n- LLM-extracted relationships from publications validated by SHACL before merge\n- Named graphs isolate internal R&D from licensed external data\n\n### Government: citizen services graph\n\nA government agency connects benefits, tax, and permit systems. Best practices emphasize security and governance:\n\n- Named graph isolation per agency with query-time RBAC\n- No LLM-extracted facts without steward approval\n- Temporal edges (`validFrom` / `validTo`) for eligibility periods\n- Ontology evolution through public stewardship council with documented changelogs **Diagram: Ontology evolution workflow**\n\n\n\n```\nflowchart LR\nA[New Use Case Request] --> B[Gap Analysis]\nB --> C[Ontology Change Proposal]\nC --> D[Steward Review]\nD --> E[Git PR + SHACL CI]\nE --> F[Staging Validation]\nF --> G[Golden Query Tests]\nG --> H[Release Tag]\nH --> I[Consumer Notification]\n```\n\n*Schema evolution is a governed workflow — not ad-hoc triple injection.*\n\n\n\n## Design Decisions\n\n\n\n| Decision | Option A | Option B | When to choose |\n| --- | --- | --- | --- |\n| Ontology depth | Minimal (RDFS) | Rich (OWL reasoning) | Minimal for most production; OWL when automated inference is worth latency |\n| Schema authority | Central ontology team | Federated domain stewards | Central for Best Practice ✅ **Best Practices** — Track quality metrics weekly, version ontologies in Git, enforce SHACL in CI, and maintain a golden query suite per use case.\n\n\n\n| Dimension | Requirement |\n| --- | --- |\n| **Use case scope** | Documented query patterns driving schema; no orphan classes |\n| **Identity** | Canonical IDs; `sameAs` links to sources; entity resolution pipeline |\n| **Ontology governance** | Git-versioned; PR review for new types; deprecation policy |\n| **Validation** | SHACL or constraints at ingest; CI gate on staging loads |\n| **Provenance** | `sourceSystem`, `assertedAt` on all assertions |\n| **Schema evolution** | Additive-first; dual-write for breaking changes; migration runbooks |\n| **Monitoring** | Validation failure rate, orphan edges, duplicate queue, staleness |\n| **Access control** | Subgraph permissions for sensitive domains |\n| **Documentation** | Data dictionary with class/property definitions and example queries |\n| **Engine ops** | Backups, cluster health — necessary but insufficient without semantic discipline |\n\n\n\n### Data quality metrics to track weekly\n\n- **Orphan rate** — edges pointing to missing nodes\n- **Validation failure rate** — % of ingest batch rejected by SHACL\n- **Duplicate candidate queue depth** — unresolved entity resolution items\n- **Staleness** — % of nodes not updated within freshness SLA\n- **Attribute completeness** — % of required fields populated per class\n- **Ontology version drift** — consumers querying deprecated predicates **Diagram: Governance and security layers**\n\n\n\n```\nflowchart TB\nsubgraph Gov[\"Governance\"]\nST[Named Domain Stewards]\nPR[Schema PR Review]\nCHG[Changelog + Impact Analysis]\nend\n\nsubgraph Sec[\"Security\"]\nACL[Subgraph Access Control]\nAUD[Query Audit Log]\nend\n\nsubgraph Qual[\"Quality\"]\nSHACL[Ingest Validation]\nMET[Weekly Metrics Review]\nGOLD[Golden Query Suite]\nend\n\nST --> PR\nPR --> CHG\nACL --> AUD\nSHACL --> MET\nMET --> GOLD\nGOLD --> ST\n```\n\n*Governance, security, and quality form a continuous loop — not one-time setup.*\n\n\n\n> Important Assign a named steward per domain ontology. Anonymous shared ownership means nobody fixes data quality.\n\n\n\n## Related Guides\n\n**Knowledge graph cluster:**\n\n- [What Is a Knowledge Graph?](https://www.dataaihub.co/learn/what-is-a-knowledge-graph) · [Knowledge Graphs](https://www.dataaihub.co/learn/knowledge-graphs) · [Learn Knowledge Graphs](https://www.dataaihub.co/learn/learn-knowledge-graphs)\n- [Enterprise Knowledge Graphs](https://www.dataaihub.co/learn/enterprise-knowledge-graphs) · [Enterprise Knowledge Graph Architecture](https://www.dataaihub.co/learn/enterprise-knowledge-graph-architecture)\n- [Ontologies](https://www.dataaihub.co/learn/ontologies) · [SHACL](https://www.dataaihub.co/learn/shacl) · [RDF](https://www.dataaihub.co/learn/rdf) · [SPARQL](https://www.dataaihub.co/learn/sparql) · [OWL](https://www.dataaihub.co/learn/owl)\n- [Graph Databases](https://www.dataaihub.co/learn/graph-databases) · [Property Graphs](https://www.dataaihub.co/learn/property-graphs) · [Cypher](https://www.dataaihub.co/learn/cypher) · [RDF vs Property Graph](https://www.dataaihub.co/learn/rdf-vs-property-graph) **AI consumption:**\n\n- [GraphRAG](https://www.dataaihub.co/learn/graphrag) · [GraphRAG Architecture](https://www.dataaihub.co/learn/graphrag-architecture)\n- [Knowledge Graph + LLM](https://www.dataaihub.co/learn/knowledge-graph-llm) · [RAG](https://www.dataaihub.co/learn/rag) **Tools and rankings:**\n\n- [Best Vector Databases](https://www.dataaihub.co/tools/rankings/best-vector-databases) · [Neo4j Vector](https://www.dataaihub.co/tools/neo4j-vector) · [LangChain](https://www.dataaihub.co/tools/langchain) · [LlamaIndex](https://www.dataaihub.co/tools/llamaindex) **Prerequisites:** [What Is a Knowledge Graph?](https://www.dataaihub.co/learn/what-is-a-knowledge-graph) · [Ontologies](https://www.dataaihub.co/learn/ontologies) · [Knowledge Graphs](https://www.dataaihub.co/learn/knowledge-graphs)\n\n**Next topics:** [SHACL](https://www.dataaihub.co/learn/shacl) · [Enterprise Knowledge Graphs](https://www.dataaihub.co/learn/enterprise-knowledge-graphs) · [Graph Databases](https://www.dataaihub.co/learn/graph-databases)\n\n\n\n## Interview Questions\n\n1. **What is the most important best practice for a new knowledge graph?** - Expected: use-case-first modeling with minimal ontology; load real data early; validate at ingest.\n2. **Why is a knowledge graph not the same as a graph database?** - Expected: KG is semantic model (ontology, governance, identity, provenance); graph DB is storage engine.\n3. **How do you handle schema changes without breaking consumers?** - Expected: additive-first; dual-write migration; version ontology in Git; impact analysis; deprecation window.\n4. **Why auto-merge entity resolution cautiously?** - Expected: false merges are hard to reverse and corrupt all downstream queries; start conservative, measure precision.\n5. **When would you use SHACL vs database constraints?** - Expected: SHACL for RDF graphs (Stardog, GraphDB, Jena); Neo4j constraints for property graphs; both at ingest.\n6. **How do best practices enable GraphRAG and RAG?** - Expected: governed entities/relationships + provenance give AI systems trustworthy structure; [RAG](https://www.dataaihub.co/learn/rag) handles documents.\n7. **What metrics indicate a graph is failing?** - Expected: rising duplicate queue, validation failure rate, orphan edges, declining query adoption, staleness.\n8. **RDF or Neo4j for a new project — how do you decide?** - Expected: RDF for SHACL/OWL/federation/compliance; Neo4j for Cypher velocity and operational traversals.\n\n## Key Takeaways\n\n- Model for concrete use cases, not the entire enterprise upfront.\n- **A knowledge graph is not a graph database** — Stardog, GraphDB, Neptune, Neo4j, and Jena store graphs; best practices govern the semantic layer above them.\n- Canonical identity and entity resolution are prerequisites for trust.\n- Validate at ingest with [SHACL](https://www.dataaihub.co/learn/shacl); measure quality continuously.\n- Schema evolution is normal — plan additive changes and migration paths.\n- Governance (stewards, versioning, PR review) matters as much as technology.\n- Provenance on every fact enables debugging, auditing, and confident [GraphRAG](https://www.dataaihub.co/learn/graphrag) / [RAG](https://www.dataaihub.co/learn/rag) grounding.\n- Compare vector tools for hybrid AI pipelines in [Best Vector Databases](https://www.dataaihub.co/tools/rankings/best-vector-databases).\n\n## FAQs\n\n\n\n### How do I start building a knowledge graph?\n\nPick one use case, model 5–15 core classes, ingest a small real dataset, validate with [SHACL](https://www.dataaihub.co/learn/shacl), and iterate based on query failures — not hypothetical future needs.\n\n\n\n### How detailed should my ontology be?\n\nDetailed enough to answer your use-case queries and enforce data quality. Avoid OWL complexity you will not use. Expand when new use cases require new types.\n\n\n\n### RDF or property graph for a new project?\n\nRDF if you need standards compliance, linked data, and SHACL validation (Stardog, GraphDB, Neptune, Jena). Property graphs if your team prioritizes Cypher ergonomics and fast traversals (Neo4j). See [What Is a Knowledge Graph?](https://www.dataaihub.co/learn/what-is-a-knowledge-graph) and [RDF vs Property Graph](https://www.dataaihub.co/learn/rdf-vs-property-graph).\n\n\n\n### How do I handle schema changes in production?\n\nAdditive changes freely. Breaking changes go through dual-write migration with consumer notification. Version ontologies in Git with changelogs.\n\n\n\n### What is the biggest mistake teams make?\n\nOver-engineering the ontology before loading data, combined with skipping entity resolution. Both erode trust before the graph delivers value.\n\n\n\n### How do I measure data quality?\n\nTrack validation failure rate, orphan edges, duplicate entity rate, attribute completeness per class, and data freshness against SLA.\n\n\n\n### When is a knowledge graph the wrong choice?\n\nWhen your data is purely tabular with no relationship traversal needs, or when source data quality is too poor to invest in cleansing. Fix data foundations first.\n\n\n\n### Can LLMs replace ontology design?\n\nNo. LLMs can extract entities from documents, but ontology structure requires domain expertise and governance. Validate all LLM extractions before production merge.\n\n\n\n### How do best practices differ for enterprise vs departmental graphs?\n\nDepartmental graphs need Levels 1–2 practices (use-case ontology, ingest validation, basic stewardship). Enterprise graphs add federated governance, MDM integration, named graph isolation, and platform SLAs — see [Enterprise Knowledge Graphs](https://www.dataaihub.co/learn/enterprise-knowledge-graphs).\n\n\n\n### Which graph database should I choose?\n\nThe engine matters less than modeling discipline. Stardog and GraphDB excel at RDF/SHACL/federation. Neo4j excels at Cypher operational graphs. Neptune offers managed AWS. Jena offers open-source standards compliance. Choose based on team skills and compliance requirements — then apply the same best practices regardless.\n\n\n\n## References\n\n- [W3C — SHACL Specification](https://www.w3.org/TR/shacl/)\n- [W3C — OWL 2 Web Ontology Language](https://www.w3.org/TR/owl2-overview/)\n- [W3C — RDF 1.1 Concepts](https://www.w3.org/TR/rdf11-concepts/)\n- [Protégé Ontology Editor](https://protege.stanford.edu/)\n- [Apache Jena Documentation](https://jena.apache.org/documentation/)\n- [Neo4j — Graph Data Modeling Guidelines](https://neo4j.com/docs/getting-started/data-modeling/)\n- [Stardog Documentation — Data Modeling](https://docs.stardog.com/)"},"author":{"name":"Data AI Hub","bio":"Practical guidelines for designing, modeling, and maintaining knowledge graphs in production - ontology design, data quality, schema evolution, and governance patterns."},"metadata":{"timestamp":"2026-07-20T00:00:00.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/4iM5q4ALRx/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-jhm48a","shareId":"Zjka8TAGU3","platform":"web","url":"https://ipn4.paymentus.com/cp/www/terms-and-conditions-no-fee.html","title":"Payment Authorization Terms and Conditions: Customer Portal: Paymentus Corp","shareMode":"full","createdAt":1787120967463,"postedAt":"2026-08-19T06:29:23.170Z","publishedDate":"2026-08-19T06:29:23.170Z","archivedAt":"2026-08-19T06:29:24.984Z","archivedDate":"2026-08-19T06:29:24.984Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/Zjka8TAGU3","contentType":"article","content":{"text":"## Payment Authorization Terms & E-Sign Disclosure and Consent Notice\n\nWelcome to the secure bill payment technology service provided by **Paymentus Corporation** (“Paymentus”, \"we\" or \"us\"). 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We will provide you with notice of termination or change as required by law.\n\nRevision Date: May, 2019"},"author":{"name":"ipn4.paymentus.com"},"metadata":{"timestamp":"2026-08-19T06:29:23.170Z"},"media":[],"aiComments":[]},{"id":"web-p4trlo","shareId":"HQIPmp4TU4","platform":"web","url":"https://digiwares.xyz/","title":"Small Tools That Do One Thing Well","shareMode":"full","createdAt":1786055091881,"postedAt":"2026-08-06T22:24:45.362Z","publishedDate":"2026-08-06T22:24:45.362Z","archivedAt":"2026-08-06T22:24:47.600Z","archivedDate":"2026-08-06T22:24:47.600Z","viewCount":3,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/HQIPmp4TU4","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/XlnEjllL4F/media/0.png","contentType":"article","content":{"text":"## DigiWares — Privacy-First Tools Built in Bangkok\n\nPrivacy-first utilities for voice, documents, and productivity. Many run entirely in your browser - your files never leave your device. No signup required for most tools.\n\n## // flagship\n\n- [tera.fm](https://tera.fm/) -Stop scrolling. Start listening.\n- [voicetotextonline.com](https://voicetotextonline.com/) -Speech to text in 55+ languages\n- [tickk.app](https://tickk.app/) -Speak, save, sort it later\n- [voicebraindump.app](https://voicebraindump.app/) -Speak now, organize never\n- [sornic.com](https://sornic.com/) -Turn any article, PDF, or photo into audio\n\n## // documents & privacy\n\n- [ikrypt.com](https://ikrypt.com/) -Stop DM-ing passwords. 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Voice-to-text, audio news, document utilities, and more."},"metadata":{"timestamp":"2026-08-06T22:24:45.362Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/XlnEjllL4F/media/0.png","type":"image","width":2400,"height":1260}],"aiComments":[]},{"shareId":"1hEmwZs6Eb","platform":"instagram","url":"https://www.instagram.com/p/DbbvXvZMTXV/","shareMode":"full","createdAt":1786045080757,"postedAt":"2026-07-30T22:15:46.000Z","publishedDate":"2026-07-30T22:15:46.000Z","archivedAt":"2026-08-06T19:37:20.781Z","archivedDate":"2026-08-06T19:37:20.781Z","viewCount":3,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/1hEmwZs6Eb","content":{"text":"HOME SERVICE BUSINESS DOING $75K+/MONTH!\n\nMost marketing agencies get paid whether your marketing works or not.\nWe decided that wasn't good enough.\n\nThat's why we created the Growth Lead Machine—our proven system for helping established home service companies generate predictable, high-quality booked jobs.\n\nWe're so confident in it that we'll help you generate $10,000 in new booked jobs within 30 days—or we'll continue working for free.\n\nWhy are we willing to make that promise?\n\nBecause we've done it before:\n\n✅ Accurate Leak & Line nearly doubled their monthly revenue in less than four months after implementing our Growth Lead Machine.\n\n✅ Premier Window Tinting increased their internet-generated leads by over 3,000%, creating a consistent pipeline of qualified customers.\n\nWe don't celebrate impressions.\nWe don't brag about clicks.\nWe don't count junk leads.\n\nWe measure success by what actually grows your business:\n- Qualified homeowners\n- ooked jobs\n- Predictable revenue\n- Measurable ROI\n\nIf your home service company is already doing around $1M+ annually and has the capacity to take on more work, this was built for you.\n\nClick below and apply today to see if your company qualifies for our $10,000 Booked Job Guarantee."},"author":{"name":"Billy Scott","handle":"grow_marketing_","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/da3e8abd255ccd80/avatar-2091167dd541.jpg"},"metadata":{"timestamp":"2026-07-30T22:15:46.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/9wm5VUCJH6/media/0.mp4","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/9wm5VUCJH6/media/thumb-0.jpg","type":"video","width":720,"height":1280}],"aiComments":[]},{"id":"t3_1s31sok","shareId":"4FpqCUvgZa","platform":"reddit","url":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocdddwd/","title":"S&P 500 Technical View","shareMode":"full","createdAt":1786044235566,"postedAt":"2026-03-25T05:15:21.113Z","publishedDate":"2026-03-25T05:15:21.113Z","archivedAt":"2026-08-06T19:22:22.868Z","archivedDate":"2026-08-06T19:22:22.868Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/4FpqCUvgZa","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/myTYHVnpwM/media/1.jpg","contentType":"post","content":{"text":"We’re clearly seeing a shift from an uptrend into a short-term downtrend.\n\nStructure: Lower highs + lower lows forming since late Feb bearish structure confirmed\n\nResistance: 6800–6900 zone (previous supply area where price kept rejecting)\n\nSupport: 6500 is being tested right now; below that next key zone is 6400\n\nMomentum:\n\nRecent candles show strong selling pressure (multiple red candles breakdown move)\n\nAny bounce so far looks like a relief rally, not a reversal\n\nTrend Insight:\n\nThe earlier rally (Dec–Feb) has lost momentum\n\nPrice rejected near all-time highs (7000) classic distribution zone\n\nWhat I’m watching:\n\nIf 6500 breaks cleanly continuation towards 6400\n\nIf price holds 6500 and reclaims 6700+possible short-term bounce\n\nNeed a higher low break above 6800 to even think about trend reversal\n\nBias (short-term): Bearish / cautious\n\nMarket context: Geopolitics macro uncertainty = volatility stays high\n\nTL;DR:\n\nTrend flipped bearish. 6500 is key support. Break = more downside, hold = temporary bounce.\n\nRead more"},"author":{"name":"Illustrious_Lie_954","handle":"u/Illustrious_Lie_954","avatar":"https://preview.redd.it/snoovatar/avatars/f92d94d1-e781-4b03-b3f0-029a3099226f-headshot.png?width=64&height=64&crop=smart&auto=webp&s=c5dbaa6736b97a991b0c4b36680f26899883dfc6"},"metadata":{"likes":139,"shares":0,"views":0,"timestamp":"2026-03-25T05:15:21.113Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/myTYHVnpwM/media/0.jpg","r2Url":"https://social-archiver-api.social-archive.org/media/archives/arc/myTYHVnpwM/media/1.jpg","type":"image","width":2179,"height":831},{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/myTYHVnpwM/media/1.jpg","r2Url":"https://social-archiver-api.social-archive.org/media/archives/arc/myTYHVnpwM/media/1.jpg","type":"video","width":2179,"height":831}],"comments":[{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocxyyuy/","content":"No shit","author":{"name":"keepingitreal68","username":"keepingitreal68","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-28T12:16:49.142Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocod0oc/","content":"Not looking good, folks. Gonna head even lower as people realize Tweets mean nothing, and oil and fertilizer shortages send shockwaves…","author":{"name":"NewHope13","username":"NewHope13","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-26T23:28:54.025Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ochp8xi/","content":"This is too technical for the current economic landscape.. enough of a discount with positive news about the war ending.. and all the big institutions will buy the shit out of the market, rendering supply and demand zones and all that stuff worthless. Technical analysis works better on lower time frames in normal conditions.","author":{"name":"Fragtag1","username":"Fragtag1","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-26T00:07:19.728Z","likes":1,"replies":[{"id":"reply-1786044141724","content":"You’re absolutely right man. In this kind of environment TA almost doesn’t even matter anymore. Can you teach me how you trade when the market’s being moved this hard by big money?","author":{"name":"ExcellentSh","username":"ExcellentSh","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-26T16:18:04.125Z","likes":1}]},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/och2eol/","content":"It's cute to think technical analysis still means anything in the market lol the fact that Trump can swing the market either way with a simple post invalidates any real trends or signals.","author":{"name":"HoS_CaptObvious","username":"HoS_CaptObvious","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T22:06:01.862Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocg7qvn/","content":"And bulls over here just talking madddd…..","author":{"name":"parks387","username":"parks387","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T19:38:48.063Z","likes":-1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocfv4dt/","content":"Thank you president Dump","author":{"name":"jazzy095","username":"jazzy095","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T18:39:11.817Z","likes":2},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocfe3fp/","content":"Lmk in 10 years","author":{"name":"Stoic_Nut","username":"Stoic_Nut","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T17:20:53.502Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/oceq9wm/","content":"Zero confidence in the US market anymore, and this sentiment is growing world wide.","author":{"name":"PurpleGarbageDonkey","username":"PurpleGarbageDonkey","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T15:31:42.904Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/oce537i/","content":"The S&P 500 is showing a clear shift in price behavior. After a period of consolidation near 7,000, we’re seeing a sharp breakdown below previous support levels. This downward momentum suggests sellers are currently in control as the index tests the 6,550 area.","author":{"name":"Dry_Environment_9631","username":"Dry_Environment_9631","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T13:50:56.918Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/oce26va/","content":"Personally think it is much better to look at the 10-year. Clear trump put at 4.5%","author":{"name":"gamjatang111","username":"gamjatang111","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T13:36:17.683Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocdyfui/","content":"This is helpful for an overall view, but I'm seeing a basket of stocks that are doing well even in this environment. Always remember that it's a market of stocks. We're in a stock picker's market.","author":{"name":"Wide_Air_4702","username":"Wide_Air_4702","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T13:16:51.962Z","likes":4},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocdexi8/","content":"The lion does not care about technicals","author":{"name":"Some_Edge1544","username":"Some_Edge1544","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T11:18:44.217Z","likes":-2},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocdep1b/","content":"We wont drop that simply. We’ll do a bounce attempt for new highs, maybe even blow off on a sugar rush before the era of 7% tbills begins around 2028 The millennials will once again be the ones cleaning up the boomers shit","author":{"name":"vtsandtrooper","username":"vtsandtrooper","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T11:17:02.627Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocdbl2q/","content":"🥭 tweets are the catalyst, everything else is just algorithmic noise.","author":{"name":"Upset-Motor-2602","username":"Upset-Motor-2602","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T10:53:50.973Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocda9qh/","content":"Do people actually trade with just a bar chart like this? It's like a naked chart with barely any information.","author":{"name":"NavyDean","username":"NavyDean","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T10:43:43.563Z","likes":2,"replies":[{"id":"reply-1786044141724","content":"You can tell a lot about crowd psychology from a chart.","author":{"name":"Upset-Motor-2602","username":"Upset-Motor-2602","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T10:55:39.159Z","likes":3}]},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocd8gv5/","content":"this whole chart is supported by AI one misstep or realignemnt of expectations in AI and it will crumble","author":{"name":"Hot_Individual5081","username":"Hot_Individual5081","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T10:29:16.235Z","likes":1},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/ocd4fj5/","content":"We are below 200 day SMA. We did not shock below like with liberation day. Liberation day was caused by a tweet and reversed with a tweet. This is not caused by words. We have real disruptions and actual war. I think we are staying under the 200 day SMA for a while.","author":{"name":"GeX_64_","username":"GeX_64_","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T09:55:21.986Z","likes":30,"replies":[{"id":"reply-1786044141724","content":"But I was told this isn’t a war. And it would be over soon. And it’s already over. And it never started. And no boots on the ground. And, and, and… Man it’s been 2 weeks and these jokes are already tired.","author":{"name":"Metals4J","username":"Metals4J","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T11:53:36.329Z","likes":7}]},{"id":"https://www.reddit.com/r/StockMarket/comments/1s31sok/comment/occwkkw/","content":"This is all worthless. Market is manipulated on a market close market open basis with scammy moves on the weekend.","author":{"name":"C_Pala","username":"C_Pala","url":"https://www.reddit.com/svc/shreddit/comments/r/StockMarket/undefined"},"timestamp":"2026-03-25T08:41:55.615Z","likes":169}],"aiComments":[]},{"id":"t3_1ohplbp","shareId":"qxXJmuGdFp","platform":"reddit","url":"https://www.reddit.com/user/Think_Reporter_8179/comments/1ohplbp/greed_just_broke_17_if_youre_laddering_out_its/","title":"Greed just broke 17 - If you're laddering out, it's time for another 20% (3 of 5 steps)","shareMode":"full","createdAt":1786044025600,"postedAt":"2025-10-27T20:29:16.267Z","publishedDate":"2025-10-27T20:29:16.267Z","archivedAt":"2026-08-06T19:20:22.741Z","archivedDate":"2026-08-06T19:20:22.741Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/qxXJmuGdFp","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/RMxmHcofjw/media/0.webp","contentType":"post","content":{"text":"Hey everyone,\n\nJust a heads up, the Shiller PE has broken through 41 and has deviated by 17.04 from its historical trend. This is the second time in history this has occurred, the only other time being the Dot-Com Bubble of the late 1990's, where Greed went as high as 22.08 in April, 1999.\n\nJust before The Great Depression, it deviated 16.89 before the downturn.\n\nIf you want to be mathematically Fearful while everyone is Greedy, this is about as close as you're going to get.\n\nEnjoy the exuberance."},"author":{"name":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/e404c65d26347c53/avatar-531e9d4eb798.webp"},"metadata":{"likes":5,"shares":0,"views":0,"timestamp":"2025-10-27T20:29:16.267Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/RMxmHcofjw/media/0.webp","type":"image","width":640,"height":591},{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/RMxmHcofjw/media/1.webp","type":"image","width":640,"height":572},{"url":"https://www.reddit.com/gallery/1ohplbp","type":"video","width":640,"height":591}],"comments":[{"id":"https://www.reddit.com/svc/shreddit/comments/r/undefined/undefined","content":"","author":{"name":"[deleted]","username":"[deleted]","url":"https://www.reddit.com/svc/shreddit/comments/r/undefined/undefined"},"timestamp":"2025-10-28T05:20:33.054Z","likes":0,"replies":[{"id":"reply-1786044021687","content":"Better than ever","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","url":"https://www.reddit.com/svc/shreddit/comments/r/undefined/undefined"},"timestamp":"2025-10-28T11:29:41.130Z","likes":1}]},{"id":"https://www.reddit.com/user/Think_Reporter_8179/comments/1ohplbp/comment/nlpm3iw/","content":"Any guesses on what will be the thing that finally drops this market? Bond auction fail, rate hike, poor earnings, black swan?","author":{"name":"Dick_Wiener","username":"Dick_Wiener","url":"https://www.reddit.com/user/Think_Reporter_8179/comments/1ohplbp/comment/npwb3i2/?force-legacy-sct=1"},"timestamp":"2025-10-27T20:36:06.989Z","likes":1,"replies":[{"id":"reply-1786044021687","content":"Lack of incredible earnings by the major tech companies. If their earnings aren't truly incredible, it'll all fall. We're at the point where everyone is expecting to fuck a dime, and if it's \"just a 9 in a bikini\", it will no longer be good enough. My analogies might be dogshit, but I think it gets the point across. Only a 10 will do at this point, or the entire market is going to drop. The AI tech has to sell at levels to make up for the prices people are buying at . So coming back to this after Nvidia earnings what do you think? Theres people who say it’s just going to keep running until there’s competition. I mean shit, China was just caught stealing 100s of Nvidia GPUs by the FBI. If they’re that valuable, and Nvidia is like most of the market, I don’t see how it goes down. Again I have no idea what I’m talking about. Maybe. I think the capital transfer circlejerk has provided enough of a balance sheet pop to give another quarter of runway at least. I don’t think end-user AI revenue is an expectation at this point.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179/comments/1ohplbp/comment/npwb3i2/?force-legacy-sct=1"},"timestamp":"2025-10-27T20:40:35.964Z","likes":1}]}],"aiComments":[]},{"id":"web-kfabkb","shareId":"BEJCODf6Tu","platform":"web","url":"https://hotleadalerts.com/","title":"SaaS Lead Generation Tool: Find Customers on Reddit, LinkedIn & More","shareMode":"full","createdAt":1786043930088,"postedAt":"2026-08-06T19:18:44.216Z","publishedDate":"2026-08-06T19:18:44.216Z","archivedAt":"2026-08-06T19:18:46.286Z","archivedDate":"2026-08-06T19:18:46.286Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/BEJCODf6Tu","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/TO71LECUkv/media/0.png","contentType":"article","content":{"text":"notifications_active Real-Time Lead Generation for SaaS\n\n## SaaS Lead Generation: Find Paying Customers Fast - or Pay Nothing\n\nHotLeadAlerts monitors social media for leads and finds people asking for SaaS solutions like yours across Reddit, X, LinkedIn, and Facebook - so you reach them first, before competitors do.\n\ncheck_circle Free 7-day trial and\n60-Day Money Back Guarantee\n\n![Social listening dashboard showing hot leads from Reddit, Facebook,\nLinkedIn, Twitter, and Instagram in real time](https://hotleadalerts.com/image/catalog/main.jpg)\n\n## Stuck in the zero-customers SaaS club?Here's how lead generation for SaaS companies actually works.\n\nRight now, someone is posting on Reddit, Facebook Groups, LinkedIn, or Twitter/X asking for exactly what your SaaS does - and your competitor is about to answer them first.\nHotLeadAlerts is the **saas lead generation** tool that monitor social media for leads and finds these high-intent posts, filters out the noise, and delivers a **short, clean list of ready-to-buy leads** directly to you.\n\ntarget\n\n##### Describe Your Product & Ideal Buyer\n\nDescribe your product, who you help, and what \"ready to buy\" signals look like (frustrations with competitors, asking for alternatives, seeking specific features - anything). No exact keyword. No broad topic matching. Just your real business goals.\n\nsearch_insights\n\n##### Get a Short List of High-Intent Leads\n\nWe scan conversations across Reddit, LinkedIn, Facebook, Twitter (X), and Instagram - in real time.\nWhile other tools dump every keyword mention, our AI understands context and intent - what people are actually trying to solve.\n**Everything else gets filtered out.**\nYou get a short, clean list of conversations with the highest chance to convert.\n\ntrending_up\n\n##### Gets Smarter Over Time\n\nThe system learns from your feedback. Every time you react to a lead - good or bad - HotLeadAlerts fine-tunes itself to match what a real opportunity looks like for your business.\n\nThe more you use it, the **better the leads get**.\n\n## Try HotLeadAlerts for 2 Months Risk-Free\n\nGive HotLeadAlerts a try for 2 months. If you're not satisfied with the results, we'll give you a full refund. No questions asked.\n\nverified_user\n\nGuaranteed Results\n\n60-Day Money Back\n\n### Built by a Founder for Founders\n\n- Fair pricing - even one sale per month can cover the cost\n- Validate your SaaS idea quickly and cheaply\n\n### Why 2 Months?\n\nHotLeadAlerts gets smarter the more you use it.\n\nI built this tool to solve my own problem. If it doesn't solve yours too, you shouldn't pay for it.\n\nFree 7-day trial + 60-Day Money Back Guarantee\n\n## See the Magic: Conversations Turn Into Customers\n\nThis is exactly how HotLeadAlerts gets customers - real conversations our system discovers that turn into signups.\n\n🔍 Active Keyword ✕\n\nreddit.com \"how\" AND \"get\" AND \"users\"\n\n##### How did you get your first 1,000 users?\n\n6.0\n\nRelevance\n\nreddit.com \"how\" AND \"get\" AND \"users\"\n\n##### How to get first 50 Users?\n\n7.0\n\nRelevance\n\nreddit.com \"how\" AND \"get\" AND \"users\"\n\n##### How do you get visibility/new users for your app?\n\n7.0\n\nRelevance\n\nreddit.com \"how\" AND \"get\" AND \"users\"\n\n##### I turned my personal Excel budget into a SaaS app last week. I have 0 real users and I don't know how to get them.\n\n8.0\n\nRelevance\n\nreddit.com \"how\" AND \"get\" AND \"users\"\n\n##### For those who grew a SaaS with little to no paid marketing - how did you actually get your first users?\n\n6.0\n\nRelevance\n\nreddit.com \"how\" AND \"get\" AND \"users\"\n\n##### Built my first micro-SaaS but I'm a dev with zero marketing skills. How do I get my first 10 users?\n\n7.0\n\nRelevance\n\nFree 7-day trial. Setup takes 2 minutes.\n\n![Fast and affordable lead generation for\nfounders](https://hotleadalerts.com/image/catalog/why1.jpg)\n\n#### Get leads fast and cheap\n\nFounder-friendly pricing - only **$39/mo for 1,000 leads**. No ads, no funnels, no guesswork. Pure **SaaS lead generation** via social listening.\n\n![High-quality filtered social media leads](https://hotleadalerts.com/image/catalog/why2.jpg)\n\n#### Noise-less, high-quality leads\n\nI hate spending hours chasing low-value conversations. **4-level noise filtering** surfaces only opportunities that are worth your time - leads that actually convert.\n\n![Monitor Reddit, LinkedIn, Facebook, Twitter and\nInstagram for leads](https://hotleadalerts.com/image/catalog/why3.jpg)\n\n#### Monitor All 5 Platforms in Real Time\n\nNot just Reddit like everyone else. HotLeadAlerts tracks Reddit, X (Twitter), LinkedIn, Facebook, and Instagram - you reach potential customers **the moment they're looking for a solution**.\n\n## 5 Platforms, 1 Alert Stream\n\nMost **social listening tools** only monitor Reddit or Twitter. HotLeadAlerts monitors **all five** - so you can **find leads on social media** across all five, no matter where your next customer is asking.\n\n###### [Reddit](https://hotleadalerts.com/reddit-leads)\n\nBuyers ask for recommendations every hour\n\n###### [Facebook Groups](https://hotleadalerts.com/facebook-leads)\n\nThe most underrated B2C lead source\n\n###### [LinkedIn](https://hotleadalerts.com/linkedin-leads)\n\nB2B buyers post \"we need a solution\" publicly\n\n###### [Twitter / X](https://hotleadalerts.com/twitter-leads)\n\nCatch buying-intent tweets in real time\n\n## This Is a Lead Tool,Not a PR Tool\n\nMost [**social listening tools**](https://hotleadalerts.com/social-listening-for-leads) are built for PR teams - they track brand mentions, measure reach, and generate reports nobody reads.\n\nHotLeadAlerts is built for one thing: **finding customers who are ready to buy right now.**\n\nIf you want to know who's saying your name - use [Founderalerts](https://founderalerts.com/).\nIf you want to find your next customer - use HotLeadAlerts.\n\n| Feature | Brand Monitoring Tools | HotLeadAlerts |\n| --- | --- | --- |\n| Goal | Track brand mentions | Find buyers ready to convert |\n| Filter quality | Keyword matches | 4-level AI intent filtering |\n| Alert timing | Daily digest | Real time |\n| Platforms | 2–3 platforms | Reddit, Facebook, LinkedIn, Twitter, Instagram |\n| Price | $99–$299/mo | From $39/mo |\n\n## Who Uses Hot Lead Alerts\n\nAnyone who needs customers - not brand reports. Here's who gets the most out of our social media lead generation tool.\n\nrocket_launch\n\n##### SaaS Founders\n\nValidate your idea and get your first paying users before burning runway on ads that don't convert.\n\ncode\n\n##### Indie Hackers\n\nFind customers for your micro-SaaS without spending $1 on ads - just pure buying-intent signals from social media.\n\nbusiness_center\n\n##### Consultants\n\nRespond to prospects asking for your expertise the moment they post - before your competitors even see the thread.\n\nlaptop_mac\n\n##### Freelancers\n\nLand your next project from someone already looking for your skills - no cold email, no LinkedIn spam.\n\ngroups\n\n##### Marketing Agencies\n\nSurface warm B2B leads for your clients across all 5 platforms - and deliver results other agencies can't match.\n\nschool\n\n##### Coaches & Creators\n\nFind people asking questions only you can answer - and turn a helpful reply into a paying client.\n\nFree 7-day trial + 60-Day Money Back Guarantee. No credit card required."},"author":{"name":"Hot Lead Alerts","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/e123a3bd3c6a8f48/avatar-cec5a7df457a.png","bio":"Hot Lead Alerts is the SaaS lead generation tool that monitors Reddit, Facebook Groups, LinkedIn, Twitter/X, and Instagram for buying-intent posts - reach customers before your competitors. 2-month risk-free."},"metadata":{"timestamp":"2026-08-06T19:18:44.216Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/TO71LECUkv/media/0.png","type":"image","width":205,"height":43}],"aiComments":[]},{"id":"web-nwzu00","shareId":"IokXPP05vH","platform":"web","url":"https://customersproof.com/?mlft=261b2438a67be68b534b089e772a4350-57","title":"Replace 7 customer tools with one cockpit","shareMode":"full","createdAt":1786043921981,"postedAt":"2026-08-06T19:18:35.737Z","publishedDate":"2026-08-06T19:18:35.737Z","archivedAt":"2026-08-06T19:18:37.843Z","archivedDate":"2026-08-06T19:18:37.843Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/IokXPP05vH","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/17bumzRSuw/media/0.png","contentType":"article","content":{"text":"## Founders shouldn’t manage customers across 7 different tools.\n\nMost founders start simple, then slowly accumulate tools for support, feedback, roadmap, analytics, and announcements. Each one adds another dashboard, another login, and another place where customer context gets lost. CustomersProof replaces that fragmented workflow with one system.\n\n## See every customer across all your projects - live.\n\nStop checking multiple inboxes, dashboards, and email threads just to stay on top of customer activity. CustomersProof automatically brings support chats, bug reports, feature requests, testimonials, and visitor activity from all your projects into one real-time control center.\n\nWhy founders love this\n\nEverything updates live\n\nNew chats, votes, reviews, and bug reports appear instantly across all projects.\n\nOne inbox for every project\n\nManage multiple SaaS products, client projects, or side businesses from one screen.\n\nNever miss customer signals\n\nSee support requests, feature demand, churn risks, and positive feedback the moment they happen.\n\napp.customersproof.com / projects\n\nLive\n\n![Multi-project dashboard with live activity from all your projects](https://customersproof.com/image/catalog/multi-project-dashboard.jpg)\n\nEverything updates in real time across every project you manage.\n\n## Your all-in-one customer management cockpit.\n\nReplace 5-7 separate SaaS subscriptions. Every widget, page, and popup is 100% customizable to match your brand.\n\n### Collect, manage, and showcase testimonials\n\nEverything you need for social proof in one place. Smart Auto-Collect proactively asks for reviews at strategic moments in the customer journey - boosting collection by dozens of percent vs passive widgets. Bulk-import the reviews you already have from dozens of sources. Then showcase your wall of love with 10 fully customizable widgets anywhere on your site.\n\n- Smart auto-collect at strategic customer-journey moments - toggle on or off any time\n- 10 customizable showcase widgets - masonry wall, carousel, rating badge, video grid, and more\n- Bulk import from dozens of sources - Google, G2, Trustpilot, Capterra, Twitter, Facebook, Instagram, YouTube, ProductHunt, and more, plus CSV\n- Collect via widget, hosted \"wall of love\" you share by URL, or in-app prompt\n\n![Testimonials wall - customer reviews with stars and avatars](https://customersproof.com/image/catalog/landing/testimonials.png)\n\n### Customer support that builds loyalty\n\nHappy customers stay. Customer support isn't about closing tickets fast - it's about making people feel heard, understood, and looked after. CustomersProof gives your customers every way to reach you - a chat bubble on your site, a hosted contact page they can email or share by URL, an embeddable contact form - and gives you one inbox to manage all of it, from a quick chat reply to a multi-step ticket.\n\n- Every channel in one inbox - chat widget, hosted contact page, embeddable form, direct email\n- Reply in live-chat mode for quick answers, or switch to ticketing mode with statuses, priorities, and assignment for complex cases\n- Internal notes hidden from customers - pinned to a customer or a specific ticket, so context never gets lost between handoffs\n- Statuses (open / responded / closed) and priority levels (low / regular / high) to keep your inbox triaged\n\n![Support chat thread with status, importance and internal notes](https://customersproof.com/image/catalog/landing/support.jpg)\n\n### Bug reports\n\nCustomers hate writing detailed bug reports - so most bugs never get reported. CustomersProof gives them a simple report button anywhere you put it (widget, hosted page, or shared URL), and you manage the whole triage queue in your dashboard. No more \"it broke but I don't know what I clicked\" tickets.\n\n- Multiple capture surfaces - embedded widget on any page, hosted bug-report page (share by URL or email), in-app form\n- Customers report bugs with a screenshot and a short description - no technical jargon required\n- Manage statuses (new / in progress / resolved) and add private internal notes for your team\n- 100% customizable look - match your brand colors and design\nSlack alerts firing twice for the same trigger\n\nvia widget · screenshot attached · 11h ago\n\nNew CSV export cuts off long reviews\n\nvia hosted page · reproduced · 2d\n\nIn progress Mobile dashboard hides delete button\n\nvia in-app form · fixed in v2.4 · 5d\n\nResolved\n\n### Feature requests & voting\n\nStop guessing what to build next. Let your customers submit ideas, vote on what matters, and comment to add context - so every sprint ships what people actually want. When a request is ready to ship, one click promotes it straight to your public roadmap. No copy-pasting between tools.\n\n- One-click promote any feature request to your public roadmap - and demote back if priorities change\n- Public board with vote counts, comments, and threaded discussion\n- Multiple capture surfaces - widget on any page, hosted feature page (share by URL), in-app submission\n- 100% customizable look - colors, layout, status labels - match your brand\n\n![Feature requests list with up-vote arrows and status badges](https://customersproof.com/image/catalog/landing/feature.jpg)\n\n### Public roadmap\n\nA public roadmap turns your product into a conversation. Customers see what's coming, vote on priorities, and watch their requests move from *Planned → Building → Shipped*. You have full control over the stages - add, rename, reorder, recolor them to match your process.\n\n- Fully customizable stages - add, edit, remove, reorder columns to fit your workflow\n- Embed on your site OR share a hosted roadmap page by URL - works anywhere\n- Users vote and comment on roadmap items - no separate tool needed\n- 100% customizable design - colors, fonts, stage names - match your brand\n\n![Public roadmap kanban - Review, Planned, In Progress columns](https://customersproof.com/image/catalog/landing/roadmap.jpg)\n\n### Announcements that always arrive\n\nEvery release is a chance to bring customers back - but email tools are expensive and your messages keep landing in spam. CustomersProof delivers announcements directly inside your product, where users actually see them. No newsletter fees, no spam filters, no deliverability anxiety.\n\n- Direct in-app delivery - personal, instant, never sent to spam\n- Cancel your newsletter subscription for product updates - save $20-200/mo on email tooling\n- Multiple formats - banner at the top of the page, modal popup, or in-app message - pick what fits\n- 100% customizable design - colors, fonts, CTAs - match your product UI exactly\nv2.4 - Webhooks are live\n\nSent Spring sale - 20% off basic plans\n\nModal popup · brand-matched design\n\nSent We miss you - here's what's new\n\nIn-app message · no Mailchimp needed\n\nScheduled\n\n### Visitor & conversion tracking\n\nAll your traffic data, conversion funnels, and ROI metrics in one place - sitting next to the customers and conversations those numbers represent. See who's on your site right now, track every conversion goal, and measure ROI on every channel. No cookie banner, GDPR-safe.\n\n- Live visitor view - see who's on your site right now, where they came from, what page they're on\n- Conversion goals + funnels - visitor → signup → paid, with drop-off rate at every step\n- ROI by traffic source - know which channels make money, not just which bring clicks\n- Privacy-friendly - no cookie banner needed, GDPR-safe out of the box\n· 1,486 visitors · 28 conversions (1.9%) · **7 users live now**"},"author":{"name":"CustomersProof","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/fb676022a9d96fa6/avatar-197de1b8a902.png","bio":"Replace 7 customer tools with one cockpit: support, testimonials, bug reports, feature voting, roadmap & analytics. Free 7-day trial, no card needed."},"metadata":{"timestamp":"2026-08-06T19:18:35.737Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/17bumzRSuw/media/0.png","type":"image","width":300,"height":94}],"aiComments":[]},{"id":"web-e2oy1j","shareId":"KjhQ3gzmGx","platform":"web","url":"https://cashcowleads.com/#pricing-section","title":"CashCow Leads — the AI employee that turns Facebook into a lead machine","shareMode":"full","createdAt":1786043877909,"postedAt":"2026-08-06T19:17:50.226Z","publishedDate":"2026-08-06T19:17:50.226Z","archivedAt":"2026-08-06T19:17:52.551Z","archivedDate":"2026-08-06T19:17:52.551Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/KjhQ3gzmGx","contentType":"article","content":{"text":"🔧\n\nPlumbing\n\nleads cost\n\n$10/lead\n\n⚡\n\nElectrical\n\nleads cost\n\n$10/lead\n\n📣\n\nMarketing\n\nleads cost\n\n$10/lead\n\n🏠\n\nRenovation\n\nleads cost\n\n$10/lead\n\n💻\n\nWeb Design\n\nleads cost\n\n$10/lead\n\n🌿\n\nLandscaping\n\nleads cost\n\n$10/lead\n\n🧹\n\nCleaning\n\nleads cost\n\n$10/lead\n\n🔒\n\nSecurity\n\nleads cost\n\n$10/lead\n\n🚗\n\nAuto Repair\n\nleads cost\n\n$10/lead\n\n🐾\n\nPet Services\n\nleads cost\n\n$10/lead\n\n📸\n\nPhotography\n\nleads cost\n\n$10/lead\n\n🍽️\n\nCatering\n\nleads cost\n\n$10/lead\n\n📦\n\nMoving\n\nleads cost\n\n$10/lead\n\n🖥️\n\nIT Support\n\nleads cost\n\n$10/lead\n\n📊\n\nAccounting\n\nleads cost\n\n$10/lead\n\n❄️\n\nHVAC\n\nleads cost\n\n$10/lead\n\n⚖️\n\nLegal Services\n\nleads cost\n\n$10/lead\n\n🐜\n\nPest Control\n\nleads cost\n\n$10/lead\n\n🏘️\n\nRoofing & Siding\n\nleads cost\n\n$10/lead\n\n🎨\n\nPainting & Decorating\n\nleads cost\n\n$10/lead\n\n🎉\n\nEvent Planning\n\nleads cost\n\n$10/lead\n\n💪\n\nFitness & Training\n\nleads cost\n\n$10/lead\n\n💇\n\nHair & Beauty\n\nleads cost\n\n$10/lead\n\n🧠\n\nTherapy & Counseling\n\nleads cost\n\n$10/lead\n\n📚\n\nTutoring & Education\n\nleads cost\n\n$10/lead\n\n🛋️\n\nInterior Design\n\nleads cost\n\n$10/lead\n\n🗑️\n\nJunk Removal\n\nleads cost\n\n$10/lead\n\n💡\n\nSmart Home\n\nleads cost\n\n$10/lead\n\n🔑\n\nLocksmith\n\nleads cost\n\n$10/lead\n\n🚚\n\nCourier & Delivery\n\nleads cost\n\n$10/lead\n\nDon't want to wait? Buy leads on the spot.\n\nBrowse a live pool of leads across **every niche we cover** — filter by category and location, and buy only the ones you want. Leads start at just **$2.50** and never cost more than **$10.00**.\n\n🛒 Marketplace\n\nBrowse and buy leads by category and location\n\n💰 Balance: **$339.00**\n\nAll\n\nPlumbing\n\nElectrical\n\nMarketing\n\nRenovation\n\nWeb Design\n\nLandscaping\n\nCleaning\n\nSecurity\n\nAuto Repair\n\nPet Services\n\nPhotography\n\nCatering\n\nMoving\n\nIT Support\n\nAccounting\n\nHVAC (Heating & Cooling)\n\nLegal Services\n\nPest Control\n\nRoofing & Siding\n\nPainting & Decorating\n\nEvent Planning\n\nFitness & Personal Training\n\nHair & Beauty Services\n\nTherapy & Counseling\n\nTutoring & Education\n\nInterior Design\n\nJunk Removal & Hauling\n\nSmart Home & Automation\n\nLocksmith Services\n\nCourier & Delivery Services\n\n422 leads found\n\nLandscapingWarm Lead\n\nLooking for yard work or landscaping opportunities.\n\n$2.50\n\nPosted todayExpires in 30 days\n\n[Purchase Lead](https://cashcowleads.com/signup)\nLandscapingCold Lead\n\nLooking for advice on maintaining new Bermuda grass.\n\n📍 Georgia\n\n$10.00\n\nPosted todayExpires in 30 days\n\n[Purchase Lead](https://cashcowleads.com/signup)\nLandscapingHot Lead\n\nNeed tilling and leveling before sod installation.\n\n📍 Atlanta, GA\n\n$2.50\n\nPosted todayExpires in 30 days\n\n[Purchase Lead](https://cashcowleads.com/signup)\nMarketingHot Lead\n\nNeed an agency to run Google Ads — $2k/mo budget.\n\n📍 Dallas, TX\n\n$10.00\n\nPosted todayExpires in 30 days\n\n[Purchase Lead](https://cashcowleads.com/signup)\nElectricalWarm Lead\n\nBreaker tripping nightly — need an evening electrician.\n\n📍 Phoenix, AZ\n\n$7.50\n\nPosted todayExpires in 30 days\n\n[Purchase Lead](https://cashcowleads.com/signup)\nWeb DesignHot Lead\n\nFull site rebuild needed in 4 weeks, budget approved.\n\n📍 Atlanta, GA\n\n$10.00\n\nPosted todayExpires in 30 days\n\n[Purchase Lead](https://cashcowleads.com/signup)\nEvery lead priced between $2.50 and $10.00 · Pay per lead · No monthly fee\n\nReal people. Real requests. Caught in real time.\n\nThis is what your AI employee pulls out of local Facebook groups every single day — people asking, in their own words, for exactly what you sell.\n\n👩\n\nDanielle M.\n\n📍 Austin Homeowners · 12.4k members\n\n“Need a **good local plumber** ASAP — kitchen sink has been leaking for 3 days. Happy to pay for same-day service!”\n\n🔧 Plumbing⚡ Urgent💰 Ready to Pay\n\n23 min ago\n\n🧔\n\nRay T.\n\n📍 Dallas Biz Network · 5.8k members\n\n“Anyone know a solid **marketing agency**? We need someone to run our Google Ads. Budget is around $2k/mo, ongoing.”\n\n📢 Marketing💰 High Budget🔁 Retainer\n\n41 min ago\n\n👵\n\nMarlene O.\n\n📍 Phoenix Neighbors · 22.1k members\n\n“My breaker keeps tripping every night and I'm honestly nervous about it. Can someone recommend an **electrician** who does evenings?”\n\n⚡ Electrical🚨 Emergency📍 Local\n\n1 hr ago\n\n👨💼\n\nChris B.\n\n📍 Atlanta Startup Hub · 9.3k members\n\n“Looking for a **web designer** to rebuild our site. Need it done in 4 weeks, we have budget approved already.”\n\n💻 Web Design💰 Budget Approved⏳ Deadline\n\n1 hr ago\n\n👩🦰\n\nSofia R.\n\n📍 Tampa Moms Group · 31.7k members\n\n“Who do y'all use for **lawn care**? Ours is out of control and I need someone reliable every two weeks.”\n\n🌳 Landscaping🔁 Recurring⭐ Referral Ask\n\n2 hrs ago\n\n🧔\n\nDevon W.\n\n📍 Denver Home Reno · 7.2k members\n\n“Just closed on a fixer-upper. Need a **general contractor** for a full kitchen gut. Serious quotes only please.”\n\n🏠 Renovation💰 Big Ticket📍 Local\n\n3 hrs ago\n\n👩💻\n\nNina P.\n\n📍 Seattle Small Biz · 4.6k members\n\n“Our office needs a **weekly cleaning crew** starting next month. Anyone have someone they'd vouch for?”\n\n🧺 Cleaning🔁 Recurring📅 Starts Soon\n\n4 hrs ago\n\n🧓\n\nWalter H.\n\n📍 Nashville Neighbors · 18.9k members\n\n“AC died in the middle of this heat wave. Need an **HVAC tech** out here today — I'll pay the emergency rate, no problem.”\n\n❄️ HVAC🚨 Emergency💰 Ready to Pay\n\n5 hrs ago\n\n👩🏫\n\nAmara J.\n\n📍 Charlotte Parents · 26.3k members\n\n“My son is struggling with algebra and finals are coming. Looking for a **math tutor** — twice a week, in person preferred.”\n\n📚 Tutoring🔁 Recurring⏳ Deadline\n\n6 hrs ago\n\nThree steps. Zero effort on your end.\n\nSet it up in two minutes. After that, your AI employee does everything.\n\n1\n\n📋\n\nTell us your city & services\n\nPick your metro areas and service categories. Takes two minutes and it's the last time you'll need to do anything.\n\n2\n\n🤖\n\nAI scans Facebook groups 24/7\n\nOur AI monitors hundreds of local groups, reading every post around the clock for people who need what you offer.\n\n3\n\n📧\n\nWe email you the lead\n\nWithin 24 hours — the original post, the poster's name, location, and a direct link to respond first.\n\nIf locals ask for it on Facebook, we find it.\n\nFrom urgent plumbing calls to month-long marketing retainers — our AI catches every professional service request in your area.\n\n🔧Plumbing\n\n⚡Electrical\n\n📢Marketing\n\n🏠Renovation\n\n💻Web Design\n\n🌳Landscaping\n\n🧺Cleaning\n\n🔒Security\n\n🚗Auto Repair\n\n🐕Pet Services\n\n📷Photography\n\n🍰Catering\n\n📦Moving\n\n🖥️IT Support\n\n⚖️Legal\n\n💰Accounting\n\n🏋️Personal Training\n\n✂️Hair & Beauty\n\n📚Tutoring\n\n＋Many more\n\nReal businesses. Real jobs closed.\n\n★★★★★ **4.9** out of 5 · based on 1,840+ reviews\n\n★★★★★\n\n“I got a $2,400 plumbing job from a lead on my first day. The woman posted in a neighborhood group and I was the first plumber to message her.”\n\n🔧\n\nMarcus D.\n\nPlumber · Houston, TX\n\n★★★★★\n\n“CashCow Leads replaced cold outreach entirely. We get warm inbound leads every single day — people who are actively looking.”\n\n📢\n\nPriya K.\n\nMarketing Agency · Atlanta, GA\n\n★★★★★\n\n“Someone posted at 11pm asking for an electrician — I got the alert at 11:04, and by morning had a $5,800 signed contract.”\n\n⚡\n\nTyler R.\n\nElectrician · Phoenix, AZ\n\n★★★★★\n\n“The marketplace is the sleeper feature. I grab three or four leads for a couple bucks each on slow weeks and it fills my whole calendar.”\n\n🌳\n\nJamal W.\n\nLandscaper · Tampa, FL\n\n★★★★★\n\n“I used to spend two hours a night scrolling groups myself. Now it just shows up in my inbox. That time back is worth more than the lead cost.”\n\n🏠\n\nRebecca L.\n\nGeneral Contractor · Denver, CO\n\n★★★★★\n\n“Not every lead closes — that's just sales. But at $10 I only need one job a month to be way ahead. We're booked out six weeks now.”\n\n🧺\n\nOwen S.\n\nCleaning Company · Seattle, WA\n\nThe simplest pricing in the industry.\n\nNo subscriptions. No monthly bill. Only charged when we deliver a real lead.\n\n$ 10 /lead\n\nOnly charged when a lead is delivered\n\n**Pay as you go.**Upload your card, turn your campaign on, and we'll email leads straight to you.\n\nEstimate your monthly spend **20 leads / mo**\n\n150100150200+\n\n**20** leads / mo\n\n**$200** est. monthly spend\n\n**$10** per lead\n\nA single closed job typically covers **40+** leads — most customers are profitable from week one.\n\n✓ Unlimited cities monitored\n\n✓ Unlimited service categories\n\n✓ Email alert within 24 hours\n\n✓ Original post forwarded\n\n✓ Poster name & location included\n\n✓ Direct link to respond instantly\n\n✓ Lead quality scoring included\n\n✓ Marketplace access included\n\n✓ Turn campaigns on/off anytime\n\n✓ No contracts, no minimums"},"author":{"name":"cashcowleads.com"},"metadata":{"timestamp":"2026-08-06T19:17:50.226Z"},"media":[],"aiComments":[]},{"id":"web-xdnxnb","shareId":"9GZJtSo5E9","platform":"web","url":"https://trendwatchers.co/#twlp-pricing","title":"Trend Watchers - Go Viral With Trends","shareMode":"full","createdAt":1786043871169,"postedAt":"2023-05-02T05:44:24.000Z","publishedDate":"2023-05-02T05:44:24.000Z","archivedAt":"2026-08-06T19:17:45.066Z","archivedDate":"2026-08-06T19:17:45.066Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/9GZJtSo5E9","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/oV7KRMfUHA/media/0.jpg","contentType":"article","content":{"text":"100+\n\nVideos per batch\n\nGenerate a full month of content in one session\n\n<10 min\n\nRaw footage to viral\n\nFrom upload to 100 finished videos in minutes\n\n30+\n\nTemplate categories\n\nEvery niche covered, new templates added weekly\n\n3\n\nPlatforms supported\n\nTikTok, Instagram Reels & YouTube Shorts\n\n0\n\nEditing skills needed\n\nAI does all the work — just upload and download\n\n## Three steps.One month of content.\n\nStep 01\n\nUpload your footage\n\nDrop any raw B-roll clips into Trend Watchers. No prep needed — unedited footage works perfectly.\n\n⚡ Under 60 seconds\n\nStep 02\n\nAI applies templates\n\nOur AI instantly matches your clips to trending templates from our 30+ category marketplace, automatically.\n\n⚡ 90 seconds to process\n\nStep 03\n\nDownload & go viral\n\nDownload up to 100 finished, platform-optimized videos ready for TikTok, Instagram, and YouTube.\n\n⚡ 100 videos at once\n\n## Every niche. Every trend. 30+ categories.\n\nNew viral templates added weekly — engineered to stop the scroll in every niche.\n\n## Built for creatorswho move fast.\n\n🍕\n\n### Food & Restaurant\n\nTurn food footage into high-converting shorts that drive foot traffic, orders, and reservations.\n\n✈\n\n### Travel Creators\n\nTransform destination clips into viral travel shorts with templates optimized for what people search.\n\n💪\n\n### Fitness Creators\n\nEdit workout footage into shorts that explode across TikTok, Reels, and YouTube Shorts at once.\n\n🏪\n\n### Local Businesses\n\nBecome the go-to destination in your area with professional content that outpaces every competitor.\n\n🎯\n\n### Personal Brands\n\nTurn thought leadership footage into high-performing shorts that build authority across every platform.\n\n🛒\n\n### Lifestyle Creators\n\nGenerate months of viral lifestyle content in one session — the consistent schedule that actually ranks.\n\n## Real creators.Real results.\n\n★★★★★\n\nI made 87 videos in one afternoon. My content schedule is fully covered for the next 6 weeks. This changed everything for my restaurant's TikTok.\n\nMK\n\nMarcus K.\n\nFood Creator · 142K followers\n\n★★★★★\n\nThe food template category alone got my client 3 million views in a week. I now run 12 accounts and Trend Watchers is the backbone of all of them.\n\n★★★★★\n\nWent from posting twice a week to 14 times across 3 platforms. My gym's TikTok hit 50K followers in 30 days using the fitness templates.\n\nDJ\n\nDarius J.\n\nFitness Creator · 51K followers\n\n★★★★★\n\nI was spending 20 hours a week editing videos for my boutique. Now I spend 20 minutes and get 60+ pieces of content. The ROI is completely insane.\n\nSR\n\nSofia R.\n\nFashion Creator · 89K followers\n\n★★★★★\n\nOur travel agency went from invisible to 200K views a month on TikTok. The travel templates are dialed in — they hook people in the first second every time.\n\nTN\n\nTyler N.\n\nTravel Creator · 203K followers\n\n★★★★★\n\nI manage content for 8 local businesses. Trend Watchers cut my production time by 90%. Every single client has seen a massive jump in reach since we started.\n\nJM\n\nJordan M.\n\nContent Agency Owner\n\n## Simple pricing.Serious output.\n\n1 credit = 1 video of any size & length\n\nor buy extra credits anytime\n\n⚡ Buy Extra Credits\n\nNo subscription needed · Valid 1 year · 1 credit = 1 video\n\n$0.60\n\nper video\n\n🕐 Time you'll save\n\n31h 40m\n\n🌿 Editing cost saved\n\n$1,400\n\nCredits100 videos\n\nPer video$0.60\n\nTotal charge$60.00\n\n🔒 Charged immediately · Secure checkout via Stripe · Credits valid for 1 year"},"author":{"name":"Trend Watchers","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/7e841e30f636fde2/avatar-444d23b03132.png","bio":"The #1 Bulk AI Editor for Viral Short-Form Content. Built for Lifestyle, Travel, Fitness, and Food Creators. Drop raw footage into viral-ready templates"},"metadata":{"timestamp":"2023-05-02T05:44:24.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/oV7KRMfUHA/media/0.jpg","type":"image","width":1170,"height":1335}],"aiComments":[]},{"id":"1jrgqns","shareId":"eTjMc8zit9","platform":"reddit","url":"https://www.reddit.com/r/u_Think_Reporter_8179/comments/1jrgqns/whitepaper_the_greedfear_residual_strategy/","title":"Whitepaper: The Greed/Fear Residual Strategy","shareMode":"full","createdAt":1786043796721,"postedAt":"2025-04-04T16:54:42.000Z","publishedDate":"2025-04-04T16:54:42.000Z","archivedAt":"2026-08-06T19:16:28.969Z","archivedDate":"2026-08-06T19:16:28.969Z","viewCount":3,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/eTjMc8zit9","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/ZcHnfIUQ8X/00-image.jpg","contentType":"article","content":{"text":"# The Greed/Fear Residual Strategy\n\nA Quantitative Model for Timing Market Extremes, by Think\\_Reporter\\_8179\n\n[Raw data here ](https://docs.google.com/spreadsheets/d/1asVTn8TUOWKwli_w7n8r6-CNZQv0vzhgcJPuTF1tHBU/edit?usp=sharing)\n\n# Overview\n\nThis strategy builds on the foundational work of economist Robert Shiller and his widely recognized Cyclically-Adjusted Price-to-Earnings ratio (CAPE) also known as Shiller PE. The Shiller PE smooths out earnings over a 10-year period to adjust for economic cycles, offering a more stable signal of market valuation than traditional P/E ratios.\n\nResidual Concept:\n\nWhile the Shiller PE is a powerful tool, it exhibits a general upward trend over time due to long-term economic growth, inflation, and shifts in market structure. To adjust for this and extract meaningful overvaluation signals, a linear regression was applied to the full historical Shiller PE dataset (1871–present). This regression line represents the expected value of the Shiller PE based on its historical trajectory. The “Greed/Fear” value is the deviation above and below this trend. (Figure1, bottom graph)\n\n# Variants\n\nThe 20/4 variant - This optimized strategy exits the market when the Greed/Fear Number exceeds 20 and re-enters when it drops below 4. It represents extremely rare euphoria and deep pessimism. Despite only triggering two exits historically, it consistently outperforms Buy & Hold from 1940 onward. CAGR since 1980 is 9.67% compared to 9.11% for Buy & Hold.\n\nThe 15/4 variant - This variant exits the market more frequently—whenever the Greed/Fear Number exceeds 15 and re-enters below 4. It also consistently outperforms Buy & Hold across the modern market era. CAGR since 1980 is 9.61%, with 7.73% since 1940, making it a strong alternative for more active positioning. (Figure 2, images 1 & 2)\n\nWhile Buy & Hold remains robust, these Greed/Fear threshold strategies demonstrate that valuation-based sentiment timing can improve long-term returns. The 20/4 model is best for minimalist investors. The 15/4 model balances performance and responsiveness. Both outperform Buy & Hold from 1940 and 1980 onward.\n\n**Procedure**\n\nLet PE(t) be the Shiller PE at time t. Let L(t) = a + bt be the best-fit linear regression of PE(t) over time. I define the residual R(t) = PE(t) - L(t). This residual represents the deviation from the trend—a proxy for market sentiment.\n\nInterpreting Residuals:\n\n\\- When R(t) >> 0: the market is overvalued relative to trend (potential euphoria or bubble).\n\n\\- When R(t) << 0: the market is undervalued (panic or capitulation).\n\n\\- When R(t) \\~ 0: the market is roughly in equilibrium with its long-term valuation.\n\n**Strategy**\n\nUsing empirical analysis, I observed that high positive residuals (e.g., R > 15 or R > 20) often precede sharp market declines, typically within 3–6 months. I also found that low residuals (e.g., R < 4) correlate with market bottoms and recovery periods. The strategy is to:\n\n1. Exit the market when R(t) > X (e.g., X = 15 or 20).\n2. Re-enter the market when R(t) < Y (e.g., Y = 4).\n\nBack testing and Results:\n\nAcross historical time frames beginning in 1900, 1940, 1970, 1980, and 1990, the 20/4 and 15/4 strategies consistently outperformed Buy & Hold. For example, from 1980 onward (Figure 2, image 3):\n\n\\- Buy & Hold CAGR: 9.11%\n\n\\- 20/4 Strategy CAGR: 9.67%\n\n\\- 15/4 Strategy CAGR: 9.61%\n\n\\- Great Depression modeling was done as well from 1900 - 1940 (Figure 3)\n\nWhy It Works:\n\nThe strategy is effective because it systematically avoids periods of extreme overvaluation while reinvesting during pessimistic lows. It does not require forecasting future earnings or macro conditions—only a valuation signal derived from historical behavior. The approach is both statistically grounded and operationally simple, requiring monitoring of a single residual metric derived from the Shiller PE.\n\n**Conclusion**\n\nThis model represents a practical synthesis of valuation theory and behavioral finance. By using a regression-adjusted Shiller PE residual, I normalize for historical bias and extract actionable sentiment. The resulting strategy is robust, transparent, and repeatable—making it a compelling alternative to traditional Buy & Hold for long-term investors.\n\nFigure 1:\n\n[Top graph is \\\\\"Greed\\/Fear\\\\\" residual of Shiller PE, including the \\\\\"15\\/4 Strategy\\\\\" delineations. Bottom graph is the Shiller PE historical data with its trend. The \\\\\"Greed\\/Fear\\\\\" value is derived from this.](https://preview.redd.it/iwkfrotxdvse1.png?width=3343&format=png&auto=webp&s=15b0d9c9692a51f8264387b581fdb1eb4590f410)\n\n[Figure 2](https://www.reddit.com/user/Think_Reporter_8179/comments/1jlee7w/greedfear_154_strategy_back_tested_analysis_it/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)\n\n[Figure 3](https://www.reddit.com/user/Think_Reporter_8179/comments/1jo4ldy/greedfear_154_strategy_backtested_during_the/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)"},"author":{"name":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","avatar":"https://styles.redditmedia.com/t5_4rlgod/styles/profileIcon_4convnvkxrjd1.jpeg"},"metadata":{"likes":25,"shares":0,"views":0,"timestamp":"2025-04-04T16:54:42.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/ZcHnfIUQ8X/00-image.jpg","r2Url":"https://social-archiver-api.social-archive.org/media/archives/arc/ZcHnfIUQ8X/00-image.jpg","type":"image","width":1200,"height":630}],"comments":[{"id":"mlyfwpc","content":"couple of questions:\n\n1. what occurs, for example, when one sells out when above 15, but greed/fear does not go below 4 to trigger buy back? do you hold cash in anticipation of a further fall?\n\n2. is this tracked using the S&P 500 or the DOW?","author":{"name":"oscarony","username":"oscarony","handle":"u/oscarony","url":"https://www.reddit.com/user/oscarony"},"timestamp":"2025-04-08T00:36:24.000Z","likes":2,"replies":[{"id":"mlz6l7h","content":"1. It is inevitable it will drop below 4 at some point. Even waiting the longest recorded time of 4.25 years during the Dot-Com bubble, pulling out at 15, reinvesting that withdrawn amount back at 4 GF, 4.25 years later, still resulted in a larger CAGR versus B&H. That said, the Dot-Com bubble was a wild exception of millennium euphoria (also noted in a smaller capacity in 1899, by the way) that lasted 49 months. The others have been 10, 17 and 12 months. \n\n\n2. The S&P 500.\n\nSimply put, the bigger the bubble, the harder the fall. If one does not pull out at 15 or higher, they will ultimately not have as much as a person that pulls out, even if early, and waits for the cheap time.\n\nThis also appears to be what Warren Buffet has done and is doing.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-08T03:23:24.000Z","likes":2,"replies":[{"id":"mm3mlqz","content":"thank you for the explanation. next two questions \n\n1. seems like shorting the market or buying long-dated puts on overvalued, high beta stocks would be really profitable once G/F hits 15, is that what you’re currently doing? and basically planning to flip from short to long when G/F hits 4?\n\n2. is the number to hop back in (G/F = 4) SPY at 480?","author":{"name":"oscarony","username":"oscarony","handle":"u/oscarony","url":"https://www.reddit.com/user/oscarony"},"timestamp":"2025-04-08T20:51:54.000Z","likes":1,"replies":[{"id":"mm3w83g","content":"1. That could be a lucrative strategy yes, but it's not what I'm doing. I pulled out and have held onto a large chunk of cash. I'm more a Warren Buffett slow-burn type investor, but theoretically it would likely turn out well if someone did this anytime the market touched 15+ G/F. I started re-entering the market two days ago in small chunks. I added more today, and will continue at each whole G/F number until we hit ~$4500 (which is right around G/F 4, to answer your second question).\n\n2. Definitely not 480. Lol. If you meant 4800, it's more around $4500. See the chart on the right side of the raw data.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-08T21:40:17.000Z","likes":1}]}]}]},{"id":"n3lbevd","content":"does this work for single equities using diluted EPS and CPI data? assuming no negative earnings…","author":{"name":"liquid_bee_3","username":"liquid_bee_3","handle":"u/liquid_bee_3","url":"https://www.reddit.com/user/liquid_bee_3"},"timestamp":"2025-07-17T07:37:41.000Z","likes":2,"replies":[{"id":"n3m3i5l","content":"No. It's based on the Shiller PE which is a behavioral measure of irrational exuberance in the market as a whole. It cannot be used to predict how humans will behave with a single equity, only how they behave with the stock market in general.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-07-17T11:40:41.000Z","likes":2}]},{"id":"oi7t923","content":"I just found this post and I'm finding it very useful to get a perspective of where market valuations are at, especially going back through the decades. The raw excel file is very nice and it seems like it is still being updated. Is this being done by hand or do you have something setup to pull the numbers automatically?","author":{"name":"much_snark_very_wow","username":"much_snark_very_wow","handle":"u/much_snark_very_wow","url":"https://www.reddit.com/user/much_snark_very_wow"},"timestamp":"2026-04-25T15:47:57.000Z","likes":2,"replies":[{"id":"oialmtr","content":"By hand","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2026-04-26T00:33:12.000Z","likes":2,"replies":[{"id":"oiate3j","content":"You are the MVP. What's shocking to me is how many \"green\" periods we had that ran for decades. Now it's just all red.","author":{"name":"much_snark_very_wow","username":"much_snark_very_wow","handle":"u/much_snark_very_wow","url":"https://www.reddit.com/user/much_snark_very_wow"},"timestamp":"2026-04-26T01:20:51.000Z","likes":1}]}]},{"id":"mnkciov","content":"Interesting.\n\nWhile I agree with the importance of the Schiller PE, using a linear regression starting in 1871 will input in your investment strategy a period / economic framework that is not relevant to today's world. Maybe use another non-linear regression method that put a stronger emphasis on the last 20 or 30 years. \n\nAlso, and even if backtesting your methodology yields positive returns, a methodology where there are entire decades where you cannot invest is not practical from a human lifespan practice. Still interesting as a theoretical model though.","author":{"name":"[deleted]","username":"[deleted]"},"timestamp":"2025-04-17T11:07:52.000Z","likes":1,"replies":[{"id":"mnkjhcq","content":"This method enumerates human behavior, so the entire data set matters in that regard. Consider that the Shiller PE by itself is a measure of irrational exuberance. My model exposes a gradual increase in people's willingness to pay more over time and then compares the current prices to this ever increasing risk tolerance. Why are people willing to take on more and more risk? That is uncertain. It could be that loans are far easier to get now than in the 1800/1900's. It could be changes in laws on how companies have to report earnings, etc. Nevertheless, there is a gradual increase in human behavioral risk tolerance in investing, and this model considers this behavioral risk and then compares current trends to this ever-growing tolerance. So it is a behavioral model first and foremost. \n\nAlso, there are no periods where decades pass where one wouldn't invest. Consider withdrawing the invested account when GF hits 15+ and using excess cash to pay down debts until GF drops below 15, and then DCA with salary as usual. However do not dump the withdrawn amount back into the market until GF hits 4 or less. This DCA's when the market is below 15, while also preserving bulk growth once the market has grown irrational. The best of both worlds. 🙂","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-17T11:58:26.000Z","likes":1,"replies":[{"id":"mnkt3dw","content":"\\> Why are people willing to take on more and more risk? That is uncertain.\n\nI think the structurally higher PE does somewhat reflect more appetite for risk, but also more and more money in the system.","author":{"name":"[deleted]","username":"[deleted]"},"timestamp":"2025-04-17T12:59:06.000Z","likes":1,"replies":[{"id":"mnl8125","content":"Well, P/E is a ratio. Thus the expanding ratio takes into account more money in the system. People are objectively willing to pay more comparatively to earnings. The inflation is included in this ratio.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-17T14:20:33.000Z","likes":1}]}]}]},{"id":"mpzek85","content":"I'm not sure if you've read lifecycle investing, but they have an equation that helps you decide how much leverage you should put into the S&P500. One of the equations that they recommend asks that you input the Shiller PE ratio. Given how historically high the Shiller PE ratio has been, the equation has essentially recommended leveraging and even buying 0% S&P500 for the past 10 years or so. \n\n\nIf you have read the book or at least heard of the idea, I'm wondering if you've come across an equation that would replace the Shiller PE ratio as there is supposedly positive drift overtime? \n\nI'm not really looking for signals to sell but looking for opportunities to buy and/or leverage based on my age, retirement savings, etc.","author":{"name":"tooclouds","username":"tooclouds","handle":"u/tooclouds","url":"https://www.reddit.com/user/tooclouds"},"timestamp":"2025-05-01T07:40:08.000Z","likes":1,"replies":[{"id":"mq055ld","content":"I'm unaware of the book you mention but the hypothetical equation you speak of is possibly the one I've shown here. It's the Residual of the Shiller PE. \n\nThe concept you speak of sounds similar to an idea I've mentioned on Reddit a few times, of something I call a \"Risk Mitigation Ladder\", which is the concept of removing earnings from the market as the Residual gets higher, and increasing as it gets lower. \n\nI haven't crunched any numbers but the concept should be sound. After all, this entire concept is based around the idea that eventually, things are just too expensive for the average investor and when that happens a bear market occurs.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-05-01T11:53:12.000Z","likes":1}]},{"id":"mm1v1ns","content":"You going to post again when the residual drops below 4?","author":{"name":"hop_along_quixote","username":"hop_along_quixote","handle":"u/hop_along_quixote","url":"https://www.reddit.com/user/hop_along_quixote"},"timestamp":"2025-04-08T15:45:10.000Z","likes":2,"replies":[{"id":"mm22dgb","content":"Yep. And probably here and there between, as the market bobs and weaves.\n\nYou can also watch the raw data sheet itself to see what the days Greed/Fear is at EoD. I update it almost daily.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-08T16:21:33.000Z","likes":1}]},{"id":"n8n5eom","content":"With the Greed/Fear Value being only .02 away from 15 what are your current thoughts?","author":{"name":"DesignerMedium8211","username":"DesignerMedium8211","handle":"u/DesignerMedium8211","url":"https://www.reddit.com/user/DesignerMedium8211"},"timestamp":"2025-08-14T12:48:12.000Z","likes":1,"replies":[{"id":"n8n7wes","content":"[https://www.reddit.com/user/Think\\_Reporter\\_8179/comments/1m960u5/if\\_the\\_market\\_holds\\_today\\_well\\_have\\_breached\\_15/?utm\\_source=share&utm\\_medium=web3x&utm\\_name=web3xcss&utm\\_term=1&utm\\_content=share\\_button](https://www.reddit.com/user/Think_Reporter_8179/comments/1m960u5/if_the_market_holds_today_well_have_breached_15/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-08-14T13:02:34.000Z","likes":1}]},{"id":"mlekyrw","content":"I grew tired of trying to point people all over old posts and historical comments I've made throughout Reddit, so I finally decided to lay out how this all works in a single \"paper\". Enjoy","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-04T17:11:50.000Z","likes":4,"replies":[{"id":"mlfhmkj","content":"Nice work, so buy and hold still outperforms, but if you want to get a little extra umph pile on when its low , makes sense. Sadly the shiller PE is still in the 30s even after all this carnage. I wonder if you could create a tool that flashes red green or yellow depending on if it is time to buy or not based on the signals.","author":{"name":"InterestSharp3835","username":"InterestSharp3835","handle":"u/InterestSharp3835","url":"https://www.reddit.com/user/InterestSharp3835"},"timestamp":"2025-04-04T19:57:37.000Z","likes":1,"replies":[{"id":"mlfk957","content":"Buy & Hold does not outperform. The 20/4 and 15/4 strategies beat it.\n\n\\- Buy & Hold CAGR: 9.11%\n\n\\- 20/4 Strategy CAGR: 9.67%\n\n\\- 15/4 Strategy CAGR: 9.61%","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-04T20:11:00.000Z","likes":2,"replies":[{"id":"mlfl518","content":"Seems like a lot of risk and effort for half a percent point. In my mind the premium over buy and hold has to be higher for me to take on the risk , especially more than 500 basis points.","author":{"name":"InterestSharp3835","username":"InterestSharp3835","handle":"u/InterestSharp3835","url":"https://www.reddit.com/user/InterestSharp3835"},"timestamp":"2025-04-04T20:15:30.000Z","likes":1,"replies":[{"id":"mlflosk","content":"That's fair. This is a new discovery and made more for large hedge fund strategy or long-term holding signals. If Greed/Fear hits 15+, sell out, wait until it hits 4, then dump back in. You win over the long term. As far as I know, nothing else beats B&H with such minimum effort. \nYou said effort, but really this is a simple calculation. Perform a linear regression on the Shiller PE, then subtract the current Shiller PE from the trend and you're done.\n\nI think the real value here is that whenever the market touches 15+ Greed/Fear, there is a bear market within 3-6 months. The quant folks could probably do a lot with that awareness.","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-04T20:18:18.000Z","likes":2,"replies":[{"id":"mljnqhk","content":"Would be curious to see how much it beat buy/hold since the 90s, or even since we fully came off the gold standard.","author":{"name":"Dick_Wiener","username":"Dick_Wiener","handle":"u/Dick_Wiener","url":"https://www.reddit.com/user/Dick_Wiener"},"timestamp":"2025-04-05T14:36:35.000Z","likes":1,"replies":[{"id":"mljnvdu","content":"It would be the same, since 15 has only shown up a handful of times since 1871, the most recent being last December on the 6th!","author":{"name":"Think_Reporter_8179","username":"Think_Reporter_8179","handle":"u/Think_Reporter_8179","url":"https://www.reddit.com/user/Think_Reporter_8179"},"timestamp":"2025-04-05T14:37:22.000Z","likes":1}]}]}]}]}]}]},{"id":"oggvjkv","content":"Not even fully sure how I stumbled upon your post but this is much appreciated info. Sincerely thank you. ","author":{"name":"Notable_Naps","username":"Notable_Naps","handle":"u/Notable_Naps","url":"https://www.reddit.com/user/Notable_Naps"},"timestamp":"2026-04-16T05:28:00.000Z","likes":2},{"id":"op3rad3","content":" I wonder if going right now as of June 26 on inverse Nasdaq etf is a good move. Summer months tend to average weaker returns. Also maybe trend might be negative until midterms. The AI narrative is also shifting a bit. Not sure…","author":{"name":"Voxick95","username":"Voxick95","handle":"u/Voxick95","url":"https://www.reddit.com/user/Voxick95"},"timestamp":"2026-06-01T10:47:54.000Z","likes":1}],"aiComments":[]},{"id":"web-je080n","shareId":"NDvG3AHpjc","platform":"web","url":"https://www.leadsie.com/blog/create-facebook-ad-account","title":"How to Create a Facebook Ad Account","shareMode":"full","createdAt":1786033846227,"postedAt":"2026-07-26T00:00:00.000Z","publishedDate":"2026-07-26T00:00:00.000Z","archivedAt":"2026-08-06T16:30:42.876Z","archivedDate":"2026-08-06T16:30:42.876Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/NDvG3AHpjc","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/I19zDBnuaO/media/0.png","contentType":"article","content":{"text":"[Leadsie Blog](https://www.leadsie.com/blog) Article Content\n\nCreate a new Facebook ad account by going to [Meta Business Suite’s Settings](https://business.facebook.com/latest/settings), open ‘ **Accounts** ’, then ‘ **Ad accounts** ’, click ‘ **Add** ’, and choose ‘ **Create a new ad account** ’. Set the name, time zone, and currency, then add a payment method before you run ads.\n\nThe rest of this guide walks through each step with screenshots, and it covers the part most tutorials skip: how to avoid the one setup mistake that forces a painful rebuild later. If your ad account ends up attached to your personal profile instead of your business, you can lose it, cap your spending, and orphan your tracking, so it is worth getting it right the first time.\n\n## What is a Facebook (Meta) ad account?\n\nA Facebook ad account is the container where you build campaigns, set your billing, and see your reporting. Everything you spend and every result you measure lives inside it. You need one before you can run a single ad*.\n\nYour ad account is not the same thing as your Facebook profile or your Facebook Page. Your profile is your personal identity, your Page is your business's public presence, and your ad account is the advertising engine that sits behind them. A Meta Pixel, the small piece of code that tracks what happens on your website, also connects to your ad account.\n\n***Note:** Meta automatically creates a personal ad account for first-timers (with no prior ad account) who boost a post. However, this is tied to their personal Facebook profile.\n\n**💡Pro tip:** If you plan to advertise regularly, it is important to create a full-featured ad account under a Business Portfolio, not from your personal Facebook profile, as doing so limits your ability to collaborate with agencies, colleagues, or freelancers.\n\n### What you need before you start\n\n**Quick checklist:**\n\n1. Personal Facebook profile (for login)\n2. A Meta Business Portfolio (create one if needed, using a business email)\n3. Facebook Page, or an admin/editor/advertiser role on one\n4. Payment method (card or PayPal)\n\n## How to create a Facebook ad account (step by step)\n\n**Step 1:** In Meta [Business Suite's Settings](https://business.facebook.com/latest/settings), look under' **Accounts** ' → click ' **Ad accounts** ', or go there directly @ [https://business.facebook.com/latest/settings/ad_accounts](https://business.facebook.com/latest/settings/ad_accounts).\n\n**Step 2:** Click on either of the blue ' **+ Add** ' button.\n\n**Step 3:** From the options, select ' **Create a new ad account**.'\n\n![How to create a new Facebook or Meta ad account](https://cdn.prod.website-files.com/60c334e72012f37063378d4b/68625355d2aa99293e89469f_7.%20How%20to%20create%20a%20new%20Facebook%20or%20Meta%20Ad%20account.png)\n\n**Step 4:** Fill in details, such as ad account name, your time zone, and currency. Click ' **Next**.'\n\n**Step 5:** Follow the on-screen prompts to declare usage and review the info and terms. Then click ' **Create Ad Account** '.\n\n> *⚠️* ***Note:*** *There are ad account limits per account. Once you create an ad account that's tied to a business portfolio, you cannot delete the ad account. However, you can close it and possibly reactivate it again in future. Read more about* [*closing Meta/Facebook ad accounts*](https://www.facebook.com/business/help/190760004667059?id=420299598837059)*.*\n\n**Step 6:** Now you have a Facebook/Meta ad account, but there's just one more step before you can start using it. Add at least one payment method to your Ad account when prompted to do so.\n\nThat is the core setup. Two details deserve their own attention, and they come next: the settings you cannot easily change later, and the payment step that quietly blocks your first campaign.\n\n*🔗 Related article:* [*How to Request Access to a Facebook Ad Account*](https://www.leadsie.com/blog/request-facebook-ad-account-access)\n\n### Desktop vs mobile\n\nSet up your ad account on a desktop browser if you can. The full settings are easier to find and the menus are clearer.\n\nYou can create and manage ad accounts in the Meta Ads Manager mobile app, but the menu labels differ from the desktop version and some options are harder to reach. Use mobile for quick checks, not for first-time setup.\n\n## Choosing your name, time zone, and currency (get this right, it's permanent)\n\nChoose your currency and time zone carefully, because they are effectively locked once the ad account is created. Changing them later usually means starting a brand-new ad account, so treat these fields as permanent decisions rather than defaults to rush past.\n\n1. **Pick the currency** you will actually pay in. Your billing follows this setting, and a mismatch creates accounting headaches every month. If your business banks in one currency, match it.\n2. **Pick the time zone** you report and schedule against. Your reporting days and your ad scheduling both run on this clock. If you manage the account from a different region than your business operates in, choose the one you will actually plan around.\n3. **Name the account** so it still makes sense if you add more accounts later. A clear naming convention, such as the business name plus the market or purpose, saves confusion once you are running more than one.\n\n| Setting | What to choose | Why it matters |\n| --- | --- | --- |\n| Currency | The currency you pay in | Billing follows it and it is hard to change later |\n| Time zone | The zone you report and schedule against | Sets your reporting days and ad timing |\n| Account name | A clear, scalable convention | Keeps multiple accounts organized |\n\n## Add your payment method and billing\n\nAdd a payment method before you try to launch anything, because no payment method means no live ads. Your account can exist fully set up and still refuse to spend until billing is attached.\n\n**How to add or edit a payment method:**\n\n1. Go to [Ads Manager.](https://adsmanager.facebook.com/adsmanager/)\n2. Open ' **Billing & payments'** from the left-hand menu\n3. Click ' **Payment methods** '\n4. Under 'Ad accounts', click ' **Add payment method** ' and enter a card or PayPal.\n5. Save it, and your account is cleared to run ads.\n\nIf your first campaign will not launch, billing is the first thing to check. It is the step people most often forget, and Meta will not deliver ads without it.\n\n![Add a payment method to your ad account on Meta Business Suite](https://cdn.prod.website-files.com/60c334e72012f37063378d4b/6a5cfacdc0a6a42dbf120369_2.%20Add%20a%20payment%20method%20to%20your%20ad%20account%20on%20Meta%20Business%20Suite.png)\n\n## The #1 setup mistake: Your ad account on a personal profile\n\nThe most common and costly setup mistake is running your ad account off your personal Facebook profile instead of a Business Portfolio. It happens by accident, it creates real risk, and fixing it later is a hassle, so check for it now.\n\nHere is how it happens. Your first brush with advertising is usually boosting a post or clicking a \"create ads\" link inside your personal profile. From there, in the words of Stacy Covitz, \"Meta, Facebook just takes it upon themselves to create an ad account under your personal profile. And you end up running ads from there.\" You never chose this setup; Facebook chose it for you.\n\nWhy it is a problem comes down to security, limits, and tracking. On the security side, your advertising is tied to your personal login: \"If your account gets hacked or locked or anything like that, your whole ad account goes down with it. And there's no separation,\" Covitz explains.\n\nOn the practical side, a personal ad account carries a tiny spending limit and cannot easily take on team members. And your tracking suffers, because \"your pixel is basically sitting on your personal account. It's not named. It's not managed. It's not getting the proper data.\"\n\n**How to check if yours is set up wrong:**\n\n1. Go to and log in at [https://business.facebook.com/latest/settings/ad_accounts/](https://business.facebook.com/latest/settings/ad_accounts/)\n2. Click on your ad account.\n3. Look at the name. If it does not carry your business name and it looks like it is \"floating\" on its own, it is sitting on your personal profile.The fix is to move your advertising under a proper Business Suite. Set up a Business Portfolio if you do not have one. Your pixel cannot simply be dragged over, so if the old pixel has little data, start a fresh one inside your new Business Suite. If the orphaned pixel does hold data worth keeping, name it first so you can at least identify it while you transition.\n\n*🔗 Related article:* [*How to create a Meta business portfolio*](https://www.leadsie.com/blog/how-to-create-meta-business-manager-and-facebook-ad-account#creating-your-first-business-portfolio)\n\n### The gold-standard ad account setup (checklist)\n\nA properly built Facebook advertising setup follows a simple structure. Start with a Meta Business Suite account. Have that be the umbrella for everything. Build yours to match this checklist and you avoid nearly every problem above.\n\n- **Meta Business Suite** as the umbrella that owns everything\n- Your **Facebook Page** and **Instagram** account connected to it\n- **One ad account** (one per business, unless you genuinely run several businesses)\n- Your **domain verified**, which is not required but always helpful\n- **Two-factor authentication** turned on\n- **Agencies, admins, and ad managers** added cleanly through settings *🔗 Related article:* [*How to give access to a Facebook (Meta) Ad account*](https://www.leadsie.com/blog/grant-facebook-ad-account-access)\n\n## Troubleshooting: Cannot find 'Create a new ad account'\n\nIf the “ **Create a new ad account** \" option is missing or greyed out, it is almost always one of four reasons, and each has a fix. The button is not broken; Meta is holding it back for a specific cause.\n\nThe usual causes are that your portfolio has reached its ad account limit, your account is too new, your business is restricted or unverified, or you do not have full admin access. Work through them in that order.\n\n| Cause | What to do |\n| --- | --- |\n| Portfolio hit its ad account limit | Request an increase, or reuse an existing account |\n| Account is too new | Wait out the new-account limits, then try again |\n| Business restricted or unverified | Verify your business and email in settings |\n| You lack full admin access | Ask a portfolio admin to grant you full control |\n\nIf none of these apply and the option still will not appear, verifying your business details and email resolves most of the remaining cases.\n\nWant to learn more? Explore our [Frequently Asked Questions](#FAQ) on this topic.\n\nABOUT THE AUTHOR\n\n##### Jocelyn Ke\n\nJocelyn’s background spans UX research, marketing, content writing, and consulting. Outside of work, she enjoys reading, surfing, board games, billiards, Latin dancing, and MMORPGs.\n\n![Image](https://cdn.prod.website-files.com/60c334e62012f37bfe378d44/63b84de59a68cc59c77f3895_party-popper_1f389.png)"},"author":{"name":"Jocelyn Ke","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/1203366d0d43f7e2/avatar-73ce35aca66a.png","bio":"Create a Facebook ad account the right way: set your name, time zone, currency, and billing, and avoid the personal-profile mistake that forces a costly rebuild."},"metadata":{"timestamp":"2026-07-26T00:00:00.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/I19zDBnuaO/media/0.png","type":"image","width":1200,"height":771}],"aiComments":[]},{"id":"web-izzida","shareId":"2upg5yLlBh","platform":"web","url":"https://www.leadsie.com/blog/managed-meta-accounts","title":"Managed Meta Accounts: What They Are and How to Migrate Your Organization","shareMode":"full","createdAt":1786033842604,"postedAt":"2026-07-31T00:00:00.000Z","publishedDate":"2026-07-31T00:00:00.000Z","archivedAt":"2026-08-06T16:30:37.633Z","archivedDate":"2026-08-06T16:30:37.633Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/2upg5yLlBh","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/NUDFalrpa7/media/0.png","contentType":"article","content":{"text":"[Leadsie Blog](https://www.leadsie.com/blog) Article Content\n\nMore and more organizations are starting to migrate to managed Meta accounts (MMA), but the process can be confusing if you’re encountering it for the first time. 😵💫\n\nSome people first hear the term after receiving an email asking them to sign up. Others want to migrate their organization but can’t find the option to enable it or aren’t sure where the process begins.\n\nPart of the confusion comes from how the system works.\n\nNot everyone can switch to managed Meta accounts. The process starts when an organization receives an invitation from Meta to begin the migration, after which the company's administrators can create accounts for employees and invite them to activate them.\n\nIn this guide, we’ll explain what managed Meta accounts are, why Meta introduced them, and how organizations can migrate to them. We’ll also walk through common issues teams run into during migration and how to fix them.\n\n### Key takeaways ✔️\n\n- **Managed Meta accounts (MMA)** are work-only accounts that let employees access Meta business tools without using a personal Facebook account.\n- Organizations must be **invited by Meta** before they can migrate to managed Meta accounts. Individuals cannot create them on their own.\n- Once invited, administrators set up identity systems (like SSO), create managed accounts for employees, and add the company’s Business Portfolios to the migration.\n- Employees then **activate their managed Meta account**, which creates a business user profile used to access tools like Meta Business Suite and Ads Manager.\n- After activation, employees may need to **reconnect third-party apps and integrations within 30 days** to prevent them from losing access to business assets.\n- External partners (such as agencies or freelancers) can still access business assets using **partner access in your Business Portfolio**, so they don’t need to be part of the managed Meta accounts created by your organization.\n\n## What are managed Meta accounts?\n\nManaged Meta accounts are free **work-only accounts that allow employees to access Meta’s business tools using their company credentials or single sign-on (SSO)** instead of a personal Facebook profile. This means employees no longer need a Facebook account to access Meta Business Suite or Ads Manager.\n\nTypically, anyone managing business assets on Meta, like ad accounts or Facebook Pages, needs to log in with a personal Facebook account. Even when assets are organized within a Business Portfolio, the login identity still belongs to the individual rather than the organization.\n\nFor businesses with larger teams, this can create security challenges because they have limited visibility into how those accounts interact with their business tools.\n\nManaged accounts solve this by separating personal and professional access.\n\n### How managed Meta accounts differ from personal Facebook accounts\n\nUnlike personal Facebook accounts, managed Meta accounts are designed **strictly** for business use.\n\nSome key differences include:\n\n**✔️ No consumer Facebook features**\n\nManaged Meta accounts don’t have a personal timeline or access to the Facebook News Feed. They are not meant for interacting with Facebook as an individual user and exist only to access business tools.\n\n**✔️ Accounts are created and managed by the organization**\n\nAdmins create accounts for employees and manage them centrally. They can set up accounts directly in the Admin Centre or connect an identity provider (IdP), such as Google Workspace, Okta, or Microsoft Azure AD.\n\n**✔️ Work-only access to business tools**\n\nThese accounts are used specifically to access Meta business products such as Meta Business Suite, Ads Manager, and other business assets.\n\n**✔️ Permissions must come through a business account**\n\nManaged Meta accounts cannot hold personal asset permissions. Access to Pages, ad accounts, or other assets must be granted through a Business Portfolio.\n\n**✔️ Login with work credentials**\n\nEmployees log in using their work email address or through single sign-on (SSO), rather than a personal Facebook profile.\n\n**🔗 Related article:** [Meta Business Suite vs. Meta Business Portfolio: What’s the Difference?](https://www.leadsie.com/blog/meta-business-manager-vs-meta-business-suite-differences)\n\n## Why Meta introduced managed accounts\n\nMeta introduced managed Meta accounts to give organizations more control over how employees access business tools and to support enterprise-level security.\n\nHere are some of the key reasons behind the change:\n\n**📝 Stronger security controls\n** Managed accounts support enterprise security features such as single sign-on (SSO). When Meta is connected to an organization’s identity provider (IdP), employees can log in using their work credentials while the company maintains control over authentication and security policies.\n\n**📝 Centralized account management\n** Accounts can be automatically created or deactivated when employees join or leave the company through identity provider integrations. This helps organizations keep employee access up to date and reduces the risk of outdated permissions.\n\n**📝 Better visibility and access auditing\n** Managed Meta accounts give organizations clearer visibility into who has access to business tools and assets, making it easier to track permissions and conduct internal security or compliance audits.\n\n## Who can use managed Meta accounts\n\nManaged Meta accounts are designed for larger organizations that want to manage employee access to Meta’s business tools in one place, using their own security and identity systems.\n\n❗ However, migration to managed Meta accounts is **not available to all organizations yet**. Businesses must first be eligible and receive an invitation from Meta before they can begin the migration process.\n\n![Image](https://cdn.prod.website-files.com/60c334e72012f37063378d4b/69b39a325364633903c1c35a_Invitation%20email%20to%20sign%20up%20to%20managed%20Meta%20accounts.png)\n\n### Can you prepare your organization for managed Meta accounts?\n\nMeta has not published a confirmed way to request or speed up access to managed Meta accounts. However, organizations can prepare their accounts so they’re ready if they become eligible.\n\nSome steps that may help include:\n\n- Completing **business verification**\n- Enabling **single sign-on (SSO)** through an identity provider\n- Enforcing **two-factor authentication (2FA)** for business tools\n- Consolidating assets under a **single Business Portfolio**\n- Maintaining **active ad accounts and consistent ad spend**\n- Working with a **Meta partner or account representative** While these steps don’t guarantee an invitation, they align with the infrastructure typically required for organizations migrating to managed accounts.\n\n**🔗 Related article:** [How to Create a Meta Business and Facebook Ad Account](https://www.leadsie.com/blog/how-to-create-meta-business-manager-and-facebook-ad-account)\n\n## How the managed Meta account migration works\n\nOrganizations migrate to managed accounts through a structured process that replaces employees’ personal Facebook logins with organization-managed accounts.\n\nThe transition is handled at the organization level, meaning administrators first prepare the company’s systems and Business Portfolios before employees begin activating their accounts.\n\nSeveral groups are usually involved in the process, including people with organization management task access, IT managers responsible for identity and security setup, and the employees who will activate their accounts. In some cases, agencies or external partners may also be involved.\n\nThe process typically takes place in four main stages.\n\n### Stage 1: Preparing the organization for migration\n\nThis stage is usually led by an **organization manager** (administrator), who oversees the migration and coordinates the setup process. In many cases, they act as project leads to ensure the migration runs smoothly.\n\nThe first step is to **audit who currently has access to the organization’s Business Portfolios and assets**. This helps ensure only the right employees are migrated to managed Meta accounts.\n\nOnce the right people are involved, administrators need to complete several setup tasks before migration can begin. These usually include:\n\n- Verifying the organization’s email domains\n- Connecting an identity provider (IdP)\n- Setting up single sign-on (SSO)\n- Enabling two-factor authentication (2FA)\n- Claiming the business portfolios that will be migrated\n\n![Image](https://cdn.prod.website-files.com/60c334e72012f37063378d4b/69b39afd30a614ecc0a24abc_Claim%20a%20business%20portfolio%20in%20Organizations%20Manager.png)\n\n👉 If you want a full list of tasks Meta recommends completing before migration, you can review the [**pre‑migration checklist for managed Meta accounts**](https://www.facebook.com/business/m/managed-meta-accounts/pre-migration-checklist).\n\nThese steps ensure the organization’s identity and security systems are ready to manage employee access to Meta’s business tools.\n\n**🔎 Note:** Organization managers can also **invite up to three people** to help them complete IT tasks in Organization Manager during migration. Learn how to invite them [here](https://www.facebook.com/business/help/1256722792362621?id=586399373675491).\n\n### Stage 2: Creating and inviting employee accounts\n\nOnce the organization is ready, administrators can create managed Meta accounts for employees.\n\n**🔎 Note:** Everyone who needs access to the organization’s Business Portfolios **must** have a managed Meta account created for them.\n\nAccounts can be [created in two ways](https://www.facebook.com/business/help/1559902565394669?id=586399373675491):\n\n1. **Automatically through an identity provider (IdP) ** Administrators can connect an identity provider such as Google Workspace, Microsoft Entra ID, or Okta in Admin Center. When this integration is enabled, managed Meta accounts are automatically created when employees are added to the organization’s identity directory. Similarly, removing someone from the directory will deactivate their managed Meta account.\n2. **Manually through Admin Center ** Administrators can also create accounts manually by entering an employee’s name and work email address in Admin Center. However, manually created accounts must also be managed manually; for example, administrators would need to deactivate the account if an employee leaves the organization.Once accounts are created and the relevant Business Portfolios have been added to Organization Manager, administrators can [invite employees to migrate to their managed Meta accounts](https://www.facebook.com/business/help/164957106539403).\n\n### Stage 3: Employees activate their managed Meta accounts\n\nEmployees must activate their managed Meta accounts before they can use them to access business tools.\n\nActivation can typically be completed through the invitation email or through a notification in Meta Business Suite.\n\n![Image](https://cdn.prod.website-files.com/60c334e72012f37063378d4b/69b39b2c513287933bf8544d_Activate%20meta%20managed%20account.png)\n\nDuring activation, employees may be asked to:\n\n- Verify their work email address\n- Set up authentication methods such as two-factor authentication\n- Log in using their organization’s identity provider (if SSO is enabled)\n- Confirm the business portfolios and assets they will access Once activated, a **business user profile** is created for the employee, which is used to access Meta’s business tools. They will no longer need to use their personal Facebook account to access their organization’s business portfolios or assets.\n\n**🔎 Note:** Employees must activate their account before the migration deadline set by the organization. If they miss the deadline, they may temporarily lose access to the organization’s business tools until the migration is completed.\n\n### Stage 4: Reconnecting integrations and tools\n\nAfter activating their managed Meta accounts, employees may need to reconnect third-party apps and integrations that were previously connected through their personal Facebook accounts.\n\nThis might include tools like social media management platforms, analytics tools, or other apps that had permission to access business assets such as Facebook Pages or ad accounts.\n\nBecause these integrations were originally authorized through a personal Facebook account, their permissions don’t automatically carry over to the new managed account. As a result, employees will need to reconnect these apps using their **business user profile**, which is created when the managed Meta account is activated.\n\nTo help avoid disruptions, Meta provides a **30-day grace period** during migration so users have time to reconnect their integrations.\n\n## How agencies and external partners can access assets after migration\n\nOrganizations often work with external partners such as marketing agencies, consultants, or freelancers who need access to business assets like Facebook Pages or ad accounts.\n\nThese partners **do not need a managed Meta account tied to their client’s organization**.\n\nInstead, they can be granted access through [**partner access**](https://www.leadsie.com/blog/request-facebook-ad-account-access) in the organization’s Business Portfolio. This allows them to continue using their personal Facebook accounts while working with your business assets.\n\nAlternatively, agencies can use a client onboarding tool like [**Leadsie**](https://www.leadsie.com/integrations/meta) to securely request access to Meta assets through a single access request link. Rather than guiding clients through Business Portfolio settings, clients can approve access in just a few clicks.\n\nOnce access is granted, partners can manage the assigned assets, such as Pages or ad accounts, without being added as employees in the organization’s managed Meta account system.\n\n**❗ Note:** External partners are **not connected to the organization’s identity provider**. Because of this, administrators must manually remove their access to business assets if the partnership ends.\n\n**🔗 Related article:** [Unable to assign assets in Meta Business Suite? Here are 4 reasons and how to fix them](https://www.leadsie.com/blog/unable-to-assign-assets-meta)\n\n## Common issues when using managed Meta accounts 🛠️\n\nMigrating to managed Meta accounts introduces new workflows and identity controls, which can sometimes lead to unexpected issues.\n\nBelow are some common problems organizations and employees may encounter during migration, along with possible explanations:\n\n### ❌ Migration is not available for your organization\n\nNot all organizations currently have access to managed Meta accounts.\n\nIf your organization has not received an invitation from Meta, it means migration is not yet available for your business. At this stage, all you can do is prepare your accounts and infrastructure for when migration becomes available.\n\n### ❌ Problems adding a Business Portfolio to Organization Manager\n\nOrganizations must add their Business Portfolios to Organization Manager before migration can begin. In some cases, administrators may encounter errors when trying to add a portfolio.\n\nSome common reasons include:\n\n- The **business ID entered is incorrect**\n- The portfolio **is already managed by your organization**\n- The portfolio **is already managed by another organization**\n- The portfolio **is already pending approval** In some cases, a request to add a portfolio may require **manual approval** from someone who has full control of the Business Portfolio.\n\nIf the request is not approved within **30 days**, it will expire and must be submitted again.\n\nAdditionally, Meta allows **only one request every 24 hours**, so administrators may need to wait before submitting another request.\n\n### ❌ Employees lose access to features after migration\n\nAfter activating a managed Meta account, employees may notice that certain features or products they previously accessed with their Facebook account are no longer available.\n\nThis happens because **business user profiles are designed for business tools only**, not consumer platforms.\n\nFor example, a managed Meta account cannot be used to access:\n\n- Facebook as a social platform\n- Instagram\n- Messenger However, employees can still manage business assets such as Pages or ad accounts through **Meta Business Suite**, provided those assets are part of the organization’s Business Portfolio.\n\n### ❌ Some Meta tools do not support managed Meta accounts\n\nCertain Meta tools currently do not support managed Meta accounts.\n\nAn example of this is **Meta Ads Manager for Excel**. If you need to analyze advertising data, Meta recommends exporting reports from Ads Manager or using the Ad Insights API instead.\n\n### ❌ Employees cannot access certain ad features\n\nSome advertising features require additional terms to be accepted after migration.\n\nFor example, users must accept the [**Customer List Custom Audiences Terms**](https://www.facebook.com/legal/terms/customaudience) before they can create or edit ads that use uploaded customer list audiences.\n\n### ❌ Missing invitation emails\n\nIf someone was invited to help with migration or activate a managed Meta account but cannot find the email invitation, they should check their spam folder or log in directly to Meta Business Suite to accept the invitation.\n\n## ⚡ Agency hack: Save hours every week onboarding new clients\n\nWhether you manage ad campaigns, social media, or analytics, getting clients’ accounts set up shouldn’t slow you down.\n\nWith Leadsie, you can request and receive access to all your clients’ accounts using just one secure link. 🔒\n\n![Image](https://cdn.prod.website-files.com/60c334e72012f37063378d4b/6a6cecb98f9f0e0347b2d1fa_GIF_v4_-_How_Leadsie_works_Meta_access_540px.avif)\n\n### What is Leadsie?\n\nLeadsie is a client onboarding software that simplifies requesting and giving access to marketing assets, social media, and ad accounts with one secure link. Get access to your clients’ or influencers’ Facebook, Instagram, Google, TikTok, Shopify, LinkedIn, and other accounts without sharing passwords.\n\nLeadsie handles the most time-consuming aspect of onboarding clients: managing access and adding users. It keeps permission management secure and organized as your agency scales.\n\n### Why agencies use Leadsie for client onboarding\n\n✅ Minimize frustrating chaser emails and calls for access\n\n✅ Reduces your agency's [turnaround time by over 50%](https://www.leadsie.com/blog/dashclicks-case-study)\n\n✅ Scales with your agency as you [grow beyond onboarding 5-10 new clients a week](https://www.leadsie.com/blog/ecommerce-equation-case-study)\n\n✅ Makes it possible to get access to 23+ social, marketing, and analytics platforms at once\n\n✅ Start billable work and billing cycles for your new clients without delays\n\n🎁 Try a [**free 14-day trial**](#signup-bottom) on us—no credit card needed!\n\n*P.S. It's risk-free, and you get to keep your account connections after the trial ends. 🙌*\n\nWant to learn more? Explore our [Frequently Asked Questions](#FAQ) on this topic.\n\nABOUT THE AUTHOR\n\n##### Nina Lelidou\n\nNina is an SEO content specialist with expertise in B2B SaaS. She helps businesses increase organic traffic with strategic, high-quality content. Outside of work, you’ll find her sailing somewhere in the Mediterranean.\n\n![Image](https://cdn.prod.website-files.com/60c334e62012f37bfe378d44/63b84de59a68cc59c77f3895_party-popper_1f389.png)"},"author":{"name":"Nina Lelidou","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/7eab3a441997ca50/avatar-73ce35aca66a.png","bio":"Confused about managed Meta accounts? Learn what they are, how migration works, and what your organization needs to do to get started."},"metadata":{"timestamp":"2026-07-31T00:00:00.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/NUDFalrpa7/media/0.png","type":"image","width":1536,"height":1024}],"aiComments":[]},{"id":"web-6cub6k","shareId":"b1swBXB8yk","platform":"web","url":"https://github.com/opendataloader-project/opendataloader-bench","title":"opendataloader-project/opendataloader-bench: OpenDataLoader Benchmark","shareMode":"full","createdAt":1786031725861,"postedAt":"2026-08-06T15:55:13.632Z","publishedDate":"2026-08-06T15:55:13.632Z","archivedAt":"2026-08-06T15:55:17.209Z","archivedDate":"2026-08-06T15:55:17.209Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/b1swBXB8yk","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/eXVeNqYhKY/media/0.png","contentType":"article","content":{"text":"## opendataloader-bench\n\n\n\n## 1. About the Project\n\nPDF documents are everywhere, but LLMs can't read them directly. Extracting structured content — headings, tables, reading order — from PDFs is essential for RAG pipelines and document processing workflows.\n\nThis benchmark evaluates document structure and layout analysis engines to help you choose the right tool.\n\n**What we measure:**\n\n- **Reading Order** — Is the text extracted in the correct sequence?\n- **Table Fidelity** — Are tables accurately reconstructed?\n- **Heading Hierarchy** — Is the document structure preserved? The evaluation pipeline is modular—add new engines, corpora, or metrics with minimal effort.\n\n\n\n## 2. Benchmark Results\n\n\n\n### Quality Comparison\n\n\n\n| Engine | Overall | Reading Order | Table | Heading | Speed (s/page) | License |\n| --- | --- | --- | --- | --- | --- | --- |\n| **opendataloader [hybrid]** | **0.907** | **0.934** | **0.928** | 0.821 | 0.463 | Apache-2.0 |\n| nutrient | 0.885 | 0.925 | 0.708 | 0.819 | **0.008** | Commercial |\n| docling | 0.882 | 0.898 | 0.887 | **0.824** | 0.762 | MIT |\n| marker | 0.861 | 0.890 | 0.808 | 0.796 | 53.932 | GPL-3.0 |\n| unstructured [hi_res] | 0.841 | 0.904 | 0.588 | 0.749 | 3.008 | Apache-2.0 |\n| edgeparse | 0.837 | 0.894 | 0.717 | 0.706 | 0.036 | Apache-2.0 |\n| opendataloader | 0.831 | 0.902 | 0.489 | 0.739 | 0.015 | Apache-2.0 |\n| mineru | 0.831 | 0.857 | 0.873 | 0.743 | 5.962 | AGPL-3.0 |\n| pymupdf4llm | 0.732 | 0.885 | 0.401 | 0.412 | 0.091 | AGPL-3.0 |\n| unstructured | 0.686 | 0.882 | 0.000 | 0.388 | 0.077 | Apache-2.0 |\n| markitdown | 0.589 | 0.844 | 0.273 | 0.000 | 0.114 | MIT |\n| liteparse | 0.576 | 0.866 | 0.000 | 0.000 | 1.061 | Apache-2.0 |\n\n\n\n> Scores are normalized to [0, 1]. Higher is better for accuracy metrics; lower is better for speed. **Bold** indicates best performance.\n\n\n\n### Visual Comparison\n\n[![Benchmark](https://github.com/opendataloader-project/opendataloader-bench/raw/main/charts/benchmark.png)](https://github.com/opendataloader-project/opendataloader-bench/blob/main/charts/benchmark.png)\n\n[![Quality Breakdown](https://github.com/opendataloader-project/opendataloader-bench/raw/main/charts/benchmark_quality.png)](https://github.com/opendataloader-project/opendataloader-bench/blob/main/charts/benchmark_quality.png)\n\nDetailed JSON outputs live alongside each engine and capture the exact metric values:\n\n- [prediction/opendataloader/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/opendataloader/evaluation.json)\n- [prediction/opendataloader-hybrid/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/opendataloader-hybrid/evaluation.json)\n- [prediction/docling/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/docling/evaluation.json)\n- [prediction/marker/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/marker/evaluation.json)\n- [prediction/edgeparse/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/edgeparse/evaluation.json)\n- [prediction/nutrient/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/nutrient/evaluation.json)\n- [prediction/mineru/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/mineru/evaluation.json)\n- [prediction/pymupdf4llm/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/pymupdf4llm/evaluation.json)\n- [prediction/unstructured/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/unstructured/evaluation.json)\n- [prediction/unstructured-hires/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/unstructured-hires/evaluation.json)\n- [prediction/markitdown/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/markitdown/evaluation.json)\n- [prediction/liteparse/evaluation.json](https://github.com/opendataloader-project/opendataloader-bench/blob/main/prediction/liteparse/evaluation.json)\n\n## 3. Metrics\n\nAll scores are normalised to the `[0, 1]` range, where higher indicates a closer match to ground truth. Documents missing the artefacts required by a given metric yield `null` in per-document results and are excluded from aggregate means.\n\n\n\n### 3.1. Reading Order Similarity (NID, NID-S)\n\nThe reading order is evaluated using Normalized Indel Distance (NID), which measures the similarity between the ground truth and predicted text.\n\n$$ NID = 1 - \\frac{\\text{distance}}{\\text{len(gt)} + \\text{len(pred)}} $$\n\n- **NID**: Compares the full extracted text of the prediction against the ground truth.\n- **NID-S**: Strips tables before comparison to focus on narrative reading order.\n\n### 3.2. Table Structure Similarity (TEDS, TEDS-S)\n\nTables are evaluated using Tree Edit Distance Similarity (TEDS), comparing DOM structures with the APTED algorithm.\n\n$$ {TEDS}(T_{\\text{gt}}, T_{\\text{pred}}) = 1 - \\frac{{EditDist}(T_{\\text{gt}}, T_{\\text{pred}})}{\\max(|T_{\\text{gt}}|, |T_{\\text{pred}}|, 1)} $$\n\n- **TEDS**: Evaluates both structure and cell text.\n- **TEDS-S**: Structure-only, ignoring textual differences (e.g., OCR noise).\n\n### 3.3. Heading-Level Similarity (MHS, MHS-S)\n\nHeadings are parsed into a flat list and compared using APTED.\n\n$$ {MHS}(H_{\\text{gt}}, H_{\\text{pred}}) = 1 - \\frac{{EditDist}(H_{\\text{gt}}, H_{\\text{pred}})}{\\max(|H_{\\text{gt}}|, |H_{\\text{pred}}|, 1)} $$\n\n- **MHS**: Rewards correctly positioned headings and aligned content blocks.\n- **MHS-S**: Structure-only, isolating heading topology.\n\n### 3.4. References\n\n- Z. Chen et al. \"MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models.\" *arXiv:2501.15000*, 2025.\n- X. Zhong et al. \"Image-based Table Recognition: Data, Model, and Evaluation.\" *ECCV Workshops*, 2020.\n- M. Pawlik and N. Augsten. \"RTED: A Robust Algorithm for the Tree Edit Distance.\" *arXiv:1201.0230*, 2011.\n- Upstage AI. \"Document Parsing Benchmark (DP-Bench).\" Hugging Face, 2024.\n\n---\n\n\n\n## 4. Reproduce the Benchmark\n\nWant to run this benchmark yourself or add a new engine? Follow the steps below.\n\n\n\n### Prerequisites\n\n- Python 3.13 or higher\n- Git LFS (for PDF files)\n\n### Installation\n\n1. **Clone and set up Git LFS**: ``` git clone https://github.com/opendataloader-project/opendataloader-bench cd opendataloader-bench git lfs install git lfs pull ```\n2. **Install base dependencies** (evaluation + chart generation only): ``` uv sync ```\n3. **Install engine(s) you want to run**: ``` # Individual engines uv sync --extra opendataloader uv sync --extra docling uv sync --extra markitdown # All permissively-licensed engines at once uv sync --extra all-safe ``` AGPL/GPL engines (marker, MinerU, PyMuPDF) and commercial engines (nutrient) are not runnable from this repo — their parser code has been removed to avoid license/commercial-tier entanglement. Their `prediction/` results are preserved so the comparison charts still display them. > Don't have uv? See [installation guide](https://docs.astral.sh/uv/getting-started/installation/)\n\n### Running the Benchmark\n\n\n\n#### Quality Benchmark (default)\n\n\n\n```\n# Full pipeline: parse → evaluate → archive → chart\nuv run src/run.py\n\n# Single engine (skips engines that already have evaluation.json)\nuv run src/run.py --engine docling\n\n# Force re-run even if results exist\nuv run src/run.py --engine docling --force\n```\n\n\n\n#### Individual Stages\n\n\n\n```\n# 1. Parse PDFs\nuv run src/pdf_parser.py\n\n# 2. Evaluate predictions\nuv run src/evaluator.py\n\n# 3. Generate charts (works with existing evaluation.json data only)\nuv run src/generate_benchmark_chart.py\n\n# 4. Archive results\nuv run src/generate_history.py\n```\n\n\n\n#### Targeting Specific Engines or Documents\n\n\n\n```\n# Single engine\nuv run src/pdf_parser.py --engine opendataloader\nuv run src/evaluator.py --engine opendataloader\n\n# Single document\nuv run src/pdf_parser.py --doc-id 01030000000001\n\n# Both\nuv run src/pdf_parser.py --engine opendataloader --doc-id 01030000000001\n```\n\n\n\n### Project Structure\n\n\n\n```\n├─ charts/ # Generated benchmark charts\n├─ ground-truth/ # Reference annotations and structured ground truth\n├─ history/ # Archived evaluation results by date\n├─ pdfs/ # Input PDF corpus (200 sample documents)\n├─ prediction/ # Engine outputs grouped by engine/markdown\n├─ src/ # Conversion, evaluation, and utility scripts\n└─ pyproject.toml # Python dependencies (uv)\n```\n\n\n\n## 5. Contributing\n\n\n\n### Development Setup\n\n\n\n```\n# After following the installation steps above:\nuv sync --dev\n```\n\nThis installs development dependencies including pytest.\n\n\n\n### Running Tests\n\n\n\n```\nuv run pytest\n```\n\n\n\n### Interpreting evaluation.json\n\nEach engine produces an `evaluation.json` with:\n\n- **`summary`**: Engine name/version, hardware info, document count, runtime, date.\n- **`metrics.score`**: Mean scores (`overall_mean`, `nid_mean`, `teds_mean`, `mhs_mean`, etc.)\n- **`metrics.*_count`**: Number of documents eligible for each metric.\n- **`documents`**: Per-document scores and availability flags.\n\n## 6. References\n\n- Z. Chen, Y. Liu, L. Shi, X. Chen, Y. Zhao, and F. Ren. \"MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models.\" *arXiv preprint arXiv:2501.15000*, 2025. [https://arxiv.org/abs/2501.15000](https://arxiv.org/abs/2501.15000)\n- J. He, M. Rungta, D. Koleczek, A. Sekhon, F. X. Wang, and S. Hasan. \"Does Prompt Formatting Have Any Impact on LLM Performance?.\" *arXiv preprint arXiv:2411.10541*, 2024. [https://arxiv.org/abs/2411.10541](https://arxiv.org/abs/2411.10541)\n- D. Min, N. Hu, R. Jin, N. Lin, J. Chen, Y. Chen, Y. Li, G. Qi, Y. Li, N. Li, and Q. Wang. \"Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data.\" *arXiv preprint arXiv:2402.12869*, 2024. [https://arxiv.org/abs/2402.12869](https://arxiv.org/abs/2402.12869)\n- M. Pawlik and N. Augsten. \"RTED: A Robust Algorithm for the Tree Edit Distance.\" *arXiv preprint arXiv:1201.0230*, 2011. [https://arxiv.org/abs/1201.0230](https://arxiv.org/abs/1201.0230)\n- Upstage AI. \"Document Parsing Benchmark (DP-Bench).\" Hugging Face, 2024. [https://huggingface.co/datasets/upstage/dp-bench](https://huggingface.co/datasets/upstage/dp-bench)\n- X. Zhong, J. Tang, and A. J. Yepes. \"Image-based Table Recognition: Data, Model, and Evaluation.\" *European Conference on Computer Vision Workshops*, 2020. [https://arxiv.org/abs/1911.10683](https://arxiv.org/abs/1911.10683)\n- X. Zhong, J. Tang, and A. J. Yepes. \"PubLayNet: largest dataset ever for document layout analysis.\" *International Conference on Document Analysis and Recognition*, 2019. [https://huggingface.co/datasets/jordanparker6/publaynet](https://huggingface.co/datasets/jordanparker6/publaynet)"},"author":{"name":"opendataloader-project","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/54fbd626d396f927/avatar-6a9577cd4f7f.svg","bio":"OpenDataLoader Benchmark. Contribute to opendataloader-project/opendataloader-bench development by creating an account on GitHub."},"metadata":{"timestamp":"2026-08-06T15:55:13.632Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/eXVeNqYhKY/media/0.png","type":"image","width":1200,"height":600}],"aiComments":[]},{"id":"web-njobmc","shareId":"hfAoltQ54v","platform":"web","url":"https://trending-repos.com/","title":"The market terminal for open source","shareMode":"full","createdAt":1786031263320,"postedAt":"2026-08-06T15:47:32.114Z","publishedDate":"2026-08-06T15:47:32.114Z","archivedAt":"2026-08-06T15:47:36.067Z","archivedDate":"2026-08-06T15:47:36.067Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/hfAoltQ54v","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/yGKAUnox2Y/media/0.png","contentType":"article","content":{"text":"[Trending ReposTrending](https://trending-repos.com/)[Trending](https://trending-repos.com/)[Risers](https://trending-repos.com/risers)[Newest](https://trending-repos.com/newest)[Collections](https://trending-repos.com/collections)[Topics](https://trending-repos.com/topics)[Compare](https://trending-repos.com/compare)[Search](https://trending-repos.com/search)[Submit](https://trending-repos.com/submit)\n\n[Trending Repos](https://trending-repos.com/)The market terminal for open source — every repository quoted by momentum, re-priced every four hours. Velocity, not raw popularity.\n\nTracking 35,304 repos across 200 languages\n\n## Discover\n\n- [Trending](https://trending-repos.com/)\n- [Risers](https://trending-repos.com/risers)\n- [Newest](https://trending-repos.com/newest)\n- [Collections](https://trending-repos.com/collections)\n- [Topics](https://trending-repos.com/topics)\n\n## Tools\n\n- [Compare](https://trending-repos.com/compare)\n- [Search](https://trending-repos.com/search)\n- [Submit a repo](https://trending-repos.com/submit)\n\n## Project\n\n- [About](https://trending-repos.com/about)\n- [Stats](https://trending-repos.com/stats)\n- [Privacy](https://trending-repos.com/privacy)\n- [GitHub](https://github.com/simonrueba)\n\nMomentum-based scoring · updated every 4 hours.\n\n## Quote board\n\nmomentum-weighted · re-priced every 4h\n\nSession\n\nSector\n\nLicense\n\nFloor\n\n100 / 35,304 LISTED\n\n| RK | SYM | Name | Stars | Δ24H | Trend | Score |\n| --- | --- | --- | --- | --- | --- | --- |\n| 01 | COMP | cloudflare/computer | 4.3k | +2.4k | 1496.1 |\n| 02 | ANYD | firecrawl/anydoc | 6.4k | +3.6k | 1090.1 |\n| 03 | SKIL | mattpocock/skills | 206k | +1.7k | 1083.3 |\n| 04 | REVE | zhaoxuya520/reverse-skill | 20k | +1.2k | 1067.1 |\n| 05 | PDFI | firecrawl/pdf-inspector | 12k | +1.5k | 1020.0 |\n| 06 | CRM | trycompai/crm | 6.9k | +1.6k | 941.0 |\n| 07 | QM | yc-software/qm | 12k | +554 | 664.9 |\n| 08 | OMNI | diegosouzapw/OmniRoute | 41k | +1.1k | 620.6 |\n| 09 | AIAG | bojieli/ai-agent-book | 33k | +1.0k | 596.4 |\n| 10 | STRI | usestrix/strix | 49k | +650 | 558.8 |\n| 11 | PONY | DietrichGebert/ponytail | 97k | +853 | 551.7 |\n| 12 | DEEP | esengine/DeepSeek-Reasonix | 32k | +822 | 520.1 |\n| 13 | FIRE | firecrawl/firecrawl | 162k | +703 | 498.3 |\n| 14 | ANIM | juliangarnier/anime | 72k | +801 | 495.9 |\n| 15 | SUPE | obra/superpowers | 268k | +823 | 488.3 |\n| 16 | ENGR | nagisanzenin/engram | 1.3k | +777 | 477.1 |\n| 17 | ORCA | stablyai/orca | 39k | +719 | 471.5 |\n| 18 | AIFO | microsoft/AI-For-Beginners | 62k | +441 | 454.5 |\n| 19 | KIMI | FareedKhan-dev/kimi-k3-in-c | 2.7k | +560 | 437.8 |\n| 20 | TAST | Leonxlnx/taste-skill | 73k | +627 | 434.4 |\n| 21 | BUZZ | block/buzz | 24k | +619 | 430.4 |\n| 22 | FDET | xdash/FDE-the-Guidance-Book-of-Forward-Deployed-Engineer | 3.5k | +487 | 423.8 |\n| 23 | AGEN | Panniantong/Agent-Reach | 67k | +548 | 420.9 |\n| 24 | HERM | NousResearch/hermes-agent | 226k | +573 | 415.7 |\n| 25 | IMPE | pbakaus/impeccable | 56k | +589 | 408.3 |\n| 26 | GCMI | LiamGvchi/gc-minimal-zine-poster | 3.8k | +599 | 402.1 |\n| 27 | GLOW | charmbracelet/glow | 27k | +22 | 400.1 |\n| 28 | PI | earendil-works/pi | 85k | +672 | 398.1 |\n| 29 | GRAP | Graphify-Labs/graphify | 103k | +539 | 374.0 |\n| 30 | GENE | microsoft/generative-ai-for-beginners | 117k | +399 | 367.4 |\n\nANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲ANYD+3.6k▲COMP+2.4k▲SKIL+1.7k▲CRM+1.6k▲PDFI+1.5k▲\n\n# The Market Terminal for Open Source\n\nevery GitHub repository, quoted by momentum\n\n● LiveSession 2026-08-06Listed 35,304Re-price T−–:––:––\n\nOSS-100 Composite30,708.7\n▼ −8.30% vs prev. session\n\nLeading sectorRust\n▲ +43.64% star velocity\n\nNew listings · session27\nfirst tracked this session\n\nNext re-priceT−–:––:––\nevery 4 hours\n\nToday's session\nThu, Aug 6\n[![cloudflare/computer avatar](https://avatars.githubusercontent.com/u/314135?v=4&s=128) cloudflare/computer#1TypeScriptGive your agent a computer 👾 24h delta+2.4kTotal stars](https://trending-repos.com/repositories/cloudflare/computer)\nSectors · star velocity vs prev. session\n[Python+7.2%](https://trending-repos.com/trending/Python)[TypeScript+21.5%](https://trending-repos.com/trending/TypeScript)[Rust+43.6%](https://trending-repos.com/trending/Rust)[JavaScript+21.7%](https://trending-repos.com/trending/JavaScript)[Go−3.1%](https://trending-repos.com/trending/Go)[Shell+3.4%](https://trending-repos.com/trending/Shell)\n\nTop gainers\nLast 24 hours\n\n- [1 ![firecrawl/anydoc avatar](https://avatars.githubusercontent.com/u/135057108?v=4&s=48) firecrawl/anydoc+3.6k](https://trending-repos.com/repositories/firecrawl/anydoc)\n- [2 ![cloudflare/computer avatar](https://avatars.githubusercontent.com/u/314135?v=4&s=48) cloudflare/computer+2.4k](https://trending-repos.com/repositories/cloudflare/computer)\n- [3 ![mattpocock/skills avatar](https://avatars.githubusercontent.com/u/28293365?v=4&s=48) mattpocock/skills+1.7k](https://trending-repos.com/repositories/mattpocock/skills)\n- [4 ![trycompai/crm avatar](https://avatars.githubusercontent.com/u/195194844?v=4&s=48) trycompai/crm+1.6k](https://trending-repos.com/repositories/trycompai/crm)\n\n## Discover by theme\n\n[All collections](https://trending-repos.com/collections)\n[AI & LLMs ![diegosouzapw/OmniRoute avatar](https://avatars.githubusercontent.com/u/8016841?v=4&s=32) diegosouzapw/OmniRoute41.3k ![bojieli/ai-agent-book avatar](https://avatars.githubusercontent.com/u/1421793?v=4&s=32) bojieli/ai-agent-book33.4k ![usestrix/strix avatar](https://avatars.githubusercontent.com/u/187630752?v=4&s=32) usestrix/strix49.2kTypeScript on top](https://trending-repos.com/collections/ai)[Web frameworks ![Leonxlnx/taste-skill avatar](https://avatars.githubusercontent.com/u/219127460?v=4&s=32) Leonxlnx/taste-skill72.9k](https://trending-repos.com/collections/web)\n\nTrending Repos quotes the momentum of 35,304 GitHub repositories — re-priced every four hours from EMA-smoothed star and fork velocity, so you see what's moving, not just what's big. Daily, weekly, and monthly sessions run side-by-side with persistent rank history. Free, for everyone.\n\nFilter by sector or license, drill into any ticker's rank history, and put a self-updating medal badge in your README. See the [about page](https://trending-repos.com/about) for the scoring formula and data sources.\n\n● LIVELAST RE-PRICE 12:09 UTC35,304 LISTEDDATA: GITHUB STARS & FORKS · EMA-SMOOTHEDTHE MARKET TERMINAL FOR OPEN SOURCE\n\n4.3k\n\nDaily score\n1496.1\n\n7-day momentum\n\n[5 ![firecrawl/pdf-inspector avatar](https://avatars.githubusercontent.com/u/135057108?v=4&s=48) firecrawl/pdf-inspector+1.5k](https://trending-repos.com/repositories/firecrawl/pdf-inspector)\n[See all](https://trending-repos.com/risers)\nNew listings\nCreated in 90 days\n\n- [1 ![Binaryify/open-kimi-ppt-skill avatar](https://avatars.githubusercontent.com/u/12221718?v=4&s=48) Binaryify/open-kimi-ppt-skill1d ago](https://trending-repos.com/repositories/Binaryify/open-kimi-ppt-skill)\n- [2 ![KKKKhazix/human-writing avatar](https://avatars.githubusercontent.com/u/239590580?v=4&s=48) KKKKhazix/human-writing1d ago](https://trending-repos.com/repositories/KKKKhazix/human-writing)\n- [3 ![mikiarlo3/awesome-growth-hacking-skills avatar](https://avatars.githubusercontent.com/u/1715109?v=4&s=48) mikiarlo3/awesome-growth-hacking-skills1d ago](https://trending-repos.com/repositories/mikiarlo3/awesome-growth-hacking-skills)\n- [4 ![cristicretu/diri avatar](https://avatars.githubusercontent.com/u/45521157?v=4&s=48) cristicretu/diri2d ago](https://trending-repos.com/repositories/cristicretu/diri)\n- [5 ![ZzzLc0405/photo-abstract-editorial avatar](https://avatars.githubusercontent.com/u/207724921?v=4&s=48) ZzzLc0405/photo-abstract-editorial2d ago](https://trending-repos.com/repositories/ZzzLc0405/photo-abstract-editorial)[See all](https://trending-repos.com/newest?window=90)\n\nHolding the top\nTop this month\n\n- [1 ![hasaneyldrm/exercises-dataset avatar](https://avatars.githubusercontent.com/u/28005450?v=4&s=48) hasaneyldrm/exercises-dataset14.9k★](https://trending-repos.com/repositories/hasaneyldrm/exercises-dataset)\n- [2 ![DigitalPlatDev/FreeDomain avatar](https://avatars.githubusercontent.com/u/126394654?v=4&s=48) DigitalPlatDev/FreeDomain191.2k★](https://trending-repos.com/repositories/DigitalPlatDev/FreeDomain)\n- [3 ![OpenByteInc/QuantDinger avatar](https://avatars.githubusercontent.com/u/306676008?v=4&s=48) OpenByteInc/QuantDinger10.2k★](https://trending-repos.com/repositories/OpenByteInc/QuantDinger)\n- [4 ![openai/whisper avatar](https://avatars.githubusercontent.com/u/14957082?v=4&s=48) openai/whisper106.7k★](https://trending-repos.com/repositories/openai/whisper)\n- [5 ![modem-dev/hunk avatar](https://avatars.githubusercontent.com/u/207912732?v=4&s=48) modem-dev/hunk8.1k★](https://trending-repos.com/repositories/modem-dev/hunk)[See all](https://trending-repos.com/?period=monthly)\n\n![nextlevelbuilder/ui-ux-pro-max-skill avatar](https://avatars.githubusercontent.com/u/246974152?v=4&s=32)\n\nnextlevelbuilder/ui-ux-pro-max-skill\n114.0k\n\n![heygen-com/hyperframes avatar](https://avatars.githubusercontent.com/u/65394575?v=4&s=32)\n\nheygen-com/hyperframes39.7k\nJavaScript on top\n\n[Developer tools ![DietrichGebert/ponytail avatar](https://avatars.githubusercontent.com/u/137048761?v=4&s=32) DietrichGebert/ponytail97.1k ![esengine/DeepSeek-Reasonix avatar](https://avatars.githubusercontent.com/u/18465053?v=4&s=32) esengine/DeepSeek-Reasonix32.1k ![stablyai/orca avatar](https://avatars.githubusercontent.com/u/127256420?v=4&s=32) stablyai/orca38.6kJavaScript on top](https://trending-repos.com/collections/dev-tools)\n[Databases ![opengeos/GeoLibre avatar](https://avatars.githubusercontent.com/u/129896036?v=4&s=32) opengeos/GeoLibre5.5k ![Pelski/ytzero avatar](https://avatars.githubusercontent.com/u/6849315?v=4&s=32) Pelski/ytzero440 ![thedotmack/claude-mem avatar](https://avatars.githubusercontent.com/u/683968?v=4&s=32) thedotmack/claude-mem89.8kTypeScript on top](https://trending-repos.com/collections/databases)"},"author":{"name":"Trending Repos","bio":"The market terminal for open source — every GitHub repository quoted by momentum, re-priced every four hours from star and fork velocity."},"metadata":{"timestamp":"2026-08-06T15:47:32.114Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/yGKAUnox2Y/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-p970go","shareId":"XysT8bAnWU","platform":"web","url":"https://arrufat.github.io/zignal/","title":"Zig Documentation","shareMode":"full","createdAt":1786030315153,"postedAt":"2026-08-06T15:31:49.616Z","publishedDate":"2026-08-06T15:31:49.616Z","archivedAt":"2026-08-06T15:31:51.787Z","archivedDate":"2026-08-06T15:31:51.787Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/XysT8bAnWU","contentType":"article","content":{"text":"Loading...\n\n## [src]\n\n## Parameters\n\n## Errors\n\nanyerror means the error set is known only at runtime.\n\n## Search Results\n\n## No Results Found\n\nPress escape to exit search and then '?' to see more options.\n\n## Fields\n\n## Types\n\n## Namespaces\n\n## Global Variables\n\n## Values\n\n## Functions\n\n## Error Sets\n\n## Example Usage\n\n## Source Code\n\n## Keyboard Shortcuts\n\n?Show this help dialog"},"author":{"name":"arrufat.github.io","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/aca5caead7305eb6/avatar-77f0a9606759.svg"},"metadata":{"timestamp":"2026-08-06T15:31:49.616Z"},"media":[],"aiComments":[]},{"id":"web-ro4pbv","shareId":"cEyU7BDk0H","platform":"web","url":"https://playground.foliopdf.dev/","title":"Folio Playground — HTML to PDF in your browser","shareMode":"full","createdAt":1786030243839,"postedAt":"2026-08-06T15:30:31.380Z","publishedDate":"2026-08-06T15:30:31.380Z","archivedAt":"2026-08-06T15:30:34.066Z","archivedDate":"2026-08-06T15:30:34.066Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/cEyU7BDk0H","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/uZNk87usHn/media/0.png","contentType":"article","content":{"text":" "},"author":{"name":"playground.foliopdf.dev","bio":"Convert HTML to PDF instantly. No server, no uploads. Open source, Apache 2.0."},"metadata":{"timestamp":"2026-08-06T15:30:31.380Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/uZNk87usHn/media/0.png","type":"image","width":3024,"height":1900}],"aiComments":[]},{"id":"web-b7lfij","shareId":"nNBS89eWbu","platform":"web","url":"https://croftanalytics.com/","title":"Croft Analytics, Marketing Tools, Search Tool, Search Trends, Share of Search, Share of Searching, Marketing Consultancy, Marketing, Brand, Data Science, Branding, How Brands Grow, Growing Brands, Marketing Effectiveness, Marketing ROI, Advertising, Frank Harrison","shareMode":"full","createdAt":1786029709893,"postedAt":"2026-08-06T15:21:42.194Z","publishedDate":"2026-08-06T15:21:42.194Z","archivedAt":"2026-08-06T15:21:45.363Z","archivedDate":"2026-08-06T15:21:45.363Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/nNBS89eWbu","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/4Grnn1BQGF/media/0.webp","contentType":"article","content":{"text":"## ABOUT\n\n#### Croft Analytics\n\nCroft Analytics provides apps and advice for marketers and agencies:\n\nGlobal apps that track trends across social and search platforms - for example see [Share of Searching](https://www.shareofsearching.com/)\n\nAdvice, projects, and reports on marketing and media topics.\n\nMarketing, media, and consumer desk research.\n\n[![Image](https://img1.wsimg.com/isteam/ip/10ab9f80-7651-45a5-b821-244016eea05e/FRANK%20HARRISON.jpg/:/rs=w:365,h:365,cg:true,m/cr=w:365,h:365)](https://www.linkedin.com/in/frharrison/)\n\n#### Frank Harrison\n\nI founded Croft Analytics in 2020 following many years in senior R&D roles at Publicis Groupe companies.\n\nCroft Analytics provides data-driven apps, such as [Share of Searching](https://www.shareofsearching.com/), for tracking brands online.\n\nPreviously I was Global Research Lead at Publicis Media where I worked with agency teams and clients across many categories and count\n\nI founded Croft Analytics in 2020 following many years in senior R&D roles at Publicis Groupe companies.\n\nCroft Analytics provides data-driven apps, such as [Share of Searching](https://www.shareofsearching.com/), for tracking brands online.\n\nPreviously I was Global Research Lead at Publicis Media where I worked with agency teams and clients across many categories and countries.\n\nSince 1996 I have been a Fellow of the UK’s Institute of Practitioners in Advertising (IPA).\n\n## SERVICES\n\n![Croft Analytics Services](data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=)\n\n#### APPS FOR MARKETERS AND AGENCIES\n\nGlobal web tracking tools that analyse and show trends across social and search platforms - see Share of Searching at [https://www.shareofsearching.com](https://www.shareofsearching.com/)\n\n#### ADVICE\n\n- Scientific rules for brand growth\n- Applying brand growth rules to media strategies and executions\n- Creating, using, measuring distinctive assets\n- Maximising physical and mental availability\n- Measuring advertising return on investment\n\n#### RESEARCH AND REPORTS\n\nDesk and primary research projects.\n\n#### SUPPORT\n\nQuick answers to marketing and media questions.\n\n#### SHARE OF SEARCHING\n\nShare of Searching is a global app that charts search trends across Google, YouTube, Bing, and Amazon. Users can view trends in search volumes and % share of search for entered lists of search terms with data back to 2004. Examine search trends in any country or worldwide for brands, companies, events, celebrities, topics etc.\n\nMarketers can compare trends in search interest for all brands in their categories. In many categories a brand’s share of category search can be used to predict its market share. Share of Searching also reveals the search terms related to a brand (or other search term) and the trend in search interest for all related terms.\n\nEnter search terms (such as a list of brands) to see charts of trends in search volumes and % share of search. Filter the data by country and date range. Export charts to PowerPoint and data to Excel.\n\nChart search volumes and share of search trends for terms that are related to an entered search term (for example a brand or topic).\n\n**The value of share of search trends for marketers**\n\nAnalysis of Google search and market share data has shown that, for brands in many categories, a brand’s share of category search volume can be used to predict its market share. The use of share of search data to predict market shares particularly applies where search is a major activity in the process of deciding what to buy. For example in (but not limited to) categories such as airlines, automotive, finance, movies, retail, travel, and electronic goods.\n\n**To start using the app click on the link below**\n\n## ENDORSEMENTS\n\n#### Anne Austin, Business and Non-Fiction Editing Specialist\n\n#### Steve Hyde, CEO & Global Talent Lead, 360xec Executive Search\n\n#### Steve Hyde, CEO & Global Talent Lead, 360xec Executive Search\n\n![Image](data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=)\n\nFrank is one of those people who is just bubbling over with ideas all the time. That mixture of enormous creative energy with a sharp analytical mind is what makes him so good at what he does, and why he's so inventive. Over the years I worked at Zenith, I think I must have lost count of the number of innovations he introduced in terms of data analysis and insights which led to the creation of very valuable proprietary tools for the company. To this, I feel moved to add that he is the very epitome of bonhomie. A delightful fellow with impeccable manners and a sunny demeanour. Who wouldn't want to work with someone like that?\n\n*October 27, 2020*\n\n*Publications &* [*Global Intelligence Manager*](https://www.linkedin.com/in/annejvaustin/)\n\n[*Z*](https://www.linkedin.com/in/annejvaustin/) *enith*\n\n[*2*](https://www.linkedin.com/in/annejvaustin/) *011-2020*\n\n#### Steve Hyde, CEO & Global Talent Lead, 360xec Executive Search\n\n#### Steve Hyde, CEO & Global Talent Lead, 360xec Executive Search\n\n#### Steve Hyde, CEO & Global Talent Lead, 360xec Executive Search\n\n![Image](data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=)\n\nHaving worked with Frank for many years and experienced so much change over that time, Frank remains one of the most knowledgeable and capable executives across the marketing and communications industry. His input has consistently been regarded most highly by Clients we have served and he is probably responsible for more new business wins and more business retention than anyone else I know. His analytical mind and shrewd vision combined with an encyclopaedic knowledge makes him an enormously valuable asset to any business be it as a senior employee or executive consultant. I cannot recommend Frank too highly.\n\n*September 4, 2020*\n\n[*CEO & Global Talent Lead*](https://www.linkedin.com/in/stevehyde1/)\n\n[*360xec Executive Search Ltd.*](https://www.linkedin.com/in/stevehyde1/)\n\n[*2011 – Present*](https://www.linkedin.com/in/stevehyde1/)\n\n#### Madeline Condron, Business Director EMEA, Starcom\n\n#### Steve Hyde, CEO & Global Talent Lead, 360xec Executive Search\n\n#### Neil Ascher, Managing Partner, Midas Exchange, WPP, USA\n\n![Image](data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=)\n\n#### Antony Young, Co-Founder, The Digital Cafe, New Zealand\n\n#### Neil Ascher, Managing Partner, Midas Exchange, WPP, USA\n\n#### Antony Young, Co-Founder, The Digital Cafe, New Zealand\n\n![Image](data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=)\n\nFrank Harrison is a genius. He has been the architect behind ZOOM, MediaDNA and Touchpoints... communication insight and analytic innovations that have helped to deliver a competitive advantage to the ZenithOptimedia Worldwide network. In addition to being wonderfully strategic, he has that rare quality to be able to present highly complex and technical material into simply, commercial and vibrant ideas. He's does this without an ego or political bone in his body. I hugely valued what he brought to our agency when he reported to me, and he has continued to bring to our Group since stepping into his global role.\n\n*March 23, 2010,*\n\n[*CEO*](https://www.linkedin.com/in/antonyyoung8/)\n\n[*Optimedia International US Inc.*](https://www.linkedin.com/in/antonyyoung8/)\n\n[*2006 – 2011*](https://www.linkedin.com/in/antonyyoung8/)\n\n#### Terence Chan, Principal, Project Oxygen, Singapore\n\n#### Neil Ascher, Managing Partner, Midas Exchange, WPP, USA\n\n#### Antony Young, Co-Founder, The Digital Cafe, New Zealand\n\n![Image](data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=)\n\nFrank \"Propeller Head\" Harrison was the amazing Brit who carved a bedrock foundation of excellence for Zenithmedia (and ZenithOptimedia), not just in UK, but for the rest of us all over the world. A pioneer of the modern day Media Agency, he pushed, and continues to push, the limits for the media industry in many areas way ahead of his time and peers. His inspired work lives on in all of us fortunate to be touched by it.\n\n*April 12, 2009*\n\n[*Chief Operations Officer*](https://www.linkedin.com/in/terencechan/)\n\n[*Zenith Media Singapore*](https://www.linkedin.com/in/terencechan/)\n\n[*1999 – 2009*](https://www.linkedin.com/in/terencechan/)"},"author":{"name":"Croft Analytics","bio":"Croft Analytics provides brand tracking tools for marketers, including Share of Searching, a search trends tool charting search volumes and % share of search"},"metadata":{"timestamp":"2026-08-06T15:21:42.194Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/4Grnn1BQGF/media/0.webp","type":"image","width":2560,"height":1094}],"aiComments":[]},{"id":"web-t9qwi","shareId":"tpdJw5xTvw","platform":"web","url":"https://www.shareofsearching.com/","title":"Share of Searching – Track Share of Search Trends on Google, YouTube & Amazon","shareMode":"full","createdAt":1786029352716,"postedAt":"2026-08-06T15:20:36.420Z","publishedDate":"2026-08-06T15:20:36.420Z","archivedAt":"2026-08-06T15:15:48.762Z","archivedDate":"2026-08-06T15:15:48.762Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/tpdJw5xTvw","contentType":"article","content":{"text":"“Share of Searching is a fantastic tool that has enabled me to mature the organic search capability for the organisation. It has helped me to discover trends with competitors so we could apply some of this logic to predict what could happen with our brands, post merger. It has been instrumental in measuring brand awareness and organic share of search in our category. I’ve been very impressed with how receptive and responsive the team at SOS are to feedback and they’ve always been able to resolve any issues in a timely manner.”\n\nMona Doncevska — Digital Growth Marketing Manager – Organic Search\n\nAustralian Retirement Trust\n\n“Share of search is a valuable tool for Tempur to evaluate our brand health and make informed decisions about marketing strategies and investments. By tracking our share of search over time, we can get a sense of how our brand is performing relative to our competitors and whether we are gaining or losing mindshare among our audience. We use the Croft Analytics tools to make crucial decisions to continue our media spend or pull back if it does not have the desired effect.”\n\nBen Wilkes — International Marketing Manager\n\nTempur Sealy International\n\n“I use Share of search for market share, product launches, competitor and trend analysis and much more. It has helped me tremendously in taking data driven decisions.”\n\nDaniel Sandvik — CCO\n\nVitamail\n\n“Share of Searching enables us to accelerate time consuming calculations for brand marketing.Share of Searching is not just an important tool, but a valuable witness of our ups and downs.”\n\nMykhailo Ivonin — Head of Brand\n\nTK Services International\n\n“We use Share of Search as one of our key KPIs as there’s more to gaining customer share of mind and consideration than just ad share of voice, the strong relationship to market share in many cases demonstrates this.”\n\nJonathon Quirk — Group Consumer Planning & Insights\n\nInchcape Australia\n\n“We’ve been using the Share of Searching tool for a couple of years now and it has fed many of our strategies, pitches, and client arguments when it comes to how we should approach our media strategy not only in terms of digital but offline as well.The very intuitive and friendly interface, the fact that we can use a lot more queries to compare versus Google Trends, the clear overview of seasonality and the new addition of the YouTube search feature, makes it one of our go - to platforms for research, analysis, and insights.Great work!”\n\nAndrea Andrus — Digital Strategy & New Business Romania\n\nEssenceMediacom\n\n“Share of Searching is a useful tool that allows us to know more about categories or industries.Once the connection between Share Of Search and Share Of Market is established, you could use SOS as a proxy(SOM) to recommend a budget.Since SOM is a metric that takes time to have.”\n\nCarolina Garcia — Senior Data Analyst\n\nPublicis Groupe\n\nWhy Share of Search Matters\n\n### Search behaviour reveals market momentum before sales data does.\n\nShare of Search has become a leading indicator used by marketers, agencies and analysts to understand brand growth, competitive pressure and future market share.\n\nTrusted By\n\nMarketers, Agencies & Researchers\n\nUsed globally to track brand demand, identify emerging competitors and benchmark market momentum."},"author":{"name":"shareofsearching.com","bio":"Visualize and compare brand share of search across Google, YouTube, and Amazon. Share of Searching is your tool for tracking search trends globally"},"metadata":{"timestamp":"2026-08-06T15:20:36.420Z"},"media":[],"aiComments":[]},{"id":"web-o1efs3","shareId":"vvH3Wg4X2h","platform":"web","url":"https://passion.digital/","title":"Digital Marketing Agency London, UK","shareMode":"full","createdAt":1786029337893,"postedAt":"2024-08-13T15:29:50.000Z","publishedDate":"2024-08-13T15:29:50.000Z","archivedAt":"2026-08-06T15:15:31.596Z","archivedDate":"2026-08-06T15:15:31.596Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/vvH3Wg4X2h","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/MHGQqCGFl9/media/0.png","contentType":"article","content":{"text":"## Search has changed and we have changed with it.\n\nYour customers are no longer just searching Google — they’re searching LLMs, social platforms, marketplaces and AI-powered discovery tools. As a search-led digital marketing agency in London, we help ambitious brands show up wherever that discovery happens.\n\n**Full Funnel Performance**\n\nWhen critical thinking, creative thinking and genuine passion come together, problems get solved. Our search expertise spans SEO, PPC, content marketing, paid social, GEO and data analytics — and we bring both rigour and fresh thinking to every one of them, ensuring your brand attracts, engages and converts the right audience at every stage of their journey.\n\nWe help ambitious brands get found, chosen and remembered — wherever discovery happens.\n\n![Image](https://passion.digital/app/uploads/2026/05/problem-solving-1557-x-726-px-1557-x-726-px.jpg)\n\n## Why work with us?\n\nBehind every standout campaign is a team of marketing experts and strategists who live and breathe digital marketing. Great campaigns are powered by great people, their experience, insight and ability to adapt to any challenge. That’s why we don’t just offer services. We are a digital marketing agency in London that builds long term partnerships, and work as an extension of your team to craft digital strategies that drive real business growth.\n\nFull-Funnel Performance\n\nWe don't just do search — we think in full funnels. From search and discovery to final conversion, we connect the dots across all digital marketing channels so your brand performs at every stage of the customer journey.\n\nBuilt to Retain\n\nWe measure success by what we keep. An 8.5 NPS, multi-year retainers, and team tenure well above industry standard — we grow by keeping what matters: our clients and our people.\n\nIndependent care, network scale\n\nMost clients can't have both. We can. As part of Stellar — the AI-native performance network powered by Pixis AI — we bring network-level technology and innovation with the senior attention of an independent digital marketing agency.\n\n## Digital Marketing Services.\n\nWe like to work with ambitious brands who are looking for a digital marketing agency in London with a growth mindset and a passion for making a difference. We offer a range of services designed to work together to accelerate growth and drive results.\n\n**Paid Search**\n\nMake every click count. Google Ads and Microsoft Ads work harder by targeting the right keywords, audiences and intent. Get in front of high-intent searchers on Google, Bing and other paid search platforms with data-led [PPC campaigns](https://passion.digital/services/ppc-agency/) that drive clicks, conversions and revenue.\n\n**SEO**\n\n[SEO](https://passion.digital/services/seo-agency/) is more than just rankings; it’s about visibility, authority, and growth. From technical optimisation to content strategy and GEO, we fine-tune every element to ensure your brand stands out in search and LLMS, drives organic traffic, and reaches the right audience at the right time.\n\n**Generative Engine Optimisation (GEO)**\n\nWith **60% of searches** now ending without a click, your customers are finding answers in AI overviews, ChatGPT, and Perplexity – not only on your website. We ensure your brand appears in both traditional search results and AI-powered answers with [Generative Engine Optimisation (GEO)](https://passion.digital/services/ai-search/).\n\n**Paid Social**\n\nEngage with your target audience on popular [social media platforms](https://passion.digital/services/paid-media/social-media-advertising/), amplifying your brand awareness and driving high-quality leads through targeted ads.\n\n**Content Marketing**\n\nGreat [content](https://passion.digital/services/seo-agency/content-marketing/) isn’t just seen. It’s remembered. From blogs and landing pages to social, digital PR, infographics and social media, we create content that fuels SEO, engages audiences and builds brand authority. Every piece is crafted with search intent, user behaviour, and platform trends in mind.\n\n**Programmatic**\n\nSmarter ads, bigger impact with [progammatic](https://passion.digital/services/paid-media/display-advertising/). AI-driven digital advertising ensures brands stay visible across display networks, native ads, connected TV and digital out-of-home (DOOH). Automated bidding, audience segmentation and real-time optimisation put the right message in front of the right people at scale.\n\n**Conversion Rate Optimisation**\n\nImprove your website’s performance and increase sales by optimising user experience and streamlining the customer journey.\n\n**Amazon Advertising**\n\nIncrease product visibility and sales on the world’s largest e-commerce platform, effectively targeting ready-to-buy customers.\n\n**Retail Media**\n\nMaximise your brand’s presence within leading online retail environments, driving both online and offline sales with highly targeted ads.\n\n**Connected TV**\n\nCaptivate a modern audience with engaging ads on smart TVs, combining the reach of traditional TV with the precision of digital marketing.\n\n## Our Clients.\n\n- **View all:**\n- [Case studies](https://passion.digital/case-studies/)\n[Securing 15 Featured Snippets & 21 AI Overviews Shoosmiths Serious Injury See how we did it](https://passion.digital/case-studies/shoosmiths-serious-injury-seo-case-study/)\n\n[Increasing lead volume by +1,420% See how we did it](https://passion.digital/case-studies/knight-frank-vendor-paid-media/)\n\n[A 201% increase in organic generic clicks & 425% increase in paid leads See how we did it](https://passion.digital/case-studies/cloud-gateway-seo-and-paid-media-case-study/)\n\nPassion put so much time and effort into understanding the business**,** the tone of voice and our position in the market**,** they've provided a unique ability to marry a numbers-focused strategy with a content plan**.** I haven't experienced that with another digital marketing agency**.**\n\nThe Thinking Traveller"},"author":{"name":"Passion Digital®","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/538dae7a99f29cac/avatar-04c806e65c16.png","bio":"Digital Marketing Agency based in London. Award-winners in Search Marketing: SEO, PPC, Social, Data & Analytics"},"metadata":{"timestamp":"2024-08-13T15:29:50.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/MHGQqCGFl9/media/0.png","type":"image","width":400,"height":217}],"aiComments":[]},{"id":"web-pj3051","shareId":"ciVlv0OihQ","platform":"web","url":"https://open-design.ai/","title":"Best Open Source Claude Design Alternative","shareMode":"full","createdAt":1786029045960,"postedAt":"2026-08-06T15:10:37.106Z","publishedDate":"2026-08-06T15:10:37.106Z","archivedAt":"2026-08-06T15:10:39.677Z","archivedDate":"2026-08-06T15:10:39.677Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/ciVlv0OihQ","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/I53IaFL5z7/media/0.png","contentType":"article","content":{"text":"## Open Design is the open-source, agentic vibe design workspace — it turns the coding agent you already run into a design engine whose output you fully own. When an LLM first proved it can truly design — real design work, not just copy — that capability arrived closed, hosted, and model-locked. Open Design opens it up: local, BYOK, Apache-2.0.\n\n![Design happens on the desktop. Local files, Figma exports, and code repos are directly readable, and the agent has full terminal-execution power.](https://open-design.ai/about/desktop-native.webp)\n\nDesign happens on the desktop.\nLocal files, Figma exports, and code repos are directly readable, and the agent has full terminal-execution power.\n\n![The Claude Code / Codex / Cursor on your machine are already strong enough. Open Design wires them into a complete design workflow.](https://open-design.ai/about/access-agent.webp)\n\nThe Claude Code / Codex / Cursor on your machine are already strong enough.\nOpen Design wires them into a complete design workflow.\n\n![Every choice settles into a design system, preferences, and memory, so the next generation lands closer to what you want.](https://open-design.ai/about/self-evolution.webp?v=4)\n\nEvery choice settles into a design system, preferences, and memory, so the next generation lands closer to what you want.\n\n## From idea to prototype, web, slides, and HTML video\n\nBrief → Template → Visual direction → Artifact → Memory\n\n1. 01Choose a starting point\n2. 02Set the visual direction\n3. 03Generate the artifact\n4. 04Deliver or make a video\n\n![Image](https://open-design.ai/step-cards/step-1.webp?v=8)\n\nDescribe your goal in one line, or start from a template / plugin.\n\n![Image](https://open-design.ai/step-cards/step-2.webp?v=8)\n\nOnce a direction is set, palette, type, and spacing flow into generation automatically.\n\n![Image](https://open-design.ai/step-cards/step-3.webp?v=7)\n\nThe agent reads all context, produces real runnable files, and previews and edits them live in a sandbox.\n\n![Image](https://open-design.ai/step-cards/step-4.webp?v=9)\n\nExport it to engineering to keep building, or turn it into a marketing video with HyperFrames.\n\n## What can you make with Open Design?\n\nPrototypes, landing pages, slide decks, dashboards, brand and design systems — even HTML video. Open Design turns each into real, runnable files, driven by your own coding agent and ready to ship or hand off to engineering.\n\n[Prototype](https://open-design.ai/plugins/templates/prototype/) [Live Artifact](https://open-design.ai/plugins/templates/live-artifact/) [Slides](https://open-design.ai/plugins/templates/deck/) [Image](https://open-design.ai/plugins/templates/image/) [HyperFrames](https://open-design.ai/plugins/templates/hyperframes/) [Video](https://open-design.ai/plugins/templates/video/)\n\n## Plug in 21+ coding agents, zero config\n\nEvery stage is iterative, visual, and research-driven — composable files, not opaque prompts.\n\n![Devin](https://open-design.ai/agent-icons/devin.png)\n\n![Aider](https://open-design.ai/agent-icons/aider.png)\n\n![Trae](https://open-design.ai/agent-icons/trae-cli.png)\n\n![21+ Coding Agent](https://open-design.ai/method-coding-agent.png)\n\n## From around the world, 389 contributors are building Open Design together\n\n## Open Design FAQ — open source, local-first, and Claude Design alternative\n\n84.1K+ stars · Apache-2.0 · Free\n\n1. 01What is Open Design?Open Design is the official open-source AI design workspace from the nexu-io/open-design project. It turns a local coding agent — Claude Code, Codex, Cursor, Gemini CLI, OpenCode, or Qwen — into a design engine driven by composable skills and portable DESIGN.md systems.\n2. 02Is Open Design official?Yes. The canonical project lives at https://open-design.ai/ and the source is on GitHub at https://github.com/nexu-io/open-design. \"Open Design\", \"OpenDesign\", \"open-design\", and \"Open Design AI\" all refer to this same project.\n3. 03How is Open Design different from Claude Design?Claude Design is a hosted product tied to a single vendor. Open Design is local-first, open source under Apache-2.0, and BYOK: you bring your own agent, credentials, and DESIGN.md system.\n4. 04Is Open Design an open-source Claude Design alternative?Yes — Open Design is the open-source, local-first Claude Design alternative. Where Claude Design is closed, hosted, and locked to Anthropic models, Open Design is Apache-2.0, runs on your own machine, and is BYOK, so you drive it with Claude Code, Codex, Cursor, Gemini, OpenCode, or Qwen and keep every artifact as files you own. [Learn more →](https://open-design.ai/alternatives/claude-design/)\n5. 05What is a vibe design workspace?A vibe design workspace is where you design by describing intent to an AI agent — from prompt to prototype, web page, slides, or HTML video — instead of hand-placing every element. Open Design is an open-source, agent-native vibe design workspace: it wires the coding agent you already run into a full design workflow, so one tool takes you from a rough idea to production-ready output you own. [Learn more →](https://open-design.ai/blog/what-is-vibe-design/)\n6. 06Does Open Design run locally?Yes. The desktop app, daemon, and skill runtime run on your machine. Generated artifacts land in your project directory instead of being forced through a vendor cloud.\n7. 07Which coding agents does Open Design support?Open Design ships 17 first-party BYOK adapters out of the box: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot CLI, Grok, Hermes, Kimi, Devin for Terminal, OpenCode, Qwen, DeepSeek, Pi, Mistral Vibe, Kiro, Kilo, and Qoder. Any adapter that speaks the same skill protocol works — switching agents is a config change, not a redesign.\n8. 08Can I self-host or fork it?Yes. The code is Apache-2.0. You can fork the repo, edit skills, add your own DESIGN.md systems, or run the daemon on your own machines.\n9. 09Is my data sent to Anthropic, OpenAI, or Google?Only your prompt and skill context goes to whichever provider you bring keys for (BYOK). Open Design has no server of its own — the daemon talks to your provider directly. Generated artifacts land as files in your project directory, not in any vendor cloud.\n10. 10Can I run Open Design without installing the CLI or desktop app?Not today. Open Design is local-first by design — the minimum is a local daemon plus an agent (Claude Code, Codex, Cursor, Gemini CLI, or one of the 17 supported adapters). A hosted sandbox is on the roadmap but not the priority: artifacts in your repo beat documents in someone else's database.\n11. 11How much does Open Design cost?The product is free and Apache-2.0 — there is no Open Design subscription. You pay the API costs of whichever provider you use (Anthropic, OpenAI, Google, Mistral, xAI, Moonshot, etc.), billed directly to your account. BYOK keeps both the credentials and the spend on your side of the line.\n12. 12Can I self-host Open Design on Vercel, Cloudflare, or my own server?Yes. The daemon runs anywhere Node 24 runs, and the landing page is a static Astro build that deploys to Cloudflare Pages, Vercel, or Netlify as-is. Teams running shared deployments typically pin the daemon to a machine inside their network and point each developer's CLI at it.\n13. 13How do I move my brand into Open Design?Drop a screenshot or a Figma export into the web UI and ask your agent to extract a brand into a DESIGN.md file. Save that file under design-systems// in your repo; every skill then renders in that brand without re-prompting. /alternatives/claude-design/ describes the same flow in step form.\n14. 14Can I switch agents without redoing my work?Yes. Skills and DESIGN.md systems are agent-agnostic — the same SKILL.md file renders against Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, Grok, Hermes, Qwen, or any other supported adapter. Switching agents is a config change in the daemon, not a redesign.\n15. 15What's on the Open Design roadmap?The live roadmap is at docs/roadmap.md in the GitHub repo, and weekly release notes ship through GitHub Releases. Major themes for the next quarter: more agent adapters, richer template families (3D, video, audio), and an optional shared-daemon mode for design teams."},"author":{"name":"Open Design","bio":"Open-source vibe design workspace & Claude Design alternative — build prototypes, landing pages, dashboards, slides & HTML video with your own coding agent."},"metadata":{"timestamp":"2026-08-06T15:10:37.106Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/I53IaFL5z7/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-mea7is","shareId":"dKFooGxprS","platform":"web","url":"https://ccswitch.io/en/changelog","title":"CC Switch 官方网站 - AI 编程 CLI 统一管理工具","shareMode":"full","createdAt":1786028752219,"postedAt":"2026-08-06T15:05:46.851Z","publishedDate":"2026-08-06T15:05:46.851Z","archivedAt":"2026-08-06T15:05:48.894Z","archivedDate":"2026-08-06T15:05:48.894Z","viewCount":6,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/dKFooGxprS","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/i8fD0l92D6/media/0.png","contentType":"article","content":{"text":"## CC Switch 官方网站\n\nCC Switch 是开源的 AI 编程 CLI 统一管理工具，支持 Claude Code、Codex、Gemini CLI、OpenCode、OpenClaw 和 Hermes Agent。\n\n[GitHub 官方仓库](https://github.com/farion1231/cc-switch) · [官方文档](https://ccswitch.io/zh/docs) · [更新日志](https://ccswitch.io/zh/changelog)"},"author":{"name":"CC Switch Team","bio":"统一管理 Claude Code、Codex、Gemini CLI、OpenCode、OpenClaw 和 Hermes Agent 的供应商配置、本地路由、MCP、Skills、会话与用量统计。"},"metadata":{"timestamp":"2026-08-06T15:05:46.851Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/i8fD0l92D6/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-pj7raw","shareId":"QSraCgmCH9","platform":"web","url":"https://github.com/EcoPasteHub/EcoPaste","title":"EcoPasteHub/EcoPaste: 🎉跨平台的剪贴板管理工具 | Cross-platform clipboard management tool","shareMode":"full","createdAt":1786028390665,"postedAt":"2026-08-06T14:59:44.211Z","publishedDate":"2026-08-06T14:59:44.211Z","archivedAt":"2026-08-06T14:59:46.768Z","archivedDate":"2026-08-06T14:59:46.768Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/QSraCgmCH9","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/C8iNNyUn2z/media/0.png","contentType":"article","content":{"text":"[![EcoPaste](https://github.com/EcoPasteHub/EcoPaste/raw/master/public/logo.png)](https://github.com/EcoPasteHub/EcoPaste/blob/master/public/logo.png)\n\n## EcoPaste\n\n**A local-first clipboard manager for macOS and Windows.**\n\nEnglish | [简体中文](https://github.com/EcoPasteHub/EcoPaste/blob/master/README.zh-CN.md)\n\n[![Tauri v2](https://camo.githubusercontent.com/a4da00fc25acb305ecdde62191c55534d580236470932a83625aca384805eb49/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f54617572692d76322d3234633864623f7374796c653d666c61742d737175617265)](https://camo.githubusercontent.com/a4da00fc25acb305ecdde62191c55534d580236470932a83625aca384805eb49/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f54617572692d76322d3234633864623f7374796c653d666c61742d737175617265) [![Rust first](https://camo.githubusercontent.com/3a42e549f44704423b36898fe924c0c26299382d366a66c01d01c9b8256ec971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f527573742d66697273742d6237343130653f7374796c653d666c61742d737175617265)](https://camo.githubusercontent.com/3a42e549f44704423b36898fe924c0c26299382d366a66c01d01c9b8256ec971/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f527573742d66697273742d6237343130653f7374796c653d666c61742d737175617265) [![React 19](https://camo.githubusercontent.com/0a5a72e2d162a8554bea2c5a421e182239ea54e068627e99274bdc99145dd960/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f52656163742d31392d3631646166623f7374796c653d666c61742d737175617265)](https://camo.githubusercontent.com/0a5a72e2d162a8554bea2c5a421e182239ea54e068627e99274bdc99145dd960/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f52656163742d31392d3631646166623f7374796c653d666c61742d737175617265) [![macOS](https://camo.githubusercontent.com/7ce59ab3ed4ee825112b749d6af6b36054cb75abd668c9c26117ef470d875325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6d61634f532d737570706f727465642d3030303030303f7374796c653d666c61742d737175617265266c6f676f3d6170706c65266c6f676f436f6c6f723d7768697465)](https://camo.githubusercontent.com/7ce59ab3ed4ee825112b749d6af6b36054cb75abd668c9c26117ef470d875325/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6d61634f532d737570706f727465642d3030303030303f7374796c653d666c61742d737175617265266c6f676f3d6170706c65266c6f676f436f6c6f723d7768697465) [![Windows](https://camo.githubusercontent.com/266620c2b31434191ed05909035c4902dd6c257dc789544444841bd02d746ca7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f57696e646f77732d737570706f727465642d3030373864343f7374796c653d666c61742d737175617265266c6f676f3d77696e646f7773266c6f676f436f6c6f723d7768697465)](https://camo.githubusercontent.com/266620c2b31434191ed05909035c4902dd6c257dc789544444841bd02d746ca7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f57696e646f77732d737570706f727465642d3030373864343f7374796c653d666c61742d737175617265266c6f676f3d77696e646f7773266c6f676f436f6c6f723d7768697465)\n\n## About\n\nEcoPaste is an open-source desktop clipboard manager built with a Rust-first Tauri architecture: durable behavior lives in Rust, while the React frontend focuses on rendering and interaction.\n\nThe app is designed to be fast, lightweight, and maintainable, with local storage, SQLite search, native shortcuts, tray integration, backup support, and a focused cross-platform surface for macOS and Windows.\n\n## Features\n\n- Capture clipboard history for plain text, HTML, RTF, images, files, and folders.\n- Search clipboard content and notes with SQLite FTS5.\n- Filter history by source application and content type.\n- Protect sensitive content by skipping high-confidence secrets such as private keys, service tokens, AWS keys, and JWTs.\n- Preview text, images, and files in a dedicated preview window.\n- Paste, copy, copy as plain text, reveal files, open links, add notes, pin, favorite, delete, and drag items out to other apps.\n- Organize history with favorites, pinned items, notes, custom groups, and configurable item actions.\n- Tune capture order, size limits, retention, display density, list sorting, and window behavior.\n- Export and import `.ecopastebak` backups, including encrypted backup containers.\n- Keep clipboard data, resources, and settings local to your machine.\n\n## Contributing\n\nDevelopment setup, architecture notes, quality checks, and contribution expectations live in the [contribution guide](https://github.com/EcoPasteHub/EcoPaste/blob/master/CONTRIBUTING.md).\n\n## License\n\nEcoPaste is licensed under the [Apache License 2.0](https://github.com/EcoPasteHub/EcoPaste/blob/master/LICENSE)."},"author":{"name":"EcoPasteHub","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/703f220225baa5bc/avatar-6a9577cd4f7f.svg","bio":"🎉跨平台的剪贴板管理工具 | Cross-platform clipboard management tool - EcoPasteHub/EcoPaste"},"metadata":{"timestamp":"2026-08-06T14:59:44.211Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/C8iNNyUn2z/media/0.png","type":"image"}],"aiComments":[]},{"id":"web-esaweh","shareId":"T8DA3zPrMh","platform":"web","url":"https://github.com/OpenCoworkAI/open-codesign","title":"OpenCoworkAI/open-codesign: Open-source Claude Design alternative. One-click import your Claude Code / Codex API key. Prompt → prototype / slides / PDF. Multi-model (Claude, GPT, Gemini, Kimi, GLM, Ollama). BYOK, local-first, MIT.","shareMode":"full","createdAt":1786028385422,"postedAt":"2026-08-06T14:59:35.977Z","publishedDate":"2026-08-06T14:59:35.977Z","archivedAt":"2026-08-06T14:59:40.118Z","archivedDate":"2026-08-06T14:59:40.118Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/T8DA3zPrMh","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/7HIe3478aa/media/0.png","contentType":"article","content":{"text":"## Open CoDesign\n\n**简体中文**: [README.zh-CN.md](https://github.com/OpenCoworkAI/open-codesign/blob/main/README.zh-CN.md)\n\n\n\n> Your prompts. Your model. Your laptop. Turn prompts into polished artifacts — locally, openly, and with whichever model you already pay for.\n\n[Website](https://opencoworkai.github.io/open-codesign/) · [Quickstart](#quickstart) · [What's new](https://github.com/OpenCoworkAI/open-codesign/releases) · [Changelog](https://github.com/OpenCoworkAI/open-codesign/blob/main/CHANGELOG.md) · [Discussions](https://github.com/OpenCoworkAI/open-codesign/discussions) · [Docs](https://opencoworkai.github.io/open-codesign/quickstart) · [Contributing](https://github.com/OpenCoworkAI/open-codesign/blob/main/CONTRIBUTING.md) · [Security](https://github.com/OpenCoworkAI/open-codesign/blob/main/SECURITY.md)\n\n**Open-source alternative to:** [Claude Design](https://opencoworkai.github.io/open-codesign/claude-design-alternative) · [v0 by Vercel](https://opencoworkai.github.io/open-codesign/v0-alternative) · [Lovable](https://opencoworkai.github.io/open-codesign/lovable-alternative) · [Bolt.new](https://opencoworkai.github.io/open-codesign/bolt-alternative) · [Figma AI](https://opencoworkai.github.io/open-codesign/figma-ai-alternative)\n\n[![Open CoDesign — prompt on the left, live artifact on the right](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/product-hero.png)](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/product-hero.png)\n\n[![GitHub release](https://camo.githubusercontent.com/3c1084543d2cf93659568f6edeed7a880d3f67617769beef7f6bccc192337c0e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f4f70656e436f776f726b41492f6f70656e2d636f64657369676e3f6c6162656c3d72656c6561736526636f6c6f723d633936343432)](https://github.com/OpenCoworkAI/open-codesign/releases) [![License](https://camo.githubusercontent.com/b8cadaa967891081f8f165695470689986c028821dd8a040132f6e661795dc0d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d4d49542d626c7565)](https://github.com/OpenCoworkAI/open-codesign/blob/main/LICENSE) [![CI](https://camo.githubusercontent.com/a144ee3138a9f895f780933205db0983e67903d7da20eaba4d527c72a562f7f8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f616374696f6e732f776f726b666c6f772f7374617475732f4f70656e436f776f726b41492f6f70656e2d636f64657369676e2f63692e796d6c3f6c6162656c3d4349)](https://github.com/OpenCoworkAI/open-codesign/actions) [![Stars](https://camo.githubusercontent.com/d51ed738e22de84aa5dff5b3ca160850adc7bd13de83d84453f6ef9eeb764b43/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4f70656e436f776f726b41492f6f70656e2d636f64657369676e3f7374796c653d736f6369616c)](https://github.com/OpenCoworkAI/open-codesign/stargazers) [![WeChat Group](https://camo.githubusercontent.com/de28abebc807f18bfaef6d860fee91aa4a51c022032ce52686c3fc2a8405b8b2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5765436861742d47726f75702d3037433136303f6c6f676f3d776563686174266c6f676f436f6c6f723d7768697465)](#community)\n\n[![Last commit](https://camo.githubusercontent.com/9e75a55958514feba993243e6156ef8e21ec46c6e44523c85e5668ee32f19cdc/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6173742d636f6d6d69742f4f70656e436f776f726b41492f6f70656e2d636f64657369676e3f6c6162656c3d6c617374253230636f6d6d697426636f6c6f723d343062346131)](https://github.com/OpenCoworkAI/open-codesign/commits/main) [![Commit activity](https://camo.githubusercontent.com/88e9b98190ddf2d08ad340ec867f4ce463be68c5211b34c75de2556e2d997606/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6d6d69742d61637469766974792f6d2f4f70656e436f776f726b41492f6f70656e2d636f64657369676e3f6c6162656c3d636f6d6d6974732532466d6f6e7468)](https://github.com/OpenCoworkAI/open-codesign/pulse) [![Contributors](https://camo.githubusercontent.com/34e76e792ee9adce3e95d56b47ddda75c594aa8ee1f1922fede47d1a49b38485/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f4f70656e436f776f726b41492f6f70656e2d636f64657369676e)](https://github.com/OpenCoworkAI/open-codesign/graphs/contributors) [![Downloads](https://camo.githubusercontent.com/2131faa23b97469e19ffef35f27553cbe0ce8133a7b405a738a80f6149ff7bfa/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f4f70656e436f776f726b41492f6f70656e2d636f64657369676e2f746f74616c3f6c6162656c3d646f776e6c6f61647326636f6c6f723d366335636537)](https://github.com/OpenCoworkAI/open-codesign/releases)\n\n`claude-code` · `claude-design-alternative` · `v0-alternative` · `bolt-alternative` · `lovable-alternative` · `figma-alternative` · `ai-design` · `design-to-code` · `prompt-to-design` · `ai-prototyping` · `desktop-design-tool` · `byok` · `local-first` · `multi-model` · `electron`\n\n\n\n---\n\n\n\n## What's new\n\n- **`feat/decompose-to-ui-kit`** *(branch)* — Image -> componentized `ui_kits//` bundle for coding-agent handoff · Boolean-per-dimension visual parity judge (12 standard checks) · Verify-and-iterate loop · Per-decompose cost row · See [BENCHMARKS.md](https://github.com/OpenCoworkAI/open-codesign/blob/main/BENCHMARKS.md). Refs [#225](https://github.com/OpenCoworkAI/open-codesign/issues/225).\n- **v0.2.0** *(2026-05-09)* — Agentic Design: workspace-backed sessions · permissioned local tools · Files panel upgrades · provider diagnostics · security hardening · `DESIGN.md` design systems\n- **v0.1.4** *(2026-04-23)* — AI image generation · ChatGPT Plus/Codex subscription support · CLIProxyAPI one-click import · API config hardening\n- **v0.1.3** *(2026-04-21)* — Gemini `models/` prefix fix · OpenAI-compatible relay \"instructions required\" fix · third-party relay SSE-truncation hint\n- **v0.1.2** *(2026-04-21)* — Release pipeline · Homebrew / winget / Scoop packaging manifests [Full release history →](https://github.com/OpenCoworkAI/open-codesign/releases) · [Changelog →](https://github.com/OpenCoworkAI/open-codesign/blob/main/CHANGELOG.md)\n\n\n\n---\n\n\n\n## What it is\n\nTurn a prompt into a polished prototype, slide deck, or marketing asset, locally, with the model you already use.\n\n**Open CoDesign is the open-source Claude Design alternative** — built for people who want the speed of AI-native design tools without subscription lock-in, cloud-only workflows, or being forced onto a single provider. An MIT-licensed desktop app, local-first from day one, with BYOK for any model (Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Ollama, or any OpenAI-compatible endpoint) plus direct ChatGPT Plus / Pro / Team subscription sign-in for Codex models. One-click import of existing Claude Code or Codex provider configs, or one-click ChatGPT sign-in, gets you running in under 90 seconds.\n\n\n\n---\n\n\n\n## See it generate\n\nFrom a blank prompt to a finished artifact, the agent plans, writes, self-checks, and ships something with hover states, tabs, and empty states already wired up:\n\n[![Generate a design from scratch](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/generate-from-scratch.gif)](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/generate-from-scratch.gif)\n\n\n\n---\n\n\n\n## Why people star it\n\n- **Runs on your laptop** — no mandatory cloud workspace\n- **Works with your model** — Claude, GPT, Gemini, Ollama, OpenRouter, and more\n- **Exports real files** — HTML, PDF, PPTX, ZIP, Markdown\n- **Shows its work** — live agent activity, visible tool calls, interruptible generation\n\n---\n\n\n\n## Why Open CoDesign?\n\nOpen source, desktop-native, and built for people who do not want their design workflow locked to one model or one cloud.\n\n\n\n| **Open CoDesign** | Claude Design | v0 by Vercel | Lovable |\n| --- | --- | --- | --- |\n| Open source | ✅ MIT | ❌ Closed | ❌ Closed | ❌ Closed |\n| Desktop native | ✅ Electron | ❌ Web only | ❌ Web only | ❌ Web only |\n| Bring your own key | ✅ Any provider | ❌ Anthropic only | ❌ Vercel only | ⚠️ Limited |\n| Local / offline | ✅ Fully local app | ❌ Cloud | ❌ Cloud | ❌ Cloud |\n| Models | ✅ 20+ (Claude, GPT, Gemini, Ollama…) | Claude only | GPT-4o | Multi-LLM |\n| Version history | ✅ Local sessions + workspace files | ❌ | ❌ | ❌ |\n| Data privacy | ✅ On-device app state | ❌ Cloud-processed | ❌ Cloud | ❌ Cloud |\n| Editable export | ✅ HTML, PDF, PPTX, ZIP, Markdown | ⚠️ Limited | ⚠️ Limited | ⚠️ Limited |\n| Price | ✅ Free app, provider/subscription cost only | 💳 Subscription | 💳 Subscription | 💳 Subscription |\n\n\n\n---\n\n\n\n## Highlights\n\n\n\n| ![Click any element, leave a pin, let the model rewrite that region](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/comment-mode.png) **Comment, don’t retype.** Click any element, drop a pin, and let the model rewrite only that region. | ![AI-emitted tweaks panel with color pickers and RGB inputs](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/tweaks-sliders.png) **AI-tuned sliders.** The model surfaces the parameters worth tweaking, so you can refine color, spacing, and typography without another full prompt. |\n| --- | --- |\n| ![Your Designs hub, filled with real generated artifacts](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/hub-your-designs.png) **Every iteration, kept.** Designs are saved locally, with instant switching between recent versions. | ![Live agent panel showing todos and streaming tool calls](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/agent-panel.png) **Watch the agent work.** Todos, tool calls, and live progress stay visible and interruptible throughout generation. |\n\n\n\n---\n\n\n\n## Quickstart\n\n**Time to first artifact:** about 3 minutes\n\n**Requires:** one API key, ChatGPT subscription sign-in, or local Ollama\n\n**Runs on:** macOS 12+ (Monterey or later), Windows 10+, Linux (glibc ≥ 2.31)\n\n\n\n### 1. Install\n\n**Package manager** (recommended):\n\n\n\n```\n# macOS\nbrew install --cask opencoworkai/tap/open-codesign\n\n# Windows — Scoop\nscoop bucket add opencoworkai https://github.com/OpenCoworkAI/scoop-bucket\nscoop install opencoworkai/open-codesign\n```\n\n**Or direct download** from the [v0.2.0 GitHub Release](https://github.com/OpenCoworkAI/open-codesign/releases/tag/v0.2.0):\n\n\n\n| Platform | File |\n| --- | --- |\n| macOS (Apple Silicon) | `open-codesign-*-arm64.dmg` |\n| macOS (Intel) | `open-codesign-*-x64.dmg` |\n| Windows (x64) | `open-codesign-*-x64-setup.exe` |\n| Windows (ARM64) | `open-codesign-*-arm64-setup.exe` |\n| Linux (x64, AppImage) | `open-codesign-*-x64.AppImage` |\n| Linux (x64, Debian/Ubuntu) | `open-codesign-*-x64.deb` |\n| Linux (x64, Fedora/RHEL) | `open-codesign-*-x64.rpm` |\n\nEach release ships with `SHA256SUMS.txt` and a CycloneDX SBOM (`*-sbom.cdx.json`) so you can verify what you downloaded.\n\n**More package managers**\n\n| Manager | Command | Status |\n| --- | --- | --- |\n| Homebrew Cask (macOS) | `brew install --cask opencoworkai/tap/open-codesign` | 🟢 Live |\n| Scoop (Windows) | `scoop bucket add opencoworkai https://github.com/OpenCoworkAI/scoop-bucket && scoop install opencoworkai/open-codesign` | 🟢 Live |\n| winget (Windows) | `winget install OpenCoworkAI.OpenCoDesign` | 🟡 PR submitted; waiting for Microsoft review |\n| Flathub (Linux) | `flatpak install flathub ai.opencowork.codesign` | ⏸ Deferred; needs signed build + AppStream metadata |\n| Snap (Linux) | `snap install --dangerous open-codesign-*.snap` | 🟡 Attached to releases best-effort; Snap Store publish not yet wired |\n\nAfter each stable tag push, CI syncs SHAs back into `packaging/` and publishes downstream Homebrew/Scoop updates when the repo secrets are configured. The first winget submission is in review; once Microsoft accepts the package, future winget bumps can be automated from the release workflow. Every `packaging/*/README.md` documents its own channel.\n\n\n\n> **Unsigned installer note:** installers are not notarized or Authenticode-signed yet. On **macOS Sequoia 15+** right-click → Open no longer bypasses Gatekeeper, and \"Open Anyway\" in System Settings often fails. Reliable one-liner: ``` xattr -cr \"/Applications/Open CoDesign.app\" ``` Then double-click normally. (Older 0.1.x builds are installed as `/Applications/open-codesign.app`.) On **Windows**: SmartScreen → More info → Run anyway. Want a verified build? Compile from source — see [CONTRIBUTING.md](https://github.com/OpenCoworkAI/open-codesign/blob/main/CONTRIBUTING.md).\n\n\n\n### 2. Add a provider\n\nOn first launch, Open CoDesign opens the Settings page. Pick the path that matches how you already use models:\n\n- **ChatGPT subscription** — sign in with ChatGPT to use Codex models without pasting an API key.\n- **API key** — paste Anthropic (`sk-ant-...`), OpenAI (`sk-...`), Google Gemini, OpenRouter, SiliconFlow, DeepSeek, or another supported provider key.\n- **Local / keyless** — use Ollama or an IP-allowlisted OpenAI-compatible gateway. Credentials stay in `~/.config/open-codesign/config.toml` and the ChatGPT OAuth token store under the app config directory. Nothing leaves your machine unless your chosen model route requires it.\n\n\n\n### 3. Type your first prompt\n\nPick one of **fifteen built-in demos** — landing page, dashboard, pitch slide, pricing, mobile app, chat UI, event calendar, blog article, receipt/invoice, portfolio, settings panel, and more — or describe your own. A sandboxed prototype appears in seconds.\n\n\n\n---\n\n\n\n## Bring your stack\n\nAlready using Claude Code or Codex? API-key provider configs import in one click, with no copy-paste and no need to re-enter settings. If you use Codex through ChatGPT subscription login, sign in directly from Settings:\n\n[![Import from Claude Code or Codex in one click](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/claude-code-import.gif)](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/claude-code-import.gif)\n\n\n\n---\n\n\n\n## Built-in taste\n\nGeneric AI tools tend to produce generic output. Open CoDesign ships with **twelve built-in design skill modules** — slide decks, dashboards, landing pages, SVG charts, glassmorphism, editorial typography, heroes, pricing, footers, chat UIs, data tables, and calendars — plus a built-in taste layer that steers the model toward considered typography, purposeful whitespace, and meaningful color.\n\nEvery skill is available in every generation. Before the model writes a line of CSS, it selects the skills that fit the brief and reasons through layout intent, design-system coherence, and contrast, bringing higher-quality design behavior to whichever model you choose.\n\nAdd a `SKILL.md` to any project to teach the model your own taste.\n\n\n\n---\n\n\n\n## What you get\n\n\n\n### Models and providers\n\n- **Unified provider model** — Anthropic, OpenAI, Gemini, DeepSeek, OpenRouter, SiliconFlow, local Ollama, or any OpenAI-compatible relay; keyless (IP-allowlisted) proxies supported\n- **One-click import and sign-in** — bring Claude Code / Codex API-key provider configs across, or sign in with ChatGPT subscription for Codex models\n- **Dynamic model picker** — every provider exposes its real model catalogue, not a hardcoded shortlist\n\n### Generation and editing\n\n- **Prompt → HTML or JSX/React component** prototype, rendered in a sandboxed iframe (vendored React 18 + Babel on-device)\n- **Fifteen built-in demos + twelve design skill modules** — ready-to-edit starting points for common design briefs\n- **Live agent panel** — watch tool calls stream in real time as the model edits files\n- **AI image generation** — opt-in bitmap assets for heroes, product shots, backgrounds, and illustrations via OpenAI, OpenRouter, or signed-in ChatGPT subscription\n- **AI-generated sliders** — the model emits the parameters worth tweaking (color, spacing, font)\n- **Comment mode** — click any element in the preview to drop a pin, leave a note, and let the model rewrite only that region\n- **Decompose to UI Kit** — one click in the chat sidebar emits a `ui_kits//` folder (`index.html` + `components/*.tsx` + `tokens.css` + `manifest.json` + `README.md`) shaped for coding-agent handoff. Built-in deterministic + vision verifiers self-check parity using a 12-question boolean rubric (no floating-point arbitrary scores) and re-iterate on gaps. Per-decompose cost surfaces inline as a toast. See [BENCHMARKS.md](https://github.com/OpenCoworkAI/open-codesign/blob/main/BENCHMARKS.md). [![Decompose to UI Kit — source image vs agent-emitted ui_kit, side-by-side parity check](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/decompose-to-ui-kit.png)](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/screenshots/decompose-to-ui-kit.png) Source image (gpt-image input) on the left, agent-emitted `ui_kit` rendered headlessly on the right. Parity score and status are derived deterministically — `parityScore = passCount / totalChecks` — from the 12-check boolean rubric. Numbers are from a real `e2e-opus-final` run, not a mock. [![Iter-0 → iter-1 reconcile loop with honest score drift](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/decompose-iter-reel.gif)](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/decompose-iter-reel.gif) 4-frame reel from the `e2e-nodebench-iter` run: source → iter-0 (parityScore 0.82, 6 gaps) → iter-1 (parityScore 0.78, 5 gaps) → honest verdict. The agent fixed some gaps and introduced new layout drift; the boolean rubric exposes the regression instead of hiding it. [MP4 version](https://raw.githubusercontent.com/OpenCoworkAI/open-codesign/main/website/public/demos/decompose-iter-reel.mp4).\n- **Generation cancellation** — stop mid-stream without losing prior turns\n\n### Preview and workflow\n\n- **Phone / tablet / desktop preview** — true responsive frames, switch with one click\n- **Files panel** — inspect multi-file artifacts (HTML, CSS, JS) before export\n- **Instant design switching** — the last five designs keep their preview iframes alive, so Hub ↔ Workspace and sidebar navigation stay zero-delay\n- **Connection diagnostic panel** — one-click test for any provider, with actionable errors\n- **Per-generation token counter** — see exactly how many tokens each run cost in the sidebar\n- **Settings with four tabs** — Models, Appearance, Storage, and Advanced\n- **Light + dark themes**, **EN + 简体中文 UI** with live toggle\n\n### Export and packaging\n\n- **Five export formats** — HTML (inlined CSS), PDF (local Chrome), PPTX, ZIP, Markdown\n- **GitHub Release pipeline** — unsigned DMG (macOS), EXE (Windows), AppImage (Linux). Code-signing lands in v0.5 along with opt-in auto-update\n\n---\n\n\n\n## Roadmap\n\nCurrent release: v0.2.0. The current release theme is **Agentic Design**.\n\n\n\n### Now — v0.2.0 shipped\n\nv0.2 turns Open CoDesign from a one-shot generator into a local design agent with a real workspace:\n\n- **Design as session** — every design is a pi session with JSONL history and a workspace folder on disk\n- **Permissioned agent loop** — pi built-ins for read, write, edit, bash, grep, find, and ls, gated by Open CoDesign's permission UI\n- **Design tools on demand** — `ask`, `scaffold`, `skill`, `preview`, `gen_image`, `tweaks`, `todos`, and `done`\n- **`DESIGN.md` as shared memory** — brand tokens and design-system decisions become editable files, not model memory\n- **v0.1 migration path** — existing SQLite designs migrate into workspaces and session history\n\n### Previous — v0.1.4\n\n- **AI image generation** — opt-in bitmap assets through OpenAI image models or OpenRouter image models\n- **ChatGPT Plus / Codex subscription login** — one-click OAuth for users who do not want to paste an API key\n- **CLIProxyAPI one-click import** — auto-detect a running local proxy and bring it into Settings\n- **API config hardening** — clearer relay diagnostics for timeouts, SSE truncation, missing `/models`, and incompatible Messages APIs\n\n### Later — v0.2.x and beyond\n\n- Renderer polish for the new `ask`, `preview`, tweaks, and process-management surfaces\n- Cost transparency — pre-generation estimate + weekly budget (per-generation token count already shipped)\n- Version snapshots + side-by-side diff\n- Three-style parallel exploration\n- Codebase → design system (token extraction)\n- Code-signing (Apple ID + Authenticode) + opt-in auto-update — v0.5\n- Figma layer export — post-1.0 Have a different priority in mind? [Open an issue](https://github.com/OpenCoworkAI/open-codesign/issues/new/choose) or 👍 an existing one — we do read them.\n\n\n\n---\n\n\n\n## Star History\n\n[![Star History Chart](https://camo.githubusercontent.com/c9004eda4e74fe950e8f158b122ebb652bc220a034750c1f380f768e88356b74/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f696d6167653f7265706f733d4f70656e436f776f726b41492f6f70656e2d636f64657369676e26747970653d44617465)](https://star-history.com/#OpenCoworkAI/open-codesign&Date)\n\n\n\n---\n\n\n\n## Built on\n\n- Electron + React 19 + Vite 6 + Tailwind v4\n- `@mariozechner/pi-ai` and `pi-coding-agent` (model/provider and agent-loop primitives)\n- `electron-builder`\n\n## Reporting issues\n\nFound a bug or have a feature request?"},"author":{"name":"OpenCoworkAI","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/9533e0cc6a2ff1ce/avatar-6a9577cd4f7f.svg","bio":"Open-source Claude Design alternative. One-click import your Claude Code / Codex API key. Prompt → prototype / slides / PDF. Multi-model (Claude, GPT, Gemini, Kimi, GLM, Ollama). BYOK, local-first,..."},"metadata":{"timestamp":"2026-08-06T14:59:35.977Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/7HIe3478aa/media/0.png","type":"image","width":1280,"height":640}],"aiComments":[]},{"id":"web-wk23ry","shareId":"9QzB4A1Cb9","platform":"web","url":"https://github.com/ai-shifu/ChatALL","title":"ai-shifu/ChatALL: Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vicuna, Claude, ChatGLM, MOSS, 讯飞星火, 文心一言 and more, discover the best answers","shareMode":"full","createdAt":1786028350733,"postedAt":"2026-08-06T14:59:03.753Z","publishedDate":"2026-08-06T14:59:03.753Z","archivedAt":"2026-08-06T14:59:06.427Z","archivedDate":"2026-08-06T14:59:06.427Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/9QzB4A1Cb9","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/0un2PTfUS9/media/0.png","contentType":"article","content":{"text":"[![Image](https://github.com/ai-shifu/ChatALL/raw/main/src/assets/logo-cover.png)](https://github.com/ai-shifu/ChatALL/blob/main/src/assets/logo-cover.png) **Chat with ALL AI Bots Concurrently, Discover the Best**\n\n[Deutsch](https://github.com/ai-shifu/ChatALL/blob/main/README_DE-DE.md) | English | [Español](https://github.com/ai-shifu/ChatALL/blob/main/README_ES-ES.md) | [Français](https://github.com/ai-shifu/ChatALL/blob/main/README_FR-FR.md) | [Italian](https://github.com/ai-shifu/ChatALL/blob/main/README_IT-IT.md) | [日本語](https://github.com/ai-shifu/ChatALL/blob/main/README_JA-JP.md) | [한국어](https://github.com/ai-shifu/ChatALL/blob/main/README_KO-KR.md) | [Русский](https://github.com/ai-shifu/ChatALL/blob/main/README_RU-RU.md) | [Tiếng Việt](https://github.com/ai-shifu/ChatALL/blob/main/README_VI-VN.md) | [简体中文](https://github.com/ai-shifu/ChatALL/blob/main/README_ZH-CN.md)\n\nhttps://codespaces.new/ai-shifu/ChatALL\n\n\n\n\n## Screenshots\n\n[![Screenshot](https://github.com/ai-shifu/ChatALL/raw/main/screenshots/screenshot-1.png?raw=true)](https://github.com/ai-shifu/ChatALL/blob/main/screenshots/screenshot-1.png?raw=true)\n\n\n\n## Features\n\nLarge Language Models (LLMs) based AI bots are amazing. However, their behavior can be random and different bots excel at different tasks. If you want the best experience, don't try them one by one. ChatALL (Chinese name: 齐叨) can send prompt to several AI bots concurrently, help you to discover the best results. All you need to do is [download, install](https://github.com/ai-shifu/ChatALL/releases) and ask.\n\n\n\n### Is this you?\n\nTypical users of ChatALL are:\n\n- 🤠 **Gurus of LLMs**, who want to find the best answers or creations from LLMs.\n- 🤓 **Researchers of LLMs**, who want to intuitively compare the strengths and weaknesses of various LLMs in different fields.\n- 😎 **Developers of LLM applications**, who want to quickly debug prompts and find the best-performing foundation models.\n\n### Supported bots\n\n\n\n| AI Bots | Web Access | API | Notes |\n| --- | --- | --- | --- |\n| 360 AI Brain | Yes | No API |\n| Baidu ERNIE | No | Yes |\n| Character.AI | Yes | No API |\n| ChatGLM2 6B & 130B | Yes | No API | No Login required |\n| ChatGPT | Yes | Yes | Web Browsing, Azure OpenAI service included |\n| Claude | Yes | Yes |\n| Code Llama | Yes | No API |\n| Cohere Aya 23 | No | Yes |\n| Cohere Command R Models | No | Yes |\n| Copilot | Yes | No API |\n| Dedao Learning Assistant | Coming soon | No API |\n| Falcon 180B | Yes | No API |\n| Gemini | Yes | Yes |\n| Gemma 2B & 7B | Yes | No API |\n| Gradio | Yes | No API | For Hugging Face space/self-deployed models |\n| Groq Cloud | No | Yes |\n| HuggingChat | Yes | No API |\n| iFLYTEK SPARK | Yes | Coming soon |\n| Kimi | Yes | No API |\n| Llama 2 13B & 70B | Yes | No API |\n| MOSS | Yes | No API |\n| Perplexity | Yes | No API |\n| Phind | Yes | No API |\n| Pi | Yes | No API |\n| Poe | Yes | Coming soon |\n| SkyWork | Yes | Coming soon |\n| Tongyi Qianwen | Yes | Coming soon |\n| Vicuna 13B & 33B | Yes | No API | No Login required |\n| WizardLM 70B | Yes | No API |\n| xAI Grok | No | Yes |\n| YouChat | Yes | No API |\n| You | Yes | No API |\n| Zephyr | Yes | No API |\n\nMore is coming. Upvote your favorite bots in [these issues](https://github.com/ai-shifu/ChatALL/labels/more%20LLMs).\n\n\n\n### Note on Web-connected Bot Reliability\n\nWeb-connected AI bots (those marked with \"Web Access\") are inherently less reliable and frequently face stability issues, as service providers regularly update their web interfaces and security measures. These web-based connections rely on reverse engineering and are difficult to maintain, often breaking unexpectedly. For a dependable experience, we strongly recommend using bots that offer API access whenever possible.\n\n\n\n### Other features\n\n- Quick-prompt mode: send the next prompt without waiting for the previous request to complete\n- Save chat history locally, protect your privacy\n- Highlight the response you like, delete the bad\n- Enable/disable any bots at any time\n- Switch between one, two, or three-column view\n- Auto update to the latest version\n- Dark mode (contributed by @tanchekwei)\n- Short keys. Press Ctrl + / to know all of them (contributed by @tanchekwei)\n- Multiple chats (contributed by @tanchekwei)\n- Proxy setting (contributed by @msaong)\n- Prompt management (contributed by @tanchekwei)\n- Supports multiple languages (Chinese, English, German, French, Russian, Vietnamese, Korean, Japanese, Spanish, Italian)\n- Supports Windows, macOS and Linux Planned features:\n\nYou are welcome to contribute to these features.\n\n- Deploy front-end to GitHub Pages\n\n## Privacy\n\nAll chat history, settings and login data are saved locally on your computer.\n\nChatALL collects anonymous usage data to help us improve the product. Including:\n\n- Which AI bots are prompted and how long the prompt is. Not including the prompt content.\n- How long the response is, and which response is deleted/highlighted. Not including the response content.\n\n## Prerequisites\n\nChatALL is a client, not a proxy. Therefore, you must:\n\n1. Have working accounts and/or API tokens for the bots.\n2. Have reliable network connections to the bots.\n\n## Download / Install\n\nDownload from [https://github.com/ai-shifu/ChatALL/releases](https://github.com/ai-shifu/ChatALL/releases)\n\n\n\n### On Windows\n\nJust download the *-win.exe file and proceed with the setup.\n\n\n\n### On macOS\n\nFor Apple Silicon Mac (M1, M2 CPU), download the *-mac-arm64.dmg file.\n\nFor other Macs, download *-mac-x64.dmg file.\n\nIf you are using [Homebrew](https://brew.sh/), you can also install it with:\n\n\n\n```\nbrew install --cask chatall\n```\n\n\n\n### On Linux\n\nDebian-based Distributions: Download the.deb file, double click it and install the software. Arch-based Distributions: You can clone ChatALL from the AUR [here](https://aur.archlinux.org/packages/chatall-bin). You can install it manually or using an AUR helper like yay or paru. Other Distributions: Download the.AppImage file, make it executable, and enjoy the click-to-run experience. You can also use [AppimageLauncher](https://github.com/TheAssassin/AppImageLauncher).\n\n\n\n## Troubleshooting\n\nIf you encounter any problems while using ChatALL, you can try the following methods to resolve them:\n\n1. **Refresh** - press Ctrl + R or ⌘ + R.\n2. **Restart** - exit ChatALL and run it again.\n3. **Re-login** - click the settings button in the upper right corner, then click the corresponding login/logout link to relogin the website.\n4. **Create a new chat** - click the `New Chat` button and send prompt again. If none of the above methods work, you can try **resetting ChatALL**. Note that this will delete all your settings and message history.\n\nYou can reset ChatALL by deleting the following directories:\n\n- Windows: `C:\\Users\\\\AppData\\Roaming\\chatall\\`\n- Linux: `/home//.config/chatall/`\n- macOS: `/Users//Library/Application Support/chatall/` If the problem persists, please [submit an issue](https://github.com/ai-shifu/ChatALL/issues).\n\n\n\n## For developers\n\n\n\n### Contribute a Bot\n\n[The guide](https://github.com/ai-shifu/ChatALL/wiki/%E5%A6%82%E4%BD%95%E6%B7%BB%E5%8A%A0%E4%B8%80%E4%B8%AA%E6%96%B0%E7%9A%84-AI-%E5%AF%B9%E8%AF%9D%E6%9C%BA%E5%99%A8%E4%BA%BA) may help you.\n\n\n\n### Run\n\n\n\n```\nnpm install\nnpm run electron:serve\n```\n\n\n\n### Build\n\nBuild for your current platform:\n\n\n\n```\nnpm run electron:build\n```\n\nBuild for all platforms:\n\n\n\n```\nnpm run electron:build -- -wml --x64 --arm64\n```\n\n\n\n## Credits\n\n\n\n### Contributors\n\n[![Image](https://camo.githubusercontent.com/e7c642f091fb66a5c431181a4459730f0123bc67073e10643b990a108fa3de50/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d61692d73686966752f43686174414c4c)](https://github.com/ai-shifu/ChatALL/graphs/contributors)\n\n### Others\n\n- GPT-4 contributed much of the code\n- ChatGPT, Copilot and Google provide many solutions (ranked in order)\n- Inspired by [ChatHub](https://github.com/chathub-dev/chathub). Respect!\n\n## Sponsor\n\nIf you like this project, please consider:\n\n[![ko-fi](https://camo.githubusercontent.com/201ef269611db7eb6b5d08e9f756ab8980df3014b64492770bdf13a6ed924641/68747470733a2f2f6b6f2d66692e636f6d2f696d672f676974687562627574746f6e5f736d2e737667)](https://ko-fi.com/F1F8KZJGJ)"},"author":{"name":"ai-shifu","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/6767febb76999024/avatar-6a9577cd4f7f.svg","bio":"Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vicuna, Claude, ChatGLM, MOSS, 讯飞星火, 文心一言 and more, discover the best answers - ai-shifu/ChatALL"},"metadata":{"timestamp":"2026-08-06T14:59:03.753Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/0un2PTfUS9/media/0.png","type":"image","width":1200,"height":600}],"aiComments":[]},{"id":"web-og28zw","shareId":"uk9J63NuZG","platform":"web","url":"https://github.com/farion1231/cc-switch","title":"farion1231/cc-switch: A cross-platform desktop All-in-One assistant for Claude Code, Codex, OpenCode, OpenClaw, Grok Build & Hermes Agent. Only official website: ccswitch.io","shareMode":"full","createdAt":1786028311117,"postedAt":"2026-08-06T14:58:23.014Z","publishedDate":"2026-08-06T14:58:23.014Z","archivedAt":"2026-08-06T14:58:26.774Z","archivedDate":"2026-08-06T14:58:26.774Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/uk9J63NuZG","thumbnail":"https://opengraph.githubassets.com/80d53778c5d36f4ca90f9630ab717e5935cbbeceb539bbad9bf8757895ddb0dd/farion1231/cc-switch","contentType":"article","content":{"text":"## CC Switch\n\n\n\n### The All-in-One Manager for Claude Code, Claude Desktop, Codex, Gemini CLI, Grok Build, OpenCode, OpenClaw & Hermes Agent\n\n[![Version](https://camo.githubusercontent.com/6a03fb0fe8bf5fa77f1c916fae4300268924adf146f25e81cbed1f06b8e0f173/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f666172696f6e313233312f63632d7377697463683f636f6c6f723d626c7565266c6162656c3d76657273696f6e)](https://github.com/farion1231/cc-switch/releases) [![Platform](https://camo.githubusercontent.com/1e93b866da33d7e3e202cb3127e9e94e902aa550dee535c19ff793c82f7f7706/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f706c6174666f726d2d57696e646f77732532302537432532306d61634f532532302537432532304c696e75782d6c69676874677265792e737667)](https://github.com/farion1231/cc-switch/releases) [![Built with Tauri](https://camo.githubusercontent.com/6949c837a0f280762fb0e33ef90dc5787e2a576490e6b78ffa78fc0e5c8541a7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6275696c74253230776974682d5461757269253230322d6f72616e67652e737667)](https://tauri.app/) [![Downloads](https://camo.githubusercontent.com/8f72a8802ac400686c753ad3112dd2a9f39d11c9dbf14b3ad363e6c0009085ce/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f646f776e6c6f6164732f666172696f6e313233312f63632d7377697463682f746f74616c)](https://github.com/farion1231/cc-switch/releases/latest)\n\n[![farion1231%2Fcc-switch | Trendshift](https://camo.githubusercontent.com/cea2a498060a4538801356a1fbcfd83ad1bc9eecfd6bb6b7f63d8796f71079d6/68747470733a2f2f7472656e6473686966742e696f2f6170692f62616467652f7265706f7369746f726965732f3135333732)](https://trendshift.io/repositories/15372) [![Star History Rank](https://camo.githubusercontent.com/b0615792ce2dd3c0a97267acc42d4110bad0be097d0c816f0bd50da97348e0ca/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f62616467653f7265706f3d666172696f6e313233312f63632d737769746368)](https://www.star-history.com/#farion1231/cc-switch&Date)\n\n\n\n### 🌐 The Only Official Website: ccswitch.io\n\nEnglish | [中文](https://github.com/farion1231/cc-switch/blob/main/README_ZH.md) | [日本語](https://github.com/farion1231/cc-switch/blob/main/README_JA.md) | [Deutsch](https://github.com/farion1231/cc-switch/blob/main/README_DE.md) | [Changelog](https://github.com/farion1231/cc-switch/blob/main/CHANGELOG.md)\n\n\n\n## ❤️Sponsor\n\n\n\n> [Want to appear here?](mailto:farion1231@gmail.com)\n\nClick to collapse [![Kimi K2.7 Code](https://camo.githubusercontent.com/77272eac5cadedcb59f6942e0a4510cce3546086b6d95f56a14eea65752faa90/68747470733a2f2f6763646e2e6d6f6f6e73686f742e636e2f67726f7774682d63646e2f73706f6e736f722f6b696d692d656e2e706e67)](https://platform.kimi.ai/?track_id=track-20d65732f0aa45dcb1df9691a15610af&aff=cc-switch)\n\nKimi K3 is Moonshot AI's most capable model and the world's first open 3T-class model. With 2.8 trillion parameters, native vision, and a 1-million-token context window, K3 delivers frontier performance across long-horizon coding, knowledge work, and reasoning. CC Switch makes it easy to configure and switch to Kimi across agentic tools. **[Click here to start using Kimi](https://platform.kimi.ai/?track_id=track-20d65732f0aa45dcb1df9691a15610af&aff=cc-switch)**\n\n**New user top-up bonus**: register via [this link](https://platform.kimi.ai/?track_id=track-20d65732f0aa45dcb1df9691a15610af&aff=cc-switch) and complete your first top-up to receive 10% of the amount as bonus API credit, up to CNY ¥1,000.\n\nDoing mostly coding work? Try the **[Kimi Code subscription](https://www.kimi.com/code/?aff=cc-switch)**.\n\n\n\n---\n\n\n\n| ![PackyCode](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/packycode.png) | Thanks to PackyCode for sponsoring this project! PackyCode is a reliable and efficient API relay service provider, offering relay services for Claude Code, Codex, Gemini, and more. PackyCode provides special discounts for our software users: register using this link and enter the \"cc-switch\" promo code during first recharge to get 10% off. |\n| --- | --- |\n| ![ZetaAPI](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/zetaapi-banner.png) | Thanks to ZetaAPI for sponsoring this project! ZetaAPI focuses on real model fidelity, no watered-down responses, no quality degradation, and pricing as low as 35% of official rates. The platform does not mix traffic, secretly replace models with lower-quality alternatives, or use fake model routing. It supports Claude Code, Codex, Gemini, ChatGPT, and other mainstream AI models, helping users significantly reduce API costs while maintaining reliable model quality. At the same time, ZetaAPI provides enterprise-grade SLA-backed stability, standard API compatibility, one API key for multiple models, fast integration, and pay-as-you-go billing, making it suitable for AI products, coding agents, internal business tools, customer service systems, content generation, and automation workflows. If any model is verified to be inconsistent with its stated quality, ZetaAPI backs it with a 10x compensation guarantee, giving users a more stable, transparent, and trustworthy experience. Register via this link and use the promo code CC-SWITCH during your first recharge to enjoy an exclusive 10% discount on your first top-up, just for CC Switch users! |\n| ![APINebula](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/apinebula_banner.png) | Thanks to APINEBULA for sponsoring this project! APINEBULA, an enterprise-grade AI aggregation platform under Galaxy Video Bureau, leverages extensive platform resources to provide developers, teams, and enterprises with stable, cost-effective access to large language model APIs. The platform integrates leading, full-powered models like Claude, GPT, and Gemini, allowing you to connect to the world's top AI models through a single API, with prices starting as low as 10% of the original cost. Designed for AI programming, Agent development, and business system integration, APINEBULA supports enterprise-grade high concurrency, formal contracts, corporate bank transfers, and invoicing services. APINEBULA provides special discounts for our software users: register using this link and enter the **\"ccswitch\"** promo code during your first recharge to get **10% off**. |\n| ![AICodeMirror](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/aicodemirror.jpg) | Thanks to AICodeMirror for sponsoring this project! AICodeMirror provides official high-stability relay services for Claude Code / Codex / Gemini CLI, with enterprise-grade concurrency, fast invoicing, and 24/7 dedicated technical support. Claude Code / Codex / Gemini official channels at 38% / 2% / 9% of original price, with extra discounts on top-ups! AICodeMirror offers special benefits for CC Switch users: register via this link to enjoy 20% off your first top-up, and enterprise customers can get up to 25% off! |\n| ![PatewayAI](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/pateway.png) | Thanks to PatewayAI for sponsoring this project! PatewayAI is an API relay service provider built for heavy AI developers, focused on directly relaying official high-quality model APIs. It offers the full Claude lineup and the Codex series, 100% sourced from official channels — no dilution, no fakes, verification welcome. Billing is transparent and every token-level invoice can be audited line by line. It also supports enterprise-grade concurrency and provides a dedicated management platform for enterprise customers — formal contracts and invoicing are available; visit the official website for contact details. Register now via this link to receive $3 in trial credit. Top-ups go as low as 60% of the original price, with a two-way referral bonus of up to $150! |\n| ![Fenno.ai](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/fenno-banner.png) | Thanks to Fenno.ai for sponsoring this project! Fenno.ai is a stable and efficient API relay service provider, currently focused on Codex relay. It is compatible with the OpenAI and Anthropic protocols and can be flexibly used from Codex, Claude Code, OpenCode, and other mainstream coding tools. It reliably supports enterprise-grade workloads of hundreds of billions of tokens per day, with corporate (B2B) settlement and invoicing for both domestic and overseas entities. Fenno.ai offers an exclusive benefit for CC Switch users: subscribe via this link to the incredible ¥9.9 Coding Plan worth $150 in credits, and earn up to 20% in referral rewards — invite more, earn more! |\n| ![RunAPI](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/runapi.jpg) | Thanks to RunAPI for sponsoring this project! RunAPI is a high-performance and reliable AI model API gateway — one API key gives you access to 150+ mainstream models including OpenAI, Claude, Gemini, DeepSeek, and Grok, with prices as low as 10% of the official rate and excellent stability. It works seamlessly with Claude Code, OpenClaw, and other tools. Exclusive benefit for CC Switch users: register via this link and enjoy a 10% discount on your first top-up! |\n| ![Shengsuanyun](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/shengsuanyun.png) | Thanks to Shengsuanyun for sponsoring this project! Shengsuanyun is a super factory serving AI Native Teams — an industrial-grade AI task parallel execution platform. Its model marketplace aggregates Claude, ChatGPT, Gemini, and other domestic and international LLM and multimedia model capabilities with direct supply. Absolutely no reverse engineering or dilution — platform-wide model SLA availability reaches 99.7%, with monitoring dashboards showing green across the board. It also offers enterprise-grade custom gateways for fine-grained team cost and permission management, smart routing, security protection, and BYOK (Bring Your Own Key) hosting. The platform charges on a pay-per-use and tokens plan (coming soon) basis, with invoicing available. Register via this link as a new user to receive ¥10 in credits plus a 10% bonus on your first top-up. |\n| ![AIGoCode](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/aigocode.png) | Thanks to AIGoCode for sponsoring this project! AIGoCode is an all-in-one platform that integrates Claude Code, Codex, and the latest Gemini models, providing you with stable, efficient, and highly cost-effective AI coding services. The platform offers flexible subscription plans, zero risk of account suspension, direct access with no VPN required, and lightning-fast responses. AIGoCode has prepared a special benefit for CC Switch users: if you register via this link, you'll receive an extra 10% bonus credit on your first top-up! |\n| ![AICoding](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/aicoding.jpg) | Thanks to AICoding for sponsoring this project! AICoding — Global AI Model API Relay Service at Unbeatable Prices! Claude Code at 19% of original price, GPT at just 1%! Trusted by hundreds of enterprises for cost-effective AI services. Supports Claude Code, GPT, Gemini and major domestic models, with enterprise-grade high concurrency, fast invoicing, and 24/7 dedicated technical support. CC Switch users who register via this link get 10% off their first top-up! |\n| ![SubRouter](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/subrouter-banner.png) | Thanks to SubRouter for sponsoring this project! SubRouter is a marketplace and smart routing platform for AI service operators. Merchants can launch operating sites, publish packages, manage users, models, and pricing, while users discover services and access reliable AI models through one unified API. Register via this link! |\n| ![APIKEY.FUN](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/apikey_banner.png) | Thanks to APIKEY.FUN for sponsoring this project! APIKEY.FUN is a professional enterprise-grade AI relay platform dedicated to providing stable, efficient, and low-cost AI model API access for enterprises and individual developers. The platform supports popular mainstream models such as Claude, OpenAI, and Gemini, with prices as low as 7% of official rates. Register through this project's exclusive link to enjoy an exclusive offer of up to **permanent 5% off top-ups**. |\n| ![ClaudeAPI](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/claudeapi.png) | This project is sponsored by Claude API. Direct Claude API access — connect Claude Code and Agent apps in 3 minutes. New users can claim a free trial credit.Powered by official Anthropic API keys + AWS Bedrock official channels. No reverse engineering, no model degradation. Full support for Opus / Sonnet / Haiku model lineup, with official capabilities preserved including Tool Use, 1M context window, and more. Built for Claude Code power users, Agent engineers, and enterprise engineering teams. Invoicing and dedicated team support available. Click here to register! |\n| ![code0.ai](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/code0.png) | Thanks to code0.ai for sponsoring this project! code0.ai is an AI coding service platform built for developers, supporting Claude Code, Codex, Gemini, and other mainstream AI coding capabilities. It helps individual developers and teams use AI Agents more stably and efficiently for coding, debugging, refactoring, and automation workflows. ccswitch users can contact customer support via the code0.ai website to claim test credits and experience a reliable AI coding service. |\n| ![TeamoRouter](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/TeamoRouter-banner.png) | Thanks to TeamoRouter for sponsoring this project! TeamoRouter is an enterprise-grade Agentic LLM gateway built for developers, AI teams, and businesses. Without requiring any subscriptions, it lets you access Claude Code, Codex, Gemini CLI, OpenAI Codex, and other popular AI agents through a single unified API, while offering API pricing at discounts of up to 90%. Unlike typical API relay services, TeamoRouter aggregates hundreds of official model providers and trusted infrastructure partners, including OpenAI, Anthropic, Vertex, Azure, and AWS bedrock. Every provider is verified for 100% Agent protocol compatibility, cache performance, and request traceability, ensuring stable quality instead of reverse-engineered or diluted endpoints. The platform delivers near-official TTFT, 99.6% SLA, enterprise-scale throughput up to 5,000 QPM, and industry-leading cache hit rates that dramatically reduce token costs for long-running agent workflows. TeamoRouter also offers enterprise features including centralized billing, team management, BYOK, smart routing, usage analytics, dynamic provider optimization, and dedicated support. For an even simpler experience, Teamo Desktop lets you use Claude Code, Codex, Gemini CLI, and other popular AI agents with one-click setup—no API key management or manual gateway configuration required. Register via this link as a new user to receive 10% off your first top-up. |\n| ![new-api](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/newapi-banner.png) | Thanks to the open-source AI infrastructure project new-api for its strong support of this project! new-api is an open-source AI infrastructure project from QuantumNous and one of the leading unified LLM access-and-distribution projects by activity and adoption, focused on helping developers, teams, and enterprises build manageable, scalable AI service platforms at lower cost. As a fellow project rooted in the open-source ecosystem, new-api hopes to sponsor and support the continued growth of more outstanding open-source projects. 🌟 Star new-api to show your support: https://github.com/QuantumNous/new-api. Website: https://www.newapi.ai/. |\n| ![ClaudeCN](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/claudecn.jpg) | Thanks to ClaudeCN for sponsoring this project! ClaudeCN is an enterprise-grade AI gateway platform operated by a registered company. It delivers high-availability commercial API access to popular models including Claude, GPT, and DeepSeek, and is built around formal enterprise procurement workflows — corporate bank transfers, signed contracts, and full compliance. Register via this link! |\n| ![BytePlus](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/byteplus.png) | Thanks to Dola seed for sponsoring this project! Dola Seed 2.0 is a full‑modal general large model independently developed by ByteDance for the global market. Built on a unified multimodal architecture, it supports joint understanding and generation of text, images, audio, and video. It natively enables agent collaboration, with strong reasoning, long‑task execution, tool integration, and coding capabilities. It is widely applicable to smart cockpits, personal assistants, education, customer support, marketing, retail, and other scenarios. It excels in multimodal perception, end‑to‑end complex task delivery, stable interaction, and data security, and is readily accessible and deployable via the ModelArk platform.Register via this link to get 500,000 tokens of free inference quota per model. >>中国大陆地区的开发者请点击这里 |\n| ![SiliconFlow](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/silicon_en.jpg) | Thanks to SiliconFlow for sponsoring this project! SiliconFlow is a high-performance AI infrastructure and model API platform, providing fast and reliable access to language, speech, image, and video models in one place. With pay-as-you-go billing, broad multimodal model support, high-speed inference, and enterprise-grade stability, SiliconFlow helps developers and teams build and scale AI applications more efficiently. Register via this link and complete real-name verification to receive ¥16 in bonus credit, usable across models on the platform. SiliconFlow is also now compatible with OpenClaw, allowing users to connect a SiliconFlow API key and call major AI models for free. |\n| ![A6API](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/a6-banner-en.jpg) | Thanks to A6API for sponsoring this project! A6API is a one-stop AI model API aggregation platform covering Claude, GPT, Gemini, Codex, and other mainstream models. Multiple vendors can list their supply on the platform, so the same model can be quoted competitively by several upstream providers. Smart routing automatically picks the more stable, lower-priced route available and fails over automatically, helping you reduce failed requests, cut costs, and improve stability. Whether you are an individual developer, an AI product team, or a studio, you can integrate quickly through a unified interface — compatible with all formats, with low migration cost. New users who register via this link receive free trial credits: try it first, then use it at a low price. |\n| ![Atlas Cloud](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/atlascloud_banner.png) | Atlas Cloud is a full-modal AI inference platform that gives developers a single AI API to access video generation, image generation, and LLM APIs. Instead of managing multiple vendor integrations, you connect once and get unified access to 300+ curated models across all modalities. Check out Atlas Cloud's new coding plan promotion for more budget-friendly API access! |\n| ![Compshare](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/ucloud.png) | Thanks to Compshare for sponsoring this project! Compshare is UCloud's AI cloud platform, providing stable and comprehensive domestic and international model APIs with just one key. Featuring cost-effective monthly and per-use domestic-model Coding Plan packages, alongside stable officially-relayed overseas models. Supports Claude Code, Codex, and API access. Enterprise-grade high concurrency, 24/7 technical support, and self-service invoicing. Users who register via this link will receive a free 5 CNY platform trial credit! |\n| Thanks to CCSub for sponsoring this project! CCSub is a stable, affordable AI API relay platform — your drop-in replacement for a Claude.ai subscription. One API key gives you access to Claude Opus 4.8, Sonnet, Haiku, GPT-5, Gemini, and DeepSeek at roughly 30% of direct API cost, with no VPN required from anywhere in the world. Compatible with Claude Code, Codex, Cursor, Cline, Continue, Windsurf, and all major AI coding tools. Register via this link and get $5 free credit on sign-up. |\n| ![SSSAiCode](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/sssaicode.png) | Thanks to SSSAiCode for sponsoring this project! SSSAiCode is a stable and reliable API relay service, dedicated to providing stable, reliable, and affordable Claude and Codex model services, with same-day fast invoicing. SSSAiCode offers a special deal for CC Switch users: register via this link to enjoy $10 extra credit on every top-up! |\n| Thanks to Micu API for sponsoring this project! Micu API is a global LLM relay service provider dedicated to delivering the best cost-performance ratio with high stability. Backed by a registered enterprise for core assurance, eliminating any risk of service discontinuation, with fast official invoicing support! We champion \"zero cost to try\": top up from as low as ¥1 with no minimum, and get fee-free refunds anytime! Micu API offers an exclusive deal for CC Switch users: register via this link and enter promo code \"ccswitch\" when topping up to enjoy a **10% discount**! |\n| ![RightCode](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/rightcode.jpg) | Thank you to Right Code for sponsoring this project! Right Code reliably provides routing services for models such as Claude Code, Codex, and Gemini, with both pay-as-you-go and monthly subscription billing options available. Invoices are available upon top-up, and enterprise and team users can receive dedicated one-on-one support. Right Code also offers an exclusive discount for CC Switch users: register via this link, and with every top-up you will receive pay-as-you-go credit equivalent to 5% of the amount paid. |\n| ![ETok](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/etok.png) | Thanks to ETok.ai for sponsoring this project! ETok.ai is dedicated to building a one-stop AI programming tool service platform. We offer professional Claude Code packages and technical community services, with support for Google Gemini and OpenAI Codex. Through carefully designed plans and a professional tech community, we provide developers with reliable service guarantees and continuous technical support, making AI-assisted programming a true productivity tool. Click here to register! |\n| ![Cubence](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/cubence.png) | Thanks to Cubence for sponsoring this project! Cubence is a reliable and efficient API relay service provider, offering relay services for Claude Code, Codex, Gemini, and more with flexible billing options including pay-as-you-go and monthly plans. Cubence provides special discounts for CC Switch users: register using this link and enter the \"CCSWITCH\" promo code during recharge to get 10% off every top-up! |\n| ![Crazyrouter](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/crazyrouter.png) | Thanks to Crazyrouter for sponsoring this project! Crazyrouter is a high-performance AI API aggregation platform — one API key for 300+ models including Claude Code, Codex, Gemini CLI, and more. All models at 55% of official pricing with auto-failover, smart routing, and unlimited concurrency. Crazyrouter offers an exclusive deal for CC Switch users: register via this link and contact customer support to claim **$2 free credit**, plus enter promo code `CCSWITCH` on your first top-up for an extra **30% bonus credit**! |\n| ![DMXAPI](https://github.com/farion1231/cc-switch/raw/main/assets/partners/logos/dmx-en.jpg) | Thanks to DMXAPI for sponsoring this project! DMXAPI provides global large model API services to 200+ enterprise users. One API key for all global models. Features include: instant invoicing, unlimited concurrency, starting from $0.15, 24/7 technical support. GPT/Claude/Gemini all at 32% off, domestic models 20-50% off, Claude Code exclusive models at 66% off! Register here |\n\n\n\n## Why CC Switch?\n\nModern AI-powered coding relies on tools like Claude Code, Claude Desktop, Codex, Gemini CLI, Grok Build, OpenCode, OpenClaw, and Hermes — but each has its own configuration format. Switching API providers means manually editing JSON, TOML, or `.env` files, and there is no unified way to manage MCP and Skills across multiple tools.\n\n**CC Switch** gives you a single desktop app to manage all supported AI tools. Instead of editing config files by hand, you get a visual interface to import providers with one click, switch between them instantly, with 50+ built-in provider presets, unified MCP and Skills management, and system tray quick switching — all backed by a reliable SQLite database with atomic writes that protect your configs from corruption.\n\n- **One App, Eight Tools** — Manage Claude Code, Claude Desktop, Codex, Gemini CLI, Grok Build, OpenCode, OpenClaw, and Hermes from a single interface\n- **No More Manual Editing** — 50+ provider presets including AWS Bedrock, NVIDIA NIM, and community relays; just pick and switch\n- **Unified MCP & Skills Management** — One panel to manage MCP servers and Skills across Claude, Codex, Gemini, Grok Build, OpenCode, and Hermes with bidirectional sync\n- **System Tray Quick Switch** — Switch providers instantly from the tray menu, no need to open the full app\n- **Cloud Sync** — Sync provider data across devices via Dropbox, OneDrive, iCloud, or WebDAV servers\n- **Cross-Platform** — Native desktop app for Windows, macOS, and Linux, built with Tauri 2\n- **Built-in Utilities** — Includes various utilities for first-launch login confirmation, signature bypass, plugin extension sync, and more\n\n## Screenshots\n\n\n\n| Main Interface | Add Provider |\n| --- | --- |\n| ![Main Interface](https://github.com/farion1231/cc-switch/raw/main/assets/screenshots/main-en.png) | ![Add Provider](https://github.com/farion1231/cc-switch/raw/main/assets/screenshots/add-en.png) |\n\n\n\n## Features\n\n[Full Changelog](https://github.com/farion1231/cc-switch/blob/main/CHANGELOG.md) | [Release Notes](https://github.com/farion1231/cc-switch/blob/main/docs/release-notes/v3.16.1-en.md)\n\n\n\n### Provider Management\n\n- **8 supported tools, 50+ presets** — Claude Code, Claude Desktop, Codex, Gemini CLI, Grok Build, OpenCode, OpenClaw, Hermes; copy your key and import with one click\n- **Universal providers** — One config syncs to Claude Code, Codex, and Gemini CLI\n- One-click switching, system tray quick access, drag-and-drop sorting, import/export\n\n### Proxy & Failover\n\n- **Local proxy with hot-switching** — Format conversion, auto-failover, circuit breaker, provider health monitoring, and request rectifier\n- **App-level takeover** — Independently proxy Claude, Codex, Gemini, or Grok Build, down to individual providers\n\n### MCP, Prompts & Skills\n\n- **Unified MCP panel** — Manage MCP servers across Claude, Codex, Gemini, Grok Build, OpenCode, and Hermes with bidirectional sync and Deep Link import\n- **Prompts** — Markdown editor with cross-app sync (CLAUDE.md / AGENTS.md / GEMINI.md) and backfill protection\n- **Skills** — One-click install from GitHub repos or ZIP files, custom repository management, with symlink and file copy support\n\n### Usage & Cost Tracking\n\n- **Usage dashboard** — Track spending, requests, and tokens with trend charts, detailed request logs, and custom per-model pricing\n\n### Session Manager & Workspace\n\n- Browse, search, and restore conversation history across supported session sources\n- **Workspace editor** (OpenClaw) — Edit agent files (AGENTS.md, SOUL.md, etc.) with Markdown preview\n\n### System & Platform\n\n- **Cloud sync** — Custom config directory (Dropbox, OneDrive, iCloud, NAS) and WebDAV server sync\n- **Deep Link** (`ccswitch://`) — Import providers, MCP servers, prompts, and skills via URL\n- Dark / Light / System theme, auto-launch, auto-updater, atomic writes, auto-backups, i18n (zh/zh-TW/en/ja)\n\n## FAQ\n\n**Which AI tools does CC Switch support?** CC Switch supports eight tools: **Claude Code**, **Claude Desktop**, **Codex**, **Gemini CLI**, **Grok Build**, **OpenCode**, **OpenClaw**, and **Hermes**. Each tool has dedicated provider presets and configuration management.\n\n**Do I need to restart the terminal after switching providers?** For most tools, yes — restart your terminal or the CLI tool for changes to take effect. The exception is **Claude Code**, which currently supports hot-switching of provider data without a restart.\n\n**My plugin configuration disappeared after switching providers — what happened?** CC Switch provides a \"Shared Config Snippet\" feature to pass common data (beyond API keys and endpoints) between providers. Go to \"Edit Provider\" → \"Shared Config Panel\" → click \"Extract from Current Provider\" to save all common data. When creating a new provider, check \"Write Shared Config\" (enabled by default) to include plugin data in the new provider. All your configuration items are preserved in the default provider imported when you first launched the app.\n\n**macOS installation** CC Switch for macOS is code-signed and notarized by Apple. You can download and install it directly — no extra steps needed. We recommend using the `.dmg` installer.\n\n**Why can't I delete the currently active provider?** CC Switch follows a \"minimal intrusion\" design principle — even if you uninstall the app, your CLI tools will continue to work normally. The system always keeps one active configuration, because deleting all configurations would make the corresponding CLI tool unusable. If you rarely use a specific CLI tool, you can hide it in Settings. To switch back to official login, see the next question.\n\n**How do I switch back to official login?** Add an official provider from the preset list. After switching to it, run the Log out / Log in flow, and then you can freely switch between the official provider and third-party providers. Codex supports switching between different official providers, making it easy to switch between multiple Plus or Team accounts.\n\n**Where is my data stored?** - **Database**: `~/.cc-switch/cc-switch.db` (SQLite — providers, MCP, prompts, skills)\n- **Local settings**: `~/.cc-switch/settings.json` (device-level UI preferences)\n- **Backups**: `~/.cc-switch/backups/` (auto-rotated, keeps 10 most recent)\n- **Skills**: `~/.cc-switch/skills/` (symlinked to corresponding apps by default)\n- **Skill Backups**: `~/.cc-switch/skill-backups/` (created automatically before uninstall, keeps 20 most recent) **Linux (Wayland + NVIDIA): clicks don't register and the window black-screens on resize** The AppImage forces `GDK_BACKEND=x11` (XWayland) to avoid a historical native-Wayland crash. On newer Wayland + NVIDIA setups this can leave the web content area unclickable (the title-bar buttons still work) and black-screen on resize. Launch with the opt-in escape hatch to switch back to native Wayland:\n\n\n\n```\nCC_SWITCH_GDK_BACKEND=wayland ./CC-Switch-*.AppImage\n```\n\nIf you launch from a desktop icon, add it to the `.desktop` `Exec=` line (e.g. `env CC_SWITCH_GDK_BACKEND=wayland /path/to/AppImage`) or set it in your session environment. The variable is generic: on tiling Wayland compositors (sway/Hyprland) where clicks don't register, try `CC_SWITCH_GDK_BACKEND=x11` instead. Leaving it unset keeps the default behavior.\n\n\n\n## Documentation\n\nFor detailed guides on every feature, check out the **[User Manual](https://github.com/farion1231/cc-switch/blob/main/docs/user-manual/en/README.md)** — covering provider management, MCP/Prompts/Skills, proxy & failover, and more.\n\n\n\n## Quick Start\n\n\n\n### Basic Usage\n\n1. **Add Provider**: Click \"Add Provider\" → Choose a preset or create custom configuration\n2. **Switch Provider**: - Main UI: Select provider → Click \"Enable\" - System Tray: Click provider name directly (instant effect)\n3. **Takes Effect**: Restart your terminal or the corresponding CLI tool to apply changes (Claude Code does not require a restart)\n4. **Back to Official**: Add an \"Official Login\" preset, restart the CLI tool, then follow its login/OAuth flow\n\n### MCP, Prompts, Skills & Sessions\n\n- **MCP**: Click the \"MCP\" button → Add servers via templates or custom config → Toggle per-app sync\n- **Prompts**: Click \"Prompts\" → Create presets with Markdown editor → Activate to sync to live files\n- **Skills**: Click \"Skills\" → Browse GitHub repos → One-click install to supported apps\n- **Sessions**: Click \"Sessions\" → Browse, search, and restore conversation history across supported session sources\n\n> **Note**: On first launch, you can manually import existing CLI tool configs as the default provider.\n\n\n\n## Download & Installation\n\n\n\n### System Requirements\n\n- **Windows**: Windows 10 and above\n- **macOS**: macOS 12 (Monterey) and above\n- **Linux**: Ubuntu 22.04+ / Debian 11+ / Fedora 34+ and other mainstream distributions\n\n### Windows Users\n\nDownload the latest `CC-Switch-v{version}-Windows.msi` installer or `CC-Switch-v{version}-Windows-Portable.zip` portable version from the [Releases](https://github.com/farion1231/cc-switch/releases) page.\n\n\n\n### macOS Users\n\n**Method 1: Install via Homebrew (Recommended)**\n\n\n\n```\nbrew install --cask cc-switch\n```\n\nUpdate:\n\n\n\n```\nbrew upgrade --cask cc-switch\n```\n\n**Method 2: Manual Download**\n\nDownload `CC-Switch-v{version}-macOS.dmg` (recommended) or `.zip` from the [Releases](https://github.com/farion1231/cc-switch/releases) page.\n\n\n\n> **Note**: CC Switch for macOS is code-signed and notarized by Apple. You can install and open it directly.\n\n\n\n### Arch Linux Users\n\n**Install via paru (Recommended)**\n\n\n\n```\nparu -S cc-switch-bin\n```\n\n\n\n### Linux Users\n\nDownload the latest Linux build from the [Releases](https://github.com/farion1231/cc-switch/releases) page:\n\n- `CC-Switch-v{version}-Linux.deb` (Debian/Ubuntu)\n- `CC-Switch-v{version}-Linux.rpm` (Fedora/RHEL/openSUSE)\n- `CC-Switch-v{version}-Linux.AppImage` (Universal)\n\n> **Flatpak**: Not included in official releases. You can build it yourself from the `.deb` — see [`flatpak/README.md`](https://github.com/farion1231/cc-switch/blob/main/flatpak/README.md) for instructions.\n\n**Architecture Overview**\n\n### Design Principles\n\n\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ Frontend (React + TS) │\n│ ┌─────────────┐ ┌──────────────┐ ┌──────────────────┐ │\n│ │ Components │ │ Hooks │ │ TanStack Query │ │\n│ │ (UI) │──│ (Bus. Logic) │──│ (Cache/Sync) │ │\n│ └─────────────┘ └──────────────┘ └──────────────────┘ │\n└────────────────────────┬────────────────────────────────────┘\n│ Tauri IPC\n┌────────────────────────▼────────────────────────────────────┐\n│ Backend (Tauri + Rust) │\n│ ┌─────────────┐ ┌──────────────┐ ┌──────────────────┐ │\n│ │ Commands │ │ Services │ │ Models/Config │ │\n│ │ (API Layer) │──│ (Bus. Layer) │──│ (Data) │ │\n│ └─────────────┘ └──────────────┘ └──────────────────┘ │\n└─────────────────────────────────────────────────────────────┘\n```\n\n**Core Design Patterns**\n\n- **SSOT** (Single Source of Truth): All data stored in `~/.cc-switch/cc-switch.db` (SQLite)\n- **Dual-layer Storage**: SQLite for syncable data, JSON for device-level settings\n- **Dual-way Sync**: Write to live files on switch, backfill from live when editing active provider\n- **Atomic Writes**: Temp file + rename pattern prevents config corruption\n- **Concurrency Safe**: Mutex-protected database connection avoids race conditions\n- **Layered Architecture**: Clear separation (Commands → Services → DAO → Database) **Key Components**\n\n- **ProviderService**: Provider CRUD, switching, backfill, sorting\n- **McpService**: MCP server management, import/export, live file sync\n- **ProxyService**: Local proxy mode with hot-switching and format conversion\n- **SessionManager**: Conversation history browsing across supported session sources\n- **ConfigService**: Config import/export, backup rotation\n- **SpeedtestService**: API endpoint latency measurement **Development Guide**\n\n### Environment Requirements\n\n- Node.js 18+\n- pnpm 8+\n- Rust 1.85+\n- Tauri CLI 2.8+\n\n### Development Commands\n\n\n\n```\n# Install dependencies\npnpm install\n\n# Dev mode (hot reload)\npnpm dev\n\n# Type check\npnpm typecheck\n\n# Format code\npnpm format\n\n# Check code format\npnpm format:check\n\n# Run frontend unit tests\npnpm test:unit\n\n# Run tests in watch mode (recommended for development)\npnpm test:unit:watch\n\n# Build application\npnpm build\n\n# Build debug version\npnpm tauri build --debug\n```\n\n\n\n### Rust Backend Development\n\n\n\n```\ncd src-tauri\n\n# Format Rust code\ncargo fmt\n\n# Run clippy checks\ncargo clippy\n\n# Run backend tests\ncargo test\n\n# Run specific tests\ncargo test test_name\n\n# Run tests with test-hooks feature\ncargo test --features test-hooks\n```\n\n\n\n### Testing Guide\n\n**Frontend Testing**:\n\n- Uses **vitest** as test framework\n- Uses **MSW (Mock Service Worker)** to mock Tauri API calls\n- Uses **@testing-library/react** for component testing **Running Tests**:\n\n\n\n```\n# Run all tests\npnpm test:unit\n\n# Watch mode (auto re-run)\npnpm test:unit:watch\n\n# With coverage report\npnpm test:unit --coverage\n```\n\n\n\n### Tech Stack\n\n**Frontend**: React 18 · TypeScript · Vite · TailwindCSS 3.4 · TanStack Query v5 · react-i18next · react-hook-form · zod · shadcn/ui · @dnd-kit\n\n**Backend**: Tauri 2.8 · Rust · serde · tokio · thiserror · tauri-plugin-updater/process/dialog/store/log\n\n**Testing**: vitest · MSW · @testing-library/react\n\n**Project Structure**\n\n```\n├── src/ # Frontend (React + TypeScript)\n│ ├── components/\n│ │ ├── providers/ # Provider management\n│ │ ├── mcp/ # MCP panel\n│ │ ├── prompts/ # Prompts management\n│ │ ├── skills/ # Skills management\n│ │ ├── sessions/ # Session Manager\n│ │ ├── proxy/ # Proxy mode panel\n│ │ ├── openclaw/ # OpenClaw config panels\n│ │ ├── settings/ # Settings (Terminal/Backup/About)\n│ │ ├── deeplink/ # Deep Link import\n│ │ ├── env/ # Environment variable management\n│ │ ├── universal/ # Cross-app configuration\n│ │ ├── usage/ # Usage statistics\n│ │ └── ui/ # shadcn/ui component library\n│ ├── hooks/ # Custom hooks (business logic)\n│ ├── lib/\n│ │ ├── api/ # Tauri API wrapper (type-safe)\n│ │ └── query/ # TanStack Query config\n│ ├── locales/ # Translations (zh/zh-TW/en/ja)\n│ ├── config/ # Presets (providers/mcp)\n│ └── types/ # TypeScript definitions\n├── src-tauri/ # Backend (Rust)\n│ └── src/\n│ ├── commands/ # Tauri command layer (by domain)\n│ ├── services/ # Business logic layer\n│ ├── database/ # SQLite DAO layer\n│ ├── proxy/ # Proxy module\n│ ├── session_manager/ # Session management\n│ ├── deeplink/ # Deep Link handling\n│ └── mcp/ # MCP sync module\n├── tests/ # Frontend tests\n└── assets/ # Screenshots & partner resources\n```\n\n\n\n## Contributing\n\nIssues and suggestions are welcome!\n\nBefore submitting PRs, please ensure:\n\n- Pass type check: `pnpm typecheck`\n- Pass format check: `pnpm format:check`\n- Pass unit tests: `pnpm test:unit` For new features, please open an issue for discussion before submitting a PR. PRs for features that are not a good fit for the project may be closed.\n\n\n\n## Star History\n\n[![Star History Chart](https://camo.githubusercontent.com/488cc4b3723f1b202c5860d8d3a094177a67ceb1ee1cab0f96344d60d61f76c6/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d666172696f6e313233312f63632d73776974636826747970653d44617465)](https://www.star-history.com/#farion1231/cc-switch&Date)\n\n\n\n## License\n\nMIT © Jason Young"},"author":{"name":"farion1231","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/89d676c4f846ddb0/avatar-6a9577cd4f7f.svg","bio":"A cross-platform desktop All-in-One assistant for Claude Code, Codex, OpenCode, OpenClaw, Grok Build & Hermes Agent. Only official website: ccswitch.io - farion1231/cc-switch"},"metadata":{"timestamp":"2026-08-06T14:58:23.014Z"},"media":[{"url":"https://opengraph.githubassets.com/80d53778c5d36f4ca90f9630ab717e5935cbbeceb539bbad9bf8757895ddb0dd/farion1231/cc-switch","type":"image"}],"aiComments":[]},{"id":"web-bpvnbt","shareId":"brnX7PFczN","platform":"web","url":"https://github.com/nexu-io/open-design","title":"nexu-io/open-design: 🎨 The open-source Claude Design alternative. 🖥️ Local-first desktop app. 🖼️ Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video — real files, HTML/PDF/PPTX/MP4 export. 🤖 Claude Code / Codex / Cursor / Gemini / OpenCode / Qwen & 20+ CLIs via BYOK.","shareMode":"full","createdAt":1786028310704,"postedAt":"2026-08-06T14:58:14.516Z","publishedDate":"2026-08-06T14:58:14.516Z","archivedAt":"2026-08-06T14:58:20.362Z","archivedDate":"2026-08-06T14:58:20.362Z","viewCount":4,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/brnX7PFczN","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/DeFTjf0yb9/media/0.png","contentType":"article","content":{"text":"## Open Design: The open-source Claude Design alternative\n\n\n\n> ⚡ **Open Design Cloud — the official model service.** One recharge to use GPT, Claude, Gemini, and DeepSeek inside Open Design: 20+ flagship models, zero config, billed by real token usage. [Try Open Design Cloud](https://open-design.ai/cloud/?utm_source=github&utm_medium=referral&utm_content=readme_try_cloud) 🏅 **The Open Design Fellow program is now open.** If you also believe design should be open — become an Open Design Fellow, shape the product alongside the core team, and help more people take part in defining the future of design. Details → [`MAINTAINERS.md`](https://github.com/nexu-io/open-design/blob/main/MAINTAINERS.md) and [Discord](https://discord.gg/mHAjSMV6gz).\n\n[![Open Design hero banner — the headline \"The open-source Claude Design alternative\" over a classical scene of columns and robed figures on a digital-code backdrop, with stat cards for design systems, plugins, coding agents, and media providers](https://camo.githubusercontent.com/03e623b9a0a74798e7adb26dc51a3b9110e55fd881d0e7d51c2dc38ae3db287a/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f6865726f2e706e67)](https://camo.githubusercontent.com/03e623b9a0a74798e7adb26dc51a3b9110e55fd881d0e7d51c2dc38ae3db287a/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f6865726f2e706e67)\n\n[Website](https://open-design.ai/?utm_source=github&utm_medium=referral&utm_content=readme_website) · [Download](https://open-design.ai/?utm_source=github&utm_medium=referral&utm_content=readme_download) · [Open Design Cloud](https://open-design.ai/cloud/?utm_source=github&utm_medium=referral&utm_content=readme_cloud) · [Discord](https://discord.gg/mHAjSMV6gz) · [Follow @OpenDesignHQ](https://x.com/OpenDesignHQ)\n\n[![release](https://camo.githubusercontent.com/2f8d34ccb8aa74962ca4b5d253f866f95253eb12e1fa35eb5d30056c17c0ba02/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f6e6578752d696f2f6f70656e2d64657369676e3f7374796c653d666c617426636f6c6f723d626c756576696f6c6574266c6162656c3d72656c6561736526696e636c7564655f70726572656c656173657326646973706c61795f6e616d653d746167)](https://github.com/nexu-io/open-design/releases) [![license](https://camo.githubusercontent.com/006dcfcb1a27dc3394e9b13ffec1730a47ab67029ba386414c44bf19b74b11a7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d626c75652e7376673f7374796c653d666c6174)](https://github.com/nexu-io/open-design/blob/main/LICENSE) [![discord](https://camo.githubusercontent.com/bc59127014638fd331e797d8a7fc64bf529032db634e5cce31a0c1b045574736/68747470733a2f2f696d672e736869656c64732e696f2f646973636f72642f313437393030323438353034303438303236363f7374796c653d666c6174266c6f676f3d646973636f7264266c6f676f436f6c6f723d7768697465266c6162656c3d646973636f726426636f6c6f723d3538363546322663616368655365636f6e64733d33363030)](https://discord.gg/mHAjSMV6gz) [![quickstart](https://camo.githubusercontent.com/293472573e20a1c9db20886e0704f9cc4c4f3a994a2932e14b84f3f02ce0259e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f717569636b73746172742d33253230636f6d6d616e64732d677265656e3f7374796c653d666c6174)](https://github.com/nexu-io/open-design/blob/main/QUICKSTART.md)\n\n**English** · [Español](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.es.md) · [Português](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.pt-BR.md) · [Deutsch](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.de.md) · [Français](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.fr.md) · [简体中文](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.zh-CN.md) · [繁體中文](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.zh-TW.md) · [한국어](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.ko.md) · [日本語](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.ja-JP.md) · [العربية](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.ar.md) · [Русский](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.ru.md) · [Українська](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.uk.md) · [Türkçe](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.tr.md) · [ภาษาไทย](https://github.com/nexu-io/open-design/blob/main/docs/i18n/README.th.md)\n\n\n\n---\n\n\n\n## What is Open Design\n\n🎨 **The open-source Claude Design alternative.** 🖥️ **Local-first native desktop app for macOS and Windows.** ⚡ **Composable skills, brand-grade `DESIGN.md` design systems, and ready-to-use plugins.** 🖼️ Generates **web · desktop · mobile prototypes**, **live dashboards / artifacts**, **decks**, **images**, **video**, plus **HyperFrames** motion graphics. 🔒 Sandboxed iframe preview · HTML / PDF / PPTX / MP4 export. 🤖 **Runs on Claude Code · OpenClaw · Codex · Cursor · OpenCode · Qwen · Copilot · Amp · Hermes · Kimi · Antigravity and 25 distinct local CLI executables**, or any OpenAI-compatible endpoint via BYOK.\n\nOpen Design is what you get when the **agent-native** loop Anthropic shipped with Claude Design — discover the brief, lock the direction, stream the artifact, critique, deliver — stops being closed and becomes a **filesystem of functional skills, rendering design templates, design systems, and plugins** that the coding agents already on your laptop can read, write, and remix. Your CLI becomes the design engine, your laptop becomes the studio, and your team's `DESIGN.md` becomes the brand contract.\n\nIt's also the **Figma alternative for the agent era** — instead of pushing pixels on a canvas, it delivers single-page artifacts in real CSS, real fonts, real components, exported straight to HTML / PDF / PPTX / MP4 — already shaped by your design system, already runnable inside the agent you use every day.\n\n\n\n---\n\n\n\n## Product tour\n\nA quick look at what Open Design is and what it does. Start from **Home**, orchestrate repeat workflows with **Automation**, distill a brand contract in **Design System**, and extend with **Plugins** and **integrations**; inside any project's **Studio**, the same design system streams out prototypes, live artifacts, HyperFrames, decks, and images.\n\n\n\n### Core pages\n\n[![Home page](https://camo.githubusercontent.com/df9488e882b012f422ce712abe5c418ef191b99f508e4aef64f2ab0b260e0fb0/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f686f6d652e706e67)](https://camo.githubusercontent.com/df9488e882b012f422ce712abe5c418ef191b99f508e4aef64f2ab0b260e0fb0/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f686f6d652e706e67)\n**Home** — the overview entry point. Pick a skill and a design system, type the brief, and kick off everything from one place.\n\n\n\n| ![Automation page](https://camo.githubusercontent.com/40cfc870f0bc994e882ebbbb49e319b185af3f2a5887e5e1c9e82a9fca8d5082/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f6175746f6d6174696f6e2e706e67) **Automation** — orchestrate repetitive design workflows into reusable, schedulable automations. | ![Design System page](https://camo.githubusercontent.com/067387f94221c8148b75072f8cc94b9f92656c0c895fc5fba6f888400a650c24/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f64657369676e2d73797374656d2e706e67) **Design System** — distill your team's `DESIGN.md` into a brand contract that shapes every output. |\n| --- | --- |\n| ![Plugin page](https://camo.githubusercontent.com/9aa4a1a137470e05ce509977317d63ec0b87ca4e812f17b189730ac2ba7a313e/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f706c7567696e2e706e67) **Plugin** — browse, install, and distribute workflow plugins to extend generation on demand. | ![Integrations page](https://camo.githubusercontent.com/98b533fad41e0f891e7825e971377718dfb4f0944280f6af7ffbb31cd1ed61fc/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f696e746567726174696f6e732e706e67) **Integrations** — connect external systems and MCP tools, and use Open Design from any IDE, script, or automation. |\n\n\n\n### Studio — many artifact types in one project\n\nInside a project's Studio, the same design system streams out multiple artifact types:\n\n\n\n| ![Prototype](https://camo.githubusercontent.com/bf1079c78f50a2df4de7e9e877adebd3ff54380ebacdf3fb25c28aa317dc4cad/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f73747564696f2d70726f746f747970652e706e67) **Prototype** — single-page HTML artifacts that read your design system and render in a sandboxed iframe, previewable instantly and downloadable as source. | ![HyperFrame](https://camo.githubusercontent.com/67ad8f124579cc4ad5c17fe5d6ec43e0a03081c75712a97049724eb5a3e60d1b/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f73747564696f2d68797065726672616d652e706e67) **HyperFrame** — programmatic motion and animated graphics, rendered to a real MP4 (e.g. 1920×1080 · 30fps). |\n| --- | --- |\n| ![Deck](https://camo.githubusercontent.com/79db650ac6ebf80df081958704044461d2e1e6c11934f3ea89087dfcf8bcb706/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f73747564696f2d7070742e706e67) **Deck** — pitch decks you can page through, navigate by keyboard, and export to PPTX / PDF. | ![Image](https://camo.githubusercontent.com/1b5c222fefebb9e23d9cde3eeea3f6f0494bae5fb18c38229700ceda666ffb3b/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f70726f647563742f73747564696f2d696d6167652e706e67) **Image** — brand-grade images and visual assets, with high-resolution generation and download. |\n\n\n\n---\n\n\n\n## Platform Compatibility\n\n\n\n> Open Design ships as **skills, a CLI, and an MCP server** that mainstream coding agents consume natively. Once OD is installed, a single `od mcp install ` wires the MCP server into that agent's config, and you call the same tools from inside any agent.\n\n\n\n| Coding agent / platform | Status | One-line MCP server install |\n| --- | --- | --- |\n| Claude Code | ✅ Supported | `od mcp install claude` |\n| Codex CLI | ✅ Supported | `od mcp install codex` |\n| DeepSeek Reasonix | ✅ Supported | `od mcp install reasonix` |\n| Raven | ✅ Supported | `od mcp install raven` |\n| Cursor | ✅ Supported | `od mcp install cursor` |\n| VS Code + GitHub Copilot | ✅ Supported | `od mcp install copilot` |\n| GitHub Copilot CLI | ✅ Supported | `od mcp install copilot` |\n| OpenCode | ✅ Supported | `od mcp install opencode` |\n| OpenClaw | ✅ Supported | `od mcp install openclaw` |\n| Antigravity | ✅ Supported | `od mcp install antigravity` |\n| Cline | ✅ Supported | `od mcp install cline` |\n| Trae | ✅ Supported | `od mcp install trae` |\n| Kimi CLI | ✅ Supported | `od mcp install kimi` |\n| Kiro | ✅ Supported | `od mcp install kiro` |\n| Pi Agent | ✅ Supported | `od mcp install pi` |\n| Mistral Vibe CLI | ✅ Supported | `od mcp install vibe` |\n| Hermes Agent | ✅ Supported | `od mcp install hermes` |\n\n`od mcp install --print` for a dry-run preview · `--uninstall` to remove · full list with `od mcp install --help`.\n\n[![The 25 coding-agent CLIs Open Design supports — Claude Code · Codex · OpenCode · Hermes · Antigravity · Vela · Grok Build · Kimi · Cursor Agent · Qwen · Qoder · GitHub Copilot · Pi · Kiro · Kilo · Mistral Vibe · DeepSeek · Reasonix · Aider · Amp · CodeBuddy · Mimo · AtomCode · Devin · Trae](https://camo.githubusercontent.com/67e5284a287c972b89b8e205f8f3a1c7ea5b104bc9ff178f5549e4010de5366d/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f636f64696e672d6167656e74732e706e67)](https://camo.githubusercontent.com/67e5284a287c972b89b8e205f8f3a1c7ea5b104bc9ff178f5549e4010de5366d/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f636f64696e672d6167656e74732e706e67)\n\n**No CLI installed?** The BYOK proxy at `POST /api/proxy/{anthropic,openai,azure,google,ollama,senseaudio}/stream` gives you the same loop (no process spawn) — paste `baseUrl` + `apiKey` + `model`, with presets for OpenAI, Atlas Cloud, Anthropic, Azure OpenAI, Google Gemini, Ollama, LM Studio, vLLM, or any OpenAI-compatible endpoint. Atlas Cloud uses `https://api.atlascloud.ai/v1` with your own key and OpenAI-compatible model ids such as `qwen/qwen3.5-flash`. Per-target SSRF protection blocks internal IPs / link-local / CGNAT at the daemon edge.\n\nRuntime definitions live in [`apps/daemon/src/runtimes/defs/`](https://github.com/nexu-io/open-design/blob/main/apps/daemon/src/runtimes/defs), with registration and shared stream handling under [`apps/daemon/src/runtimes/`](https://github.com/nexu-io/open-design/blob/main/apps/daemon/src/runtimes). See [`docs/agent-adapters.md`](https://github.com/nexu-io/open-design/blob/main/docs/agent-adapters.md) for the adapter contract.\n\n\n\n---\n\n\n\n## Demo\n\nFour core product categories, all rendered by a coding agent running on your laptop. Click a thumbnail to see the real example.\n\n\n\n### 1 · Prototypes — web · desktop · mobile\n\nThe default output surface. Single-page HTML artifacts that read your `DESIGN.md` and render in a sandboxed iframe.\n\n\n\n| ![Entry view](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/01-entry-view.png) **Entry view** — pick a skill, pick a design system, type the brief. One surface for prototypes, dashboards, decks, mobile apps, magazine pages. | ![Mobile onboarding](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/mobile-onboarding.png) **Mobile prototype** — pixel-accurate iPhone 15 Pro chrome, multi-screen flows. The agent never redraws the phone frame; shared device frames live in `assets/frames/`. |\n| --- | --- |\n| ![Web prototype dating-web](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/dating-web.png) **Web prototype** — an editorial dashboard with scrollbars, KPIs, and charts. Rendered straight from `design-templates/dating-web/`. | ![Gamified app](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/gamified-app.png) **Mobile app prototype** — a three-screen gamified flow with XP ribbons and quest detail. Hand off straight to Cursor / Codex / Claude Code to turn into React/Next/Vue. |\n\n\n\n### 2 · Live artifacts & dashboards\n\nLive dashboards, decision rooms, KPI walls — single-page artifacts that pull data through a tweaks panel and stay editable in place.\n\n\n\n| ![Live dashboard](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/live-dashboard.png) **Live dashboard** — an editable KPI wall whose tweaks panel surfaces the parameters worth nudging. The agent emits a manifest, and the iframe re-renders without a reload. | ![Decision room](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/research-decision-room.png) **Decision room** — a multi-source briefing artifact for product / research / ops meetings. |\n| --- | --- |\n| ![GitHub dashboard](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/github-dashboard.png) **GitHub-style dashboard** — repo metrics presented as a live artifact. | ![Flow live dashboard](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/flowai-live-dashboard-template.png) **Flow live-dashboard template** — a domain-specific KPI template, branded through the active `DESIGN.md`. |\n\n\n\n### 3 · Decks — magazine decks, weekly updates, pitches\n\n\n\n| ![Magazine deck (guizang-ppt)](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/07-magazine-deck.png) **Deck mode (guizang-ppt)** — magazine layouts, WebGL hero, P0/P1/P2 checklists. Bundled verbatim from `op7418/guizang-ppt-skill` with its original license preserved. | ![Swiss deck](https://github.com/nexu-io/open-design/raw/main/docs/screenshots/skills/deck-swiss-international.png) **Swiss International-style deck** — grid-anchored, monochrome accents. One of **15 deck templates** and **36 themes** under `design-templates/html-ppt-*/`. |\n| --- | --- |\n\nEvery deck exports to **HTML** (single file, inlined assets), **PDF** (browser print, deck-aware), **PPTX** (agent-driven skill), **ZIP** (archive), or **Markdown**.\n\n\n\n### 4 · Images — gpt-image-2, ImageRouter, custom API\n\n\n\n| ![Illustrated city food map](https://camo.githubusercontent.com/6fa657a2d3ff5fe5a1b4b5cdb76991e7da052682209188e4a5b7dcb88bef8507/68747470733a2f2f636d732d6173736574732e796f756d696e642e636f6d2f6d656469612f313737363636323637333031345f6e66307461775f4847524d4e447962734141474738382e6a7067) **Illustrated city food map** Hand-drawn editorial travel poster | ![Cinematic elevator scene](https://camo.githubusercontent.com/b6f878fd0c45ebc3abbc2835309136eab2e0d500c36c69bdb86ccbce8dbf899e/68747470733a2f2f636d732d6173736574732e796f756d696e642e636f6d2f6d656469612f313737373435333134393032365f6764326b35305f4848435376796d626f4141567363632e6a7067) **Cinematic elevator scene** Single-frame editorial still | ![Cyberpunk anime portrait](https://camo.githubusercontent.com/f0a2c32e49e2f38c4d2ee066b15027add5a7c45f8eda96dc1ba90c1082b76b20/68747470733a2f2f636d732d6173736574732e796f756d696e642e636f6d2f6d656469612f313737373435333136343939335f6d74356236395f4848446f57666561554145413656742e6a7067) **Cyberpunk portrait** Profile avatar — neon face text | ![3D stone staircase evolution](https://camo.githubusercontent.com/de40e76a0d0a5f6f4b819a326d66ab0d4f70d7cd86e3a2fa321de1e41cd1d0c2/68747470733a2f2f636d732d6173736574732e796f756d696e642e636f6d2f6d656469612f313737363636313936383430345f386135666c6d5f484751635f4b4f614d4141327674302e6a7067) **3D stone staircase** Hewn-stone infographic | ![Glamorous portrait](https://camo.githubusercontent.com/0c8920ba286e3bf2402267a01a13923e6e84a2cebae1d46fdb2c875efa2b8ba1/68747470733a2f2f636d732d6173736574732e796f756d696e642e636f6d2f6d656469612f313737373435333138343235375f76623968766c5f48473974416b4f613441417552726e2e6a7067) **Glamorous portrait** Editorial studio shot |\n| --- | --- | --- | --- | --- |\n\n**93 ready-to-replicate prompts** live in [`prompt-templates/`](https://github.com/nexu-io/open-design/blob/main/prompt-templates) — preview thumbnails, full prompt body, target model, aspect ratio, and source attribution. One click drops a brief into the composer.\n\n\n\n### 5 · Video & HyperFrames — agent-native motion graphics\n\n**[HyperFrames](https://github.com/heygen-com/hyperframes)** is HeyGen's open-source, agent-native video framework, integrated as a first-class citizen in Open Design. The agent writes HTML + CSS + GSAP, and HyperFrames renders it to a deterministic MP4 via headless Chrome + FFmpeg. Pair it with **Seedance 2.0** for cinematic t2v / i2v, **Veo 3 / Sora 2 / Kling 2** for routed model variants, and **Suno v5 / Lyria 2** for the audio layer.\n\n\n\n| ![SaaS promo](https://camo.githubusercontent.com/f2c5d04db433ad932047df28524eb1f60df7618993aaa0b7cb9b983bd9c70acd/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f6170702d73686f77636173652e706e67) **30s SaaS product promo** · 16:9 · UI 3D reveals | ![TikTok karaoke](https://camo.githubusercontent.com/93aad44a79d01dda8529c4617382393b9b7b5f2b8293f090b6d0c8cd5b22e0e7/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f74696b746f6b2d666f6c6c6f772e706e67) **TikTok karaoke talking-head** · 9:16 · TTS + word-synced captions | ![Brand sizzle reel](https://camo.githubusercontent.com/fe1d571773e4b15e1cbd70ce2e2c5cfeb0fa263a69987b398bac971548d9c69b/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f6c6f676f2d6f7574726f2e706e67) **30s brand sizzle reel** · 16:9 · audio-reactive kinetic type | ![Bar chart race](https://camo.githubusercontent.com/d7da203e0ca7f0b7622415d6cb0ca5d6efa412e2357fa265a18930535f46288b/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f646174612d63686172742e706e67) **Bar chart race** · 16:9 · NYT-style data infographic |\n| --- | --- | --- | --- |\n| ![Flight map](https://camo.githubusercontent.com/4d44d940fad71478f44cd985225f8e2db1f03d6c737eeb8ad2fe076e988078fb/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f6e79632d70617269732d666c696768742e706e67) **Flight map** · 16:9 · Apple-style route reveal | ![Logo outro](https://camo.githubusercontent.com/fe1d571773e4b15e1cbd70ce2e2c5cfeb0fa263a69987b398bac971548d9c69b/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f6c6f676f2d6f7574726f2e706e67) **4s cinematic logo outro** · 16:9 · piece-by-piece assembly + bloom | ![Money counter](https://camo.githubusercontent.com/8ead3506fef8c686feb719d94063c58ec669e799117229239ce96c7a4bf70432/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f6170706c652d6d6f6e65792d636f756e742e706e67) **$0 → $10K money counter** · 9:16 · Apple-style hype | ![Website to video](https://camo.githubusercontent.com/0d12d94c829897cc96d253ceb41dfdfdb4dac4fcaa2215afd8f0abeb4bd9a112/68747470733a2f2f7374617469632e68657967656e2e61692f68797065726672616d65732d6f73732f646f63732f696d616765732f636174616c6f672f626c6f636b732f696e7374616772616d2d666f6c6c6f772e706e67) **Website-to-video** · 16:9 · captures the site at 3 viewports |\n\n11 HyperFrames templates + 39 Seedance prompts ship with the repo. Catalog thumbnails © HeyGen; the framework is Apache-2.0. The OD-specific render workflow (composition cache, sandbox-exec workaround, MP4-as-chip) is detailed in [`design-templates/hyperframes/`](https://github.com/nexu-io/open-design/blob/main/design-templates/hyperframes).\n\n\n\n---\n\n\n\n## Why Open Design\n\n\n\n> **In April 2026, Anthropic released Claude Design — the first time an LLM stopped writing prose and started delivering design artifacts directly.** It went viral. But it stayed closed-source, paid-only, cloud-only, locked to Anthropic's model, Anthropic's skills, Anthropic's surface. No checkout, no self-host, no Vercel deploy, no swap-in-your-own-agent.\n\nOpen Design (OD) is the open-source alternative. Same loop, same artifact-first mental model, none of the lock-in:\n\n- 🤖 **Agent-native, model-agnostic.** We don't ship an agent. The `claude` / `codex` / `cursor-agent` / `copilot` / `hermes` / `kimi` already on your `PATH` are the design engine. Swap with one click.\n- 🧠 **Brand-grade by default.** Every render reads the active package's `DESIGN.md` as the core brand contract. 151 design-system packages ship with the repo; legacy packages may be `DESIGN.md` -only, while newer packages can add `manifest.json`, `tokens.css`, components, assets, and provenance. Drop a folder in, the picker finds it.\n- 🖥️ **Local-first, BYOK at every layer.** Native desktop apps for macOS (Apple Silicon + Intel) and Windows (x64). Linux AppImage on the optional release lane. Product analytics and session replay are consent-gated; scrubbed safety and reliability telemetry is always on. Before describing daemon data paths, contributors and operators MUST read `AGENTS.md` → **Daemon data directory contract**. This README MUST NOT restate it.\n- 🌍 **Composable on four planes.** **Plugins** carry runnable workflows · functional **skills** carry agent behavior · **design templates** carry rendering blueprints · **design systems** carry the brand. All four use portable, versionable directories that anyone can author and publish.\n- 🔁 **Refresh an existing codebase.** Hand a `git` repo + `DESIGN.md` to the agent and it refactors your real components to the brand spec. Dedicated plugins migrate Figma / Pencil workflows into React / Next.js / Vue code.\n- 🔒 **Privacy by conviction.** Everything runs where your data lives — your laptop, your team's server, your Vercel project. When the network is needed, the BYOK proxy is SSRF-guarded.\n\n### Comparison\n\n\n\n| Claude Design | Figma | Lovable / v0 / Bolt | **Open Design** |\n| --- | --- | --- | --- |\n| Open source | ❌ | ❌ | ❌ | **✅ Apache-2.0** |\n| Self-host / desktop | ❌ | ❌ | ❌ | **✅ macOS + Windows + Docker + Vercel web** |\n| Agent-native (runs in your CLI) | Anthropic only | ❌ | Cloud agent only | **✅ 25 CLIs + BYOK** |\n| Brand-grade `DESIGN.md` | Proprietary | Theme JSON | Limited tokens | **✅ 151 systems shipped** |\n| Skills / plugins / templates | Closed | Plugin store | Closed | **✅ 100+ functional skills · rendering templates · 277 plugins** |\n| HyperFrames (HTML→MP4) | ❌ | ❌ | ❌ | **✅ First-class** |\n| Refresh an existing repo to brand | ❌ | ❌ | ❌ | **✅ via agent + `DESIGN.md`** |\n| Minimum billing | Pro / Max / Team | Pro / Org | Pro / Team | **BYOK · any compatible endpoint** |\n\n\n\n---\n\n\n\n## Quick start\n\nThe fastest way to use Open Design. No Node, no pnpm, no clone.\n\n- **macOS** (Apple Silicon · Intel x64) → [**open-design.ai**](https://open-design.ai/?utm_source=github&utm_medium=referral&utm_content=readme_download_macos) or [GitHub Releases](https://github.com/nexu-io/open-design/releases)\n- **Windows** (x64) → [**open-design.ai**](https://open-design.ai/?utm_source=github&utm_medium=referral&utm_content=readme_download_windows) or [GitHub Releases](https://github.com/nexu-io/open-design/releases)\n- **Linux** (AppImage, optional lane) → [GitHub Releases](https://github.com/nexu-io/open-design/releases) After install: the app auto-detects every coding-agent CLI on your `PATH`, loads 100+ functional skills, the separate rendering-template catalog, and 151 design systems, and lets you type a brief in the entry view.\n\n\n\n### 🤖 Install into your coding agent (no UI)\n\nYou can use Open Design without ever opening the GUI — call it as a skill, plugin, or MCP server inside Claude Code, Codex, Cursor, Copilot, OpenClaw, Antigravity, Hermes, Kimi, and more.\n\nIf you installed the macOS desktop app via the DMG or Homebrew cask, your shell may still resolve `od` to Apple's built-in `/usr/bin/od` octal-dump utility. In that case, open **Settings → MCP server** in the desktop app and copy the client-specific snippet; it uses absolute paths and does not rely on the bare `od` command.\n\n\n\n```\n# One-line install into the agent you're using:\nod mcp install\n# = claude | codex | reasonix | raven | cursor | copilot | openclaw\n# | antigravity | pi | vibe | hermes | cline | kimi | kiro\n# | trae | opencode\n\n# Hosted equivalent for curl-based setup:\ncurl -fsSL https://open-design.ai/install.sh | sh -s\n```\n\n`install.sh` is a thin shell wrapper around `od mcp install`; it exists so the hosted URL returns shell instead of the landing-page HTML fallback and fails fast if your shell resolves a non-Open-Design `od` binary.\n\n\n\n> **macOS / WSL2 users:** `/usr/bin/od` is a system octal-dump command and can shadow Open Design's `od` command. Desktop-app users should prefer the **Settings → MCP server** snippet; WSL2 users should follow the [`WSL2 setup guide`](https://github.com/nexu-io/open-design/blob/main/docs/wsl-setup.md) first.\n\nThen, inside the agent:\n\n\n\n```\n> Use open-design to generate a landing page with the Linear design system\n```\n\nIn a filesystem-backed local CLI run, the agent composes the selected functional skill or design template with your `DESIGN.md`, writes the canonical project files, and Open Design previews those files. A BYOK/plain-API run without filesystem tools instead returns one complete `` block.\n\n\n\n### 🐳 Run with Docker\n\n\n\n```\ngit clone https://github.com/nexu-io/open-design.git\ncd open-design/deploy\ncp .env.example .env\necho \"OD_API_TOKEN=$(openssl rand -hex 32)\" >> .env\ndocker compose up -d\n# open http://localhost:7456\n```\n\n\n\n> **macOS users:** If the web UI shows `Authorization: Bearer required`, Docker Desktop bridge networking is the cause. See [Docker Desktop on macOS](https://github.com/nexu-io/open-design/blob/main/deploy/README.md#docker-desktop-on-macos) for the fix.\n\n\n\n### 🚀 Deploy on Sealos\n\n[![Deploy on Sealos](https://camo.githubusercontent.com/ea79f0254cdc854923560e4dc71f7e63ffe09b31cad41657a11a077dccdee01d/68747470733a2f2f7365616c6f732e696f2f4465706c6f792d6f6e2d5365616c6f732e737667)](https://sealos.io/products/app-store/open-design/)\n\nThe Sealos App Store template runs the published Open Design Docker image with persistent workspace storage and Basic Auth on the public proxy. For custom public or shared Docker deployments, follow the reverse-proxy and `OPEN_DESIGN_ALLOWED_ORIGINS` guidance in [`deploy/README.md`](https://github.com/nexu-io/open-design/blob/main/deploy/README.md#local-compose).\n\n\n\n### 🧑💻 Run from source\n\n\n\n```\ngit clone https://github.com/nexu-io/open-design.git\ncd open-design\ncorepack enable && pnpm install\npnpm tools-dev run web\n```\n\nOpen the URL printed by `tools-dev`; development ports are allocated dynamically unless you pass explicit port flags.\n\nNode `~24`, pnpm `10.33.x`. WSL2 users, see [`docs/wsl-setup.md`](https://github.com/nexu-io/open-design/blob/main/docs/wsl-setup.md); native Windows users, see [`docs/windows-troubleshooting.md`](https://github.com/nexu-io/open-design/blob/main/docs/windows-troubleshooting.md). Full quickstart, env vars, Nix flake, and packaged build flow → [`QUICKSTART.md`](https://github.com/nexu-io/open-design/blob/main/QUICKSTART.md).\n\n\n\n### A full workflow — from brief to artifact\n\n`brief → plugin → direction → design system → artifact → handoff → memory`\n\n1. **A PM submits a brief.** The plugin picker offers landing page · pitch deck · dashboard · social post · PM spec · OKR scorecard…\n2. **A designer (or the agent) locks the direction.** No brand? Pick from 5 curated directions. Have a brand? Drop a screenshot / URL → the agent connects GitHub, imports Figma, and codifies a reusable `DESIGN.md`.\n3. **The agent creates the first deliverable.** Plugin + functional skill or design template + `DESIGN.md` are bound. Filesystem-backed CLI runs write canonical project files and the preview follows them; BYOK/plain-API runs without file tools return one complete `` block.\n4. **Hand off to engineering.** The artifact is real HTML/CSS — drop it into Cursor, Codex, or Claude Code to keep building as code. Or export PPTX / PDF / MP4 straight to marketing.\n5. **Open Design gets smarter as you use it.** Your screenshots, fonts, palettes, and confirmed artifacts accumulate as defaults for the next session. Less rework, less drift.\n\n---\n\n\n\n## Use Open Design from your coding agent\n\nOpen Design ships a **stdio MCP server** and per-agent **install scripts**. Any MCP-compatible agent in another repo can read files from your local Open Design projects directly — tokens CSS, JSX components, entry HTML — as a structured API queryable by name. The agent always sees the live file, not a stale export.\n\n\n\n```\n# One-line install (16+ CLIs supported):\nod mcp install\n\n# Then the agent can:\nod project list --json\nod files list --json\nod files read\nod plugin list --json\nod skills list --json\n```\n\n**Why MCP?** Exporting and re-attaching a zip every iteration breaks flow. MCP exposes the design source directly — the agent always sees the live file.\n\n**For an agent starting from scratch,** the installer places `~/.config//open-design.json` (or the platform equivalent) plus a copy-paste MCP snippet. Cursor gets a one-click deeplink; Claude Code gets a `claude mcp add-json` one-liner; every other agent gets JSON in the schema its config expects. On macOS desktop installs, prefer that Settings snippet over typing bare `od mcp install ` in Terminal, because `/usr/bin/od` may win on PATH. Full per-agent flow → **Settings → MCP server** in the desktop app, or [`docs/agent-adapters.md`](https://github.com/nexu-io/open-design/blob/main/docs/agent-adapters.md).\n\n**Security model.** Read-only by default, the daemon binds to `127.0.0.1`, and SSRF is blocked at the proxy edge. LAN exposure requires an explicit `OD_BIND_HOST` plus `OD_ALLOWED_ORIGINS`. Connector credentials and live-artifact preview routes stay loopback-only regardless.\n\n**Internally-hosted model endpoints.** To prevent SSRF, the daemon blocks provider base URLs that resolve to private/internal address ranges (RFC1918, link-local, CGNAT, and cloud-metadata IPs) by default, surfacing `Internal IPs blocked`. If you run an internally-hosted gateway (e.g. LiteLLM or Ollama on a VPN-only `10.x` / `192.168.x` address), opt that host out with `OD_ALLOWED_INTERNAL_HOSTS=,,...` — a comma- or whitespace-separated list of bare hostnames or IPs (`10.0.0.5`, `litellm.internal.corp`; a `host:port` or full URL is accepted and reduced to its hostname; IPv6 must be bracketed, e.g. `[fd00::1]`). The allowlist is strict opt-in (empty by default), exact-host (no subdomain/substring matching), and applies **only** to provider endpoints you configure (connection test, model discovery, BYOK chat). It deliberately does **not** relax the guard on download URLs returned inside upstream responses, which stay blocked. A malformed entry — or CIDR notation, which is not supported — is dropped with a warning rather than silently trusted, so a typo never quietly widens (or fails to widen) the guard. Allowlisting a hostname trusts whatever it resolves to (like `OD_ALLOWED_ORIGINS`); allowlist the resolved IP instead if you want the DNS-resolved address re-checked.\n\n\n\n---\n\n\n\n## Skills and design templates\n\n**100+ functional skills ship in [`skills/`](https://github.com/nexu-io/open-design/blob/main/skills)**. Each follows the Agent Skills [`SKILL.md`](https://docs.anthropic.com/en/docs/claude-code/skills) convention and supplies reusable agent behavior, references, or utilities. Renderable starters live separately in [`design-templates/`](https://github.com/nexu-io/open-design/blob/main/design-templates); they may also use `SKILL.md`, but they populate the design-template catalog rather than the functional-skill registry.\n\nTwo **modes** anchor the design-template catalog: `prototype` (web/mobile/desktop single-page artifacts) and `deck` (horizontal-swipe presentations). Other templates cover `image`, `video`, `audio`, and utility surfaces. The **`scenario`** field groups templates by audience: `design` · `marketing` · `operation` · `engineering` · `product` · `finance` · `hr` · `sale` · `personal`.\n\n\n\n| Design template | Mode | Scenario | What it produces |\n| --- | --- | --- | --- |\n| `web-prototype` | prototype | design | Default landing page / hero |\n| `saas-landing` | prototype | marketing | Hero / features / pricing / CTA |\n| `dashboard` | prototype | operation | Admin / analytics (with sidebar) |\n| `mobile-app` | prototype | design | iPhone 15 Pro / Pixel framed app |\n| `mobile-onboarding` | prototype | design | Splash · value-prop · sign-in flow |\n| `social-carousel` | prototype | marketing | 3-card 1080×1080 carousel |\n| `email-marketing` | prototype | marketing | Table-fallback-safe brand email |\n| `magazine-poster` | prototype | marketing | Single-page magazine layout |\n| `motion-frames` | prototype | marketing | Looping CSS motion hero |\n| `sprite-animation` | prototype | marketing | 8-bit pixel animated explainer |\n| `pm-spec` | prototype | product | PM spec doc (with TOC + decision log) |\n| `team-okrs` | prototype | product | OKR scorecard |\n| `eng-runbook` | prototype | engineering | Incident runbook |\n| `finance-report` | prototype | finance | Exec finance summary |\n| `hr-onboarding` | prototype | hr | Role onboarding plan |\n| `guizang-ppt` | deck | marketing | Magazine-style web PPT (deck default) |\n| `html-ppt-*` | deck | marketing | 15 deck templates × 36 themes (master template in `design-templates/html-ppt/`) |\n| `hyperframes` | video | marketing | HTML → MP4 motion graphics (HeyGen OSS framework) |\n| `critique` | utility | design | Five-dimensional self-critique scoresheet |\n| `tweaks` | utility | design | AI-emitted tweaks-panel manifest |\n\nFull protocol and directory split → [`docs/skills-protocol.md`](https://github.com/nexu-io/open-design/blob/main/docs/skills-protocol.md). Registry endpoints: `GET /api/skills` for functional skills and `GET /api/design-templates` for rendering templates.\n\n\n\n---\n\n\n\n## Design Systems\n\n**151 brand-grade design-system packages centered on `DESIGN.md`** ship with the repo. Legacy packages may contain only that Markdown contract; newer packages can also carry `manifest.json`, compiled `tokens.css`, component fixtures, assets, and provenance evidence. The catalog mixes upstream-derived systems with project-owned additions; [`design-systems/README.md`](https://github.com/nexu-io/open-design/blob/main/design-systems/README.md) records the package shape and provenance. Switch a system → the next render uses the new tokens.\n\n**Full catalog (click to expand)** **AI & LLM** — `claude` · `cohere` · `mistral-ai` · `minimax` · `together-ai` · `replicate` · `runwayml` · `elevenlabs` · `ollama` · `x-ai`\n\n**Developer Tools** — `cursor` · `vercel` · `linear-app` · `framer` · `expo` · `clickhouse` · `mongodb` · `supabase` · `hashicorp` · `posthog` · `sentry` · `warp` · `webflow` · `sanity` · `mintlify` · `lovable` · `composio` · `opencode-ai` · `voltagent`\n\n**Productivity** — `notion` · `figma` · `miro` · `airtable` · `superhuman` · `intercom` · `zapier` · `cal` · `clay` · `raycast`\n\n**Fintech** — `stripe` · `coinbase` · `binance` · `kraken` · `mastercard` · `revolut` · `wise`\n\n**E-commerce** — `shopify` · `airbnb` · `uber` · `nike` · `starbucks` · `pinterest`\n\n**Media** — `spotify` · `playstation` · `wired` · `theverge` · `meta`\n\n**Automotive** — `tesla` · `bmw` · `ferrari` · `lamborghini` · `bugatti` · `renault`\n\n**Other** — `apple` · `ibm` · `nvidia` · `vodafone` · `resend` · `spacex`\n\n**Starters** — `default` (Neutral Modern) · `warm-editorial`\n\nRe-import the library via [`scripts/sync-design-systems.ts`](https://github.com/nexu-io/open-design/blob/main/scripts/sync-design-systems.ts). Add your own brand → drop a `DESIGN.md` into `design-systems//`. Full guide → [`design-systems/README.md`](https://github.com/nexu-io/open-design/blob/main/design-systems/README.md).\n\n\n\n---\n\n\n\n## Plugins\n\n**277 official plugins plus 183 remixable reference examples** live in [`plugins/_official/`](https://github.com/nexu-io/open-design/blob/main/plugins/_official). Each entry is a portable plugin directory anchored by `open-design.json` plus the payload required by its type: for example `SKILL.md` for agent workflows, `template.json` for media templates, or `DESIGN.md` for design-system entries. Jump straight to a category:\n\n\n\n| Category | Count | Contents |\n| --- | --- | --- |\n| `scenarios/` | 13 | Complete design scenarios — `od-default`, `od-design-refine`, `od-figma-migration`, `od-code-migration`, `od-react-export`, `od-nextjs-export`, `od-vue-export`, `od-media-generation`, `od-new-generation`, `od-tune-collab`, `od-plugin-authoring`, `od-share-to-community`, `od-web-effect-extractor` |\n| `image-templates/` | 45 | One-shot image prompts — editorial, cinematic, product, portrait |\n| `video-templates/` | 63 | HyperFrames / Seedance / Veo motion templates |\n| `design-systems/` | 143 | Brand `DESIGN.md` wrapped as plugins |\n| `atoms/` | 13 | Reusable UI fragments (buttons, heroes, KPI cards) |\n| `examples/` | 183 | Remixable reference outputs |\n\nAlso [`plugins/community/`](https://github.com/nexu-io/open-design/blob/main/plugins/community) for community plugins and [`plugins/registry/`](https://github.com/nexu-io/open-design/blob/main/plugins/registry) for the publishing flow.\n\n\n\n### What plugins can do\n\n- 🤖 **Run in any coding agent** — [Claude Code](https://github.com/nexu-io/open-design/blob/main/docs/agent-adapters.md), Codex, Cursor, Copilot, [OpenClaw](https://github.com/openclaw/openclaw), [Antigravity](https://antigravity.google/), Hermes, Kimi… through the same skill protocol the agent already knows.\n- 🔁 **Migrate Figma / Pencil workflows** → React, Next.js, or Vue source. See [`od-figma-migration`](https://github.com/nexu-io/open-design/blob/main/plugins/_official/scenarios/od-figma-migration).\n- 🛠️ **Refresh an existing codebase to a brand spec** — point a plugin at a `git` repo + `DESIGN.md`, get a PR. See [`od-code-migration`](https://github.com/nexu-io/open-design/blob/main/plugins/_official/scenarios/od-code-migration).\n- 💾 **Persist custom workflows** — your team's reusable templates sit next to the shipped ones.\n\n### Using plugins\n\nPlugins are at full parity across the **web UI** and the **`od` CLI** — same `/api/plugins` endpoints, pick whichever fits.\n\n**In the desktop / web app:** open the **Plugin** page to browse the marketplace and click **Install**; inside a project's Studio, plugins appear as composer chips you click to apply (with the inputs they declare).\n\n**On the command line** (runs without a UI — this is the path external agents use):\n\n\n\n```\nod plugin list # list installed plugins (--task-kind / --mode / --tag filters)\nod plugin search \"landing page\" # search by keyword\nod plugin info od-default # inspect a plugin's metadata, inputs, capabilities\nod plugin install od-figma-migration # install from a registry; also accepts ./local-folder or an https://… link\nod plugin apply od-default --input brief=\"a one-page pitch for our seed round\"\nod plugin upgrade od-default # upgrade\nod plugin uninstall od-default # uninstall\n```\n\nEvery command supports `--json`, so you can pipe it through `jq` / `xargs` into automation.\n\n\n\n### Building a plugin\n\nAn Open Design plugin requires `open-design.json` plus the payload required by its type. A workflow skill or scenario also includes `SKILL.md`; manifest-only template and design-system entries use their own payloads instead:\n\n\n\n```\nmy-plugin/\n├── open-design.json ← required: marketplace metadata + inputs + pipeline + capabilities\n├── SKILL.md ← required for agent-skill/scenario entries; omitted for other plugin types\n├── README.md ← optional: usage, install, registry links\n├── preview/ ← optional: index.html / poster.png (strongly recommended for visual plugins)\n└── examples/ ← optional: concrete use cases\n```\n\nCore `open-design.json` fields: `specVersion` (currently `1.0.0`), `name` (stable ID), `version` (semver), optional `compat.agentSkills[].path` (points at `./SKILL.md` when the entry exposes an Agent Skill), `od.kind` (`skill` / `scenario` / `atom` / `bundle`), `od.taskKind` (`new-generation` / `figma-migration` / `code-migration` / `tune-collab`), `od.mode` (the output surface, e.g. `prototype` / `deck` / `live-artifact` / `image` / `video` / `hyperframes` / `audio` / `design-system` / `scenario`), `od.capabilities[]` (**declare the minimum** — a restricted install grants only `prompt:inject` by default), `od.inputs[]` (apply-time parameters).\n\nScaffold + validate locally:\n\n\n\n```\nod plugin scaffold --id my-plugin --title \"My Plugin\" # generate the skeleton\nod plugin validate ./my-plugin # check manifest / file layout\npnpm guard && pnpm --filter @open-design/plugin-runtime typecheck\n```\n\nFull field set and runtime contract → [`plugins/spec/SPEC.md`](https://github.com/nexu-io/open-design/blob/main/plugins/spec/SPEC.md); developing a plugin with a coding agent → [`plugins/spec/AGENT-DEVELOPMENT.md`](https://github.com/nexu-io/open-design/blob/main/plugins/spec/AGENT-DEVELOPMENT.md); copy-paste minimal templates → [`plugins/spec/examples/`](https://github.com/nexu-io/open-design/blob/main/plugins/spec/examples).\n\n\n\n### Contributing a plugin\n\n1. Drop the plugin folder into [`plugins/community/`](https://github.com/nexu-io/open-design/blob/main/plugins/community) (third-party plugins), or — to ship it bundled with Open Design — into the matching tier of [`plugins/_official/`](https://github.com/nexu-io/open-design/blob/main/plugins/_official).\n2. Pass validation: `od plugin validate`, `pnpm guard`, `pnpm --filter @open-design/plugin-runtime typecheck`.\n3. Fill the PR using the template in [`plugins/spec/CONTRIBUTING.md`](https://github.com/nexu-io/open-design/blob/main/plugins/spec/CONTRIBUTING.md) (ID, version, lane, mode, capabilities, trigger examples; attach a screenshot / preview for visual plugins).\n4. To publish to an external registry (skills.sh / ClawHub / standalone GitHub) → [`plugins/spec/PUBLISHING-REGISTRIES.md`](https://github.com/nexu-io/open-design/blob/main/plugins/spec/PUBLISHING-REGISTRIES.md). Plugin registry endpoint: `GET /api/plugins`. Directory overview → [`plugins/README.md`](https://github.com/nexu-io/open-design/blob/main/plugins/README.md) ([简体中文](https://github.com/nexu-io/open-design/blob/main/plugins/README.zh-CN.md)).\n\n\n\n---\n\n\n\n## Architecture\n\n\n\n```\n┌────────────────── browser (Next.js 16) / Electron shell ──────────────┐\n│ chat · file workspace · iframe preview · settings · import · MCP │\n└──────────────┬─────────────────────────────────────┬─────────────────┘\n│ /api/* │\n▼ ▼\n┌─────────────────────────────────┐ /api/proxy/{provider}/stream (SSE)\n│ local daemon (Express+SQLite) │ ─→ any OpenAI-compatible BYOK,\n│ │ SSRF-guarded at the edge\n│ /api/skills /api/design-templates /api/plugins │\n│ /api/design-systems │\n│ /api/chat (SSE) /api/proxy/* │\n│ /api/projects/:id/files/... │\n│ /api/artifacts/{save,lint} │\n│ /api/import/claude-design │\n│ MCP stdio server │\n└─────────┬───────────────────────┘\n│ spawn(cli, [...], { cwd: managed project cwd })\n▼\n┌──────────────────────────────────────────────────────────────────┐\n│ Local runtime definitions come from runtimes/registry.ts; │\n│ the base registry has 26 definitions (including byok-opencode), │\n│ backed by 25 distinct local CLI executables because byok-opencode shares │\n│ the OpenCode executable. See docs/agent-adapters.md. │\n│ composes a functional skill or design template + DESIGN.md; writes files │\n└──────────────────────────────────────────────────────────────────┘\n```\n\n\n\n| Layer | Stack |\n| --- | --- |\n| Frontend | Next.js 16 App Router + React 18 + TypeScript |\n| Daemon | Node 24 · Express · SSE streaming · `better-sqlite3` |\n| Storage | Before changing or documenting daemon storage paths, you MUST read `AGENTS.md` → **Daemon data directory contract**. This README MUST NOT restate it. |\n| Preview | Filesystem runs render canonical project files; BYOK/plain-API runs parse one complete `` block into a sandboxed `srcdoc` iframe |\n| Export | HTML (inlined) · PDF (browser print) · PPTX (agent-driven) · ZIP · Markdown · MP4 (HyperFrames) |\n| Desktop | Electron shell + sandboxed renderer + sidecar IPC (STATUS · EVAL · SCREENSHOT · CONSOLE · CLICK · SHUTDOWN) |\n| Lifecycle | One entry point: `pnpm tools-dev` (start / stop / run / status / logs / inspect / check) |\n\nFull architecture → [`docs/architecture.md`](https://github.com/nexu-io/open-design/blob/main/docs/architecture.md). Skill protocol → [`docs/skills-protocol.md`](https://github.com/nexu-io/open-design/blob/main/docs/skills-protocol.md). Agent adapter contract → [`docs/agent-adapters.md`](https://github.com/nexu-io/open-design/blob/main/docs/agent-adapters.md).\n\n\n\n---\n\n\n\n## Roadmap\n\n- Daemon + 26 runtime definitions across 25 distinct coding-agent CLI executables + skill/design-template registries + design-system catalog\n- Web app + chat + question form + 5-direction picker + todo progress + sandboxed preview\n- 100+ functional skills · separate rendering-template catalog · 151 design-system packages · 5 visual directions · 5 device frames\n- SQLite-backed projects · conversations · messages · tabs · templates\n- Multi-provider BYOK proxy (`/api/proxy/{anthropic,openai,azure,google,ollama,senseaudio}/stream`) with OpenAI-compatible presets including Atlas Cloud + SSRF guard\n- Claude Design ZIP import (`/api/import/claude-design`)\n- Sidecar protocol + Electron desktop + IPC automation\n- Artifact lint API + 5-dim self-critique pre-emit gate\n- **0.8.0** — plugin marketplace infrastructure (261 official plugins, manifest spec, per-agent install scripts)\n- **0.9.0** — Open Design Cloud (official model service built into the app: zero config, one-click sign-in)\n- **0.10.0** — the all-in-one design workspace: the whole craft loop in one window (references → material → interactive editing → motion → handoff)\n- **0.11.0** — *The Bazaar*: built in the open — a community marketplace of plugins and design systems anyone can pick from and contribute to\n- **0.12.0** — *Brand-backed Design System*: turn the brand you already own into a reusable, portable `DESIGN.md` system\n- **0.13.0** — *Stay in Flow*: native session resume, faster model picking, and export straight to screenshot-backed PPTX / PDF\n- Packaged Electron builds — macOS (Apple Silicon + Intel) + Windows (x64) + Linux AppImage (optional lane)\n- Comment-mode surgical edits — partially shipped; reliable targeted patching in progress\n- AI-emitted tweaks panel UX — not yet implemented\n- `npx od init` to scaffold a project with `DESIGN.md`\n- Plugin SDK + `od plugin {add,list,remove,test,publish}` CLI\n- Figma / Pencil → React / Next / Vue migration plugins (alpha)\n- Refresh-existing-codebase plugin (point at a git repo + `DESIGN.md`) Phased delivery → [`docs/roadmap.md`](https://github.com/nexu-io/open-design/blob/main/docs/roadmap.md).\n\n\n\n---\n\n\n\n## Community\n\nReal people behind every channel.\n\n- 💬 **Discord** — daily chat, plugin sharing, questions → [**discord.gg/mHAjSMV6gz**](https://discord.gg/mHAjSMV6gz)\n- 🐦 **X / Twitter** — release notes, milestones, behind the scenes → [**@OpenDesignHQ**](https://x.com/OpenDesignHQ)\n- 🗣️ **GitHub Discussions** — deep Q&A, RFCs, \"show your work\" → [**Discussions**](https://github.com/nexu-io/open-design/discussions)\n- 🐛 **GitHub Issues** — bug reports, feature requests → [**Issues**](https://github.com/nexu-io/open-design/issues) The [`good-first-issue`](https://github.com/nexu-io/open-design/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [`help-wanted`](https://github.com/nexu-io/open-design/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels are the easiest way in.\n\n\n\n---\n\n\n\n## Contributing\n\nOpen Design keeps moving because contributors — designers, engineers, prompt authors — keep showing up. Many of the most-used skills, design systems, and plugins were written by people outside the core team.\n\n\n\n### 🎯 Where to start (max leverage, min change)\n\n\n\n| Want to ship… | How | Where |\n| --- | --- | --- |\n| A new functional **skill** | Drop a folder with `SKILL.md` + optional `assets/` + `references/` | `skills/` · spec in `docs/skills-protocol.md` |\n| A new rendering **design template** | Add a renderable `SKILL.md` bundle | `design-templates/` |\n| A new **design system** | Drop a package centered on `DESIGN.md`; add `manifest.json`, `tokens.css`, components, assets, or provenance when needed | `design-systems//` |\n| A new **plugin** | Drop `open-design.json` + the type-specific payload under a category folder | `plugins/community/` · spec in `plugins/spec/SPEC.md` · agent dev guide in `plugins/spec/AGENT-DEVELOPMENT.md` |\n| Support a new **coding-agent CLI** | One runtime definition + registry entry; add a parser only for a new wire format | `apps/daemon/src/runtimes/defs/` |\n| Fix a bug or polish UI | Browse the `good-first-issue` label | Issues → |\n| Translate the docs | Update the `README..md` files | `TRANSLATIONS.md` |\n\n\n\n### 🤖 Contributing as an agent\n\nIf *you are the agent reading this*, the fastest path is:\n\n\n\n```\n# 1. Boot locally\ngit clone https://github.com/nexu-io/open-design.git\ncd open-design && corepack enable && pnpm install\npnpm tools-dev run web\n\n# 2. Find a good-first-issue and assign yourself\ngh issue list --label \"good first issue\" --state open --limit 20\ngh issue develop # create a branch and worktree\n\n# 3. Make the change, run the checks\npnpm guard && pnpm typecheck\npnpm --filter @open-design/ test\n\n# 4. Open the PR\ngh pr create --fill\n```\n\nFull agent-friendly contribution flow, code style, and PR bar → [`CONTRIBUTING.md`](https://github.com/nexu-io/open-design/blob/main/CONTRIBUTING.md) ([Deutsch](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.de.md) · [Français](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.fr.md) · [简体中文](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.zh-CN.md) · [日本語](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.ja-JP.md) · [한국어](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.ko.md) · [Português](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.pt-BR.md) · [ภาษาไทย](https://github.com/nexu-io/open-design/blob/main/docs/i18n/CONTRIBUTING.th.md)).\n\n\n\n### 🏅 Open Design Fellow program\n\nWe're recruiting **Open Design Fellows** around the world — Fellows shape the product alongside the core team, represent Open Design officially in their region, and grow the community locally, backed by funded support ($1,000 / MR), free LLM credits, and a direct review track. Details → [`MAINTAINERS.md`](https://github.com/nexu-io/open-design/blob/main/MAINTAINERS.md) and the announcement on [Discord](https://discord.gg/mHAjSMV6gz).\n\n\n\n---\n\n\n\n## Maintainers\n\nThey carry a lot of the load — daily maintenance, review, and community support.\n\n\n\n| ![@Nagendhra-web](https://github.com/Nagendhra-web.png) **@Nagendhra-web** Maintainer | ![@Sid-Qin](https://github.com/Sid-Qin.png) **@Sid-Qin** Maintainer | ![@YOMXXX](https://github.com/YOMXXX.png) **@YOMXXX** Maintainer |\n| --- | --- | --- |\n\nMaintainer rules, promotion criteria, and the exit protocol → [`MAINTAINERS.md`](https://github.com/nexu-io/open-design/blob/main/MAINTAINERS.md) (also [Deutsch](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.de.md) · [Français](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.fr.md) · [简体中文](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.zh-CN.md) · [日本語](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.ja-JP.md) · [한국어](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.ko.md) · [Português](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.pt-BR.md) · [ภาษาไทย](https://github.com/nexu-io/open-design/blob/main/docs/i18n/MAINTAINERS.th.md)).\n\n\n\n## Contributors\n\nThanks to everyone who has taken part — code, docs, feedback, a sharp issue, a new skill, a new design system.\n\n[![Open Design contributors](https://camo.githubusercontent.com/2208217c7f3633ca71b24147888d7996ecf9724aed2026cddcfd0747f5ad7a5a/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6e6578752d696f2f6f70656e2d64657369676e266d61783d35303026636f6c756d6e733d323026616e6f6e3d312663616368655f627573743d323032362d30382d3034)](https://github.com/nexu-io/open-design/graphs/contributors)\n\n---\n\n\n\n## Repository activity\n\n\n\n![Open Design — repository metrics](https://camo.githubusercontent.com/578869d8ce49ad650d21acd62603f7846b8a3f215dfa0ea4bab4546129c1c1f8/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f6769746875622d6d6574726963732e737667)\n\n\nThe SVG above is regenerated daily by [`.github/workflows/metrics.yml`](https://github.com/nexu-io/open-design/blob/main/.github/workflows/metrics.yml) using [`lowlighter/metrics`](https://github.com/lowlighter/metrics).\n\n\n\n---\n\n\n\n## Star us\n\n[![Star Open Design on GitHub — github.com/nexu-io/open-design](https://camo.githubusercontent.com/591d034afc535cc83bc56d4090d4282ba5371e8ad60f8fde4b6ee81b560a3443/68747470733a2f2f7265706f2d6173736574732e6f70656e2d64657369676e2e61692f7265736f75726365732f696d616765732f737461722d75732e706e67)](https://github.com/nexu-io/open-design)\n\nIf this saved you thirty minutes, give it a ★. Stars don't pay rent — but they tell the next designer, agent, and contributor that this experiment is worth their attention. One click, three seconds, a real signal.\n\n[![Open Design star history](https://camo.githubusercontent.com/14c169d6c560776ba61203c58384dd6358066b82c40db615e4eb343949f359f0/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d6e6578752d696f2f6f70656e2d64657369676e26747970653d446174652663616368655f627573743d323032362d30382d3034)](https://star-history.com/#nexu-io/open-design&Date)\n\n---\n\n\n\n## References & lineage\n\n\n\n| Project | Role |\n| --- | --- |\n| Claude Design | The closed-source product this repo is the open-source alternative to. |\n| `alchaincyf/huashu-design` | The design-philosophy compass — junior-designer workflow, brand-asset protocol, anti-AI-slop checklist, five-dimensional critique. |\n| `op7418/guizang-ppt-skill` | The magazine-style web PPT skill, bundled verbatim under `design-templates/guizang-ppt/`. Default for deck mode. |\n| `lewislulu/html-ppt-skill` | The HTML PPT Studio family — 15 deck templates, 36 themes, 31 page layouts, animation runtime, magnetic-card presenter mode. |\n| `OpenCoworkAI/open-codesign` | The first open-source Claude Design alternative; UX patterns we borrow (streaming-artifact loop, sandboxed iframe, live agent panel). |\n| `multica-ai/multica` | The daemon + adapter architecture — PATH-scan agent detection, local daemon as the only privileged process. |\n| `VoltAgent/awesome-design-md` | Historical source of the original 9-section `DESIGN.md` schema and 70 upstream-derived systems; current packages may extend that baseline. |\n| `bergside/awesome-design-skills` | Source of the 57 design skills added under `design-systems/`. |\n| `heygen-com/hyperframes` | The HTML→MP4 motion-graphics framework, integrated as the first-class `hyperframes-html` in Open Design. |\n| Claude Code skills | The `SKILL.md` convention we adopt verbatim. |\n\nDetailed provenance → [`docs/references.md`](https://github.com/nexu-io/open-design/blob/main/docs/references.md).\n\n\n\n## License\n\nApache-2.0. Bundled skills and templates with their own `LICENSE` files retain those licenses, including `design-templates/guizang-ppt/` (MIT, [@op7418](https://github.com/op7418)), `design-templates/html-ppt/` (MIT, [@lewislulu](https://github.com/lewislulu)), and `skills/web-clone/` (MIT, [@Jane-xiaoer](https://github.com/Jane-xiaoer))."},"author":{"name":"nexu-io","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/acb9ab7e19be520d/avatar-6a9577cd4f7f.svg","bio":"🎨 The open-source Claude Design alternative. 🖥️ Local-first desktop app. 🖼️ Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video — real fil..."},"metadata":{"timestamp":"2026-08-06T14:58:14.516Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/DeFTjf0yb9/media/0.png","type":"image","width":1200,"height":600}],"aiComments":[]},{"id":"web-vwbztq","shareId":"Ty9DrBBoVr","platform":"web","url":"https://github.com/responsively-org/responsively-app","title":"responsively-org/responsively-app: A modified web browser that helps in responsive web development. A web developer's must have dev-tool.","shareMode":"full","createdAt":1786028299289,"postedAt":"2026-08-06T14:58:07.008Z","publishedDate":"2026-08-06T14:58:07.008Z","archivedAt":"2026-08-06T14:58:10.663Z","archivedDate":"2026-08-06T14:58:10.663Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/Ty9DrBBoVr","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/QW1yx63km5/media/0.png","contentType":"article","content":{"text":"[![Responsively Logo](https://camo.githubusercontent.com/0fefc998ce975f9a3fce39c4f79be20032d80b2aaba0a3df750c3fe9e24288ce/68747470733a2f2f726573706f6e736976656c792e6170702f6173736574732f696d672f6c6f676f2e706e67)](https://camo.githubusercontent.com/0fefc998ce975f9a3fce39c4f79be20032d80b2aaba0a3df750c3fe9e24288ce/68747470733a2f2f726573706f6e736976656c792e6170702f6173736574732f696d672f6c6f676f2e706e67)\n\n## Responsively App\n\n**A must-have dev tool for web developers for quicker responsive web development. 🚀**\n\n###### Save time by becoming 5x faster!\n\n\n[![Twitter](https://camo.githubusercontent.com/81d7c738a74191adc5c66e53a65423e4b1159a6cba2ca555387250259b7344cd/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f726573706f6e736976656c794170703f636f6c6f723d323641304544266c6162656c3d466f6c6c6f77266c6f676f3d74776974746572266c6f676f436f6c6f723d7768697465267374796c653d666c6174)](https://x.com/ResponsivelyApp) [![contributors](https://camo.githubusercontent.com/2914a2077a50035911d4061dbf1c2690f8a66dca3341b161a4e9d85c8694c377/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f616c6c2d636f6e7472696275746f72732f726573706f6e736976656c792d6f72672f726573706f6e736976656c792d6170703f7374796c653d666c6174)](#contributors-) [![Discord](https://camo.githubusercontent.com/6f5d7926a78a8546a35fe376ee99410b609eb0308ae2f6318a2fb94dfa96b5e7/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4a6f696e2532302d446973636f72642d626c75653f6c6f676f3d646973636f7264266c6f676f436f6c6f723d7768697465)](https://responsively.app/join-discord) [![XS:Code](https://camo.githubusercontent.com/bf995d4f9bab7011cc7895c5c27519dca9022c8e0460233905a7511d48af7a2d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f417661696c61626c652532306f6e2d7873253341636f64652d626c75653f7374796c653d3f7374796c653d666c6174266c6f676f3d6170707665796f72266c6f676f3d646174613a696d6167652f706e673b6261736536342c6956424f5277304b47676f414141414e5355684555674141414541414141424143414d41414143647434487341414141475852465748525462325a30643246795a5142425a4739695a53424a6257466e5a564a6c5957523563636c6c5041414141415a51544652462f2f2f2f2f2f2f2f56587a316241414141414a30556b35542f77446c747a424b414141416c556c455156523432757a58537771414d417745304d6e394c2b33476774676b6b33355177636e534a6f39532b7947774d3944436f6f4362676e3459724a3443495055635146372f58534262783254457a3473415a3271315241454342416959426c437476774e2b4b6959416c47375544476a35394d56695439684f77457141685943744173555a764c36493657386332776362642b4c495753434853546553414145436e674e3478784944534b39663442397433373757643748354e74372f587a3865416777417665734c526a595950755541414141415355564f524b35435949493d)](https://xscode.com/manojvivek/responsively-app) [![PRs Welcome](https://camo.githubusercontent.com/7d9ed3c8f22eceb1711573169b1390cc0b1194467340dc815205060c162b5309/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d77656c636f6d652d627269676874677265656e2e7376673f7374796c653d666c6174)](https://github.com/responsively-org/responsively-app/issues)\n\n[![ProductHunt](https://camo.githubusercontent.com/a6dff16436dc55a2a9f1e4fa2bbedb14ee696398fc5e1bb7f4a9ed6f37eefa35/68747470733a2f2f6170692e70726f6475637468756e742e636f6d2f776964676574732f656d6265642d696d6167652f76312f746f702d706f73742d62616467652e7376673f706f73745f69643d323030333735267468656d653d6c6967687426706572696f643d6461696c79)](https://www.producthunt.com/posts/responsively?utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-responsively)\n\nDownload Now (free!): [responsively.app](https://responsively.app/download)\n\n\n\n## Responsively App\n\n\n\n> A modified browser built using [Electron](https://www.electronjs.org/) that helps in responsive web development.\n\n[![Quick Demo](https://camo.githubusercontent.com/978fd9c182f7fde1a0f00e4daa65ec0799588da03aee781bff81e14b3fe45714/68747470733a2f2f726573706f6e736976656c792e6170702f6173736574732f696d672f726573706f6e736976656c792d6170702e676966)](https://camo.githubusercontent.com/978fd9c182f7fde1a0f00e4daa65ec0799588da03aee781bff81e14b3fe45714/68747470733a2f2f726573706f6e736976656c792e6170702f6173736574732f696d672f726573706f6e736976656c792d6170702e676966)\n\n\n\n## Features\n\n1. Mirrored User-interactions across all devices.\n2. Customizable preview layout to suit all your needs.\n3. One handy elements inspector for all devices in preview.\n4. 30+ built-in device profiles with the option to add custom devices.\n5. One-click screenshots on all your devices.\n6. Hot reloading is supported for developers. Please visit the website to learn more about the application - [https://responsively.app](https://responsively.app/)\n\n\n\n## Download\n\nThe application is available for Mac, Windows and Linux platforms. Please [download it from responsively.app](https://responsively.app/download)\n\nAlternatively, MacOS users can use [`brew`](https://formulae.brew.sh/cask/responsively) [![Homebrew installs](https://camo.githubusercontent.com/9e983c3856e747f82794d8ca947900be687c0f4ce0cfecd3b0a48f051552469d/68747470733a2f2f62616467656e2e6e65742f686f6d65627265772f6361736b2f64792f726573706f6e736976656c79)](https://formulae.brew.sh/cask/responsively)\n\n\n\n```\nbrew install --cask responsively\n```\n\nAlso, Windows users can use [`chocolatey`](https://chocolatey.org/packages/responsively/) [![Chocolatey installs](https://camo.githubusercontent.com/c60a34bfbe006b61c054114a652fe55ccb9b0cb666e00faa82d5aaf7e698baa6/68747470733a2f2f696d672e736869656c64732e696f2f63686f636f6c617465792f64742f726573706f6e736976656c79)](https://chocolatey.org/packages/responsively/)\n\n\n\n```\nchoco install responsively\n```\n\nor [`winget`](https://github.com/microsoft/winget-cli):\n\n\n\n```\nwinget install ResponsivelyApp\n```\n\nLinux users using an RPM Package Manager can use `rpm`\n\n\n\n```\nsudo rpm -i https://github.com/responsively-org/responsively-app/releases/download/v[VERSION]/Responsively-App-[VERSION].x86_64.rpm\n```\n\notherwise, download an AppImage from [the releases page](https://github.com/responsively-org/responsively-app/releases)\n\nFollow us on Twitter for future updates - [![Twitter Follow](https://camo.githubusercontent.com/321513dde13e54936bd657265a87f56ff6d7f55f10c82e4470f8b661a32fabb7/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f526573706f6e736976656c794170703f7374796c653d736f6369616c)](https://x.com/ResponsivelyApp)\n\n\n\n## Browser Extension\n\nInstall the handy browser extension to easily send links from your browser to the app and preview instantly.\n\n- [Download for Chrome](https://chrome.google.com/webstore/detail/responsively-helper/jhphiidjkooiaollfiknkokgodbaddcj) [![Chrome Web Store](https://camo.githubusercontent.com/fbac267fe6b29374e7efc8131f4357e27e12c4bec2a0b5196470a75fd3c999db/68747470733a2f2f696d672e736869656c64732e696f2f6368726f6d652d7765622d73746f72652f75736572732f6a6870686969646a6b6f6f69616f6c6c66696b6e6b6f6b676f6462616464636a3f636f6c6f723d626c7565)](https://chrome.google.com/webstore/detail/responsively-helper/jhphiidjkooiaollfiknkokgodbaddcj)\n- [Download for Firefox](https://addons.mozilla.org/en-US/firefox/addon/responsively-helper/) [![Mozilla Add-on](https://camo.githubusercontent.com/1421bcf96e4fc309f32c344dc698160226b015848e382ba2362910b8abf90424/68747470733a2f2f696d672e736869656c64732e696f2f616d6f2f75736572732f726573706f6e736976656c792d68656c706572)](https://addons.mozilla.org/en-US/firefox/addon/responsively-helper/)\n- [Download for Edge](https://microsoftedge.microsoft.com/addons/detail/responsively-helper/ooiejjgflcgkbbehheengalibfehaojn) [![Edge Add-on](https://camo.githubusercontent.com/7113eaaea9b157941638e58b0bbeaedd62ea2539f7f8c0bf9623b46f5f6d8a49/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f64796e616d69632f6a736f6e3f6c6162656c3d75736572732671756572793d2532342e616374697665496e7374616c6c436f756e742675726c3d68747470732533412532462532466d6963726f736f6674656467652e6d6963726f736f66742e636f6d2532466164646f6e7325324667657470726f6475637464657461696c73627963727869642532466f6f69656a6a67666c63676b626265686865656e67616c6962666568616f6a6e)](https://microsoftedge.microsoft.com/addons/detail/responsively-helper/ooiejjgflcgkbbehheengalibfehaojn)\n\n## Issues\n\nIf you face any problems while using the application, please open an issue here - [https://github.com/responsively-org/responsively-app/issues](https://github.com/responsively-org/responsively-app/issues)\n\n\n\n## Roadmap\n\nHere is the roadmap of the desktop app - [https://github.com/responsively-org/responsively-app/projects/](https://github.com/responsively-org/responsively-app/projects/)\n\n\n\n## Gold sponsors 🥇\n\n\n\n## Contribute\n\nTo get started with contributing your awesome ideas to Responsively, follow the [comprehensive guide here](https://github.com/responsively-org/responsively-app/blob/main/CONTRIBUTING.md)!\n\n\n\n## Get in touch\n\nCome say hi to us on [Discord](https://responsively.app/join-discord)! 👋\n\n\n\n## Contributors ✨\n\nThanks go to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):\n\n\n\n| ![Manoj Vivek](https://avatars1.githubusercontent.com/u/1283424?v=4?s=100) **Manoj Vivek** 💻 ⚠️ 📆 | ![Suresh P](https://avatars0.githubusercontent.com/u/26249498?v=4?s=100) **Suresh P** 💻 ⚠️ 📆 | ![Sigit Prabowo](https://avatars2.githubusercontent.com/u/11748183?v=4?s=100) **Sigit Prabowo** 💻 | ![Leon Feng](https://avatars1.githubusercontent.com/u/523684?v=4?s=100) **Leon Feng** 📖 | ![Kishore S](https://avatars2.githubusercontent.com/u/30261988?v=4?s=100) **Kishore S** 💻 |\n| --- | --- | --- | --- | --- |\n| ![José Javier Rodríguez Zas](https://avatars2.githubusercontent.com/u/13673443?v=4?s=100) **José Javier Rodríguez Zas** 💻 ⚠️ | ![Roman Akash](https://avatars1.githubusercontent.com/u/40427975?v=4?s=100) **Roman Akash** 💻 | ![Romain Francony](https://avatars3.githubusercontent.com/u/22396965?v=4?s=100) **Romain Francony** 💻 | ![AARYAN-MAHENDRA](https://avatars1.githubusercontent.com/u/64866670?v=4?s=100) **AARYAN-MAHENDRA** 💻 | ![Andy](https://avatars3.githubusercontent.com/u/18606648?v=4?s=100) **Andy** 💻 |\n| ![Ryan Pais](https://avatars0.githubusercontent.com/u/15522605?v=4?s=100) **Ryan Pais** 💻 ⚠️ | ![Jonathan](https://avatars1.githubusercontent.com/u/395137?v=4?s=100) **Jonathan** 💻 | ![Gema Anggada ✌︎](https://avatars1.githubusercontent.com/u/10743728?v=4?s=100) **Gema Anggada ✌︎** 💻 | ![jonathanurias96](https://avatars2.githubusercontent.com/u/57416786?v=4?s=100) **jonathanurias96** 💻 | ![Federico Alecci](https://avatars2.githubusercontent.com/u/17703824?v=4?s=100) **Federico Alecci** 💻 |\n| ![Abduraimov Muminjon](https://avatars1.githubusercontent.com/u/24930020?v=4?s=100) **Abduraimov Muminjon** 📖 | ![Víctor Lázaro](https://avatars1.githubusercontent.com/u/38981659?v=4?s=100) **Víctor Lázaro** 💻 | ![Kavita Nambissan](https://avatars0.githubusercontent.com/u/3608742?v=4?s=100) **Kavita Nambissan** 💻 | ![Prashant Palikhe](https://avatars0.githubusercontent.com/u/2657709?v=4?s=100) **Prashant Palikhe** 💻 | ![Jaune Sarmiento](https://avatars1.githubusercontent.com/u/1166928?v=4?s=100) **Jaune Sarmiento** 🖋 |\n| ![Diego Vieira](https://avatars2.githubusercontent.com/u/930792?v=4?s=100) **Diego Vieira** 💻 | ![Ajaykumar](https://avatars0.githubusercontent.com/u/21375014?v=4?s=100) **Ajaykumar** 💻 | ![Kiruba Karan](https://avatars0.githubusercontent.com/u/38885946?v=4?s=100) **Kiruba Karan** 💻 | ![Sebastián Rodríguez](https://avatars1.githubusercontent.com/u/1605931?v=4?s=100) **Sebastián Rodríguez** 💻 | ![Karthick Raja](https://avatars1.githubusercontent.com/u/47154512?v=4?s=100) **Karthick Raja** 💻 |\n| ![Johnny Zabala](https://avatars0.githubusercontent.com/u/1315054?v=4?s=100) **Johnny Zabala** 💻 | ![Ross Moody](https://avatars0.githubusercontent.com/u/29072694?v=4?s=100) **Ross Moody** 🎨 | ![Mehrdad Shokri](https://avatars1.githubusercontent.com/u/13661520?v=4?s=100) **Mehrdad Shokri** 🚇 | ![Abdelhak Akermi](https://avatars1.githubusercontent.com/u/60294727?v=4?s=100) **Abdelhak Akermi** 💻 | ![Carlos Perez](https://avatars0.githubusercontent.com/u/20329014?v=4?s=100) **Carlos Perez** 💻 |\n| ![Jayant Arya](https://avatars0.githubusercontent.com/u/42388314?v=4?s=100) **Jayant Arya** 💻 | ![John Rawlins](https://avatars3.githubusercontent.com/u/42707277?v=4?s=100) **John Rawlins** 💻 | ![lepasq](https://avatars3.githubusercontent.com/u/53230128?v=4?s=100) **lepasq** 💻 | ![mrfelfel](https://avatars0.githubusercontent.com/u/19575588?v=4?s=100) **mrfelfel** 💻 | ![gorogoroumaru](https://avatars3.githubusercontent.com/u/30716350?v=4?s=100) **gorogoroumaru** 💻 |\n| ![Rui Saraiva](https://avatars2.githubusercontent.com/u/7356098?v=4?s=100) **Rui Saraiva** 💻 | ![Mehmet Bakirci](https://avatars2.githubusercontent.com/u/9880089?v=4?s=100) **Mehmet Bakirci** 💻 | ![Julien Bertazzo Lambert](https://avatars0.githubusercontent.com/u/42924425?v=4?s=100) **Julien Bertazzo Lambert** 💻 | ![Dinesh Balaji](https://avatars3.githubusercontent.com/u/4656109?v=4?s=100) **Dinesh Balaji** 💻 | ![MedBMoussa](https://avatars3.githubusercontent.com/u/26111211?v=4?s=100) **MedBMoussa** 💻 |\n| ![Lucie Vrsovska](https://avatars.githubusercontent.com/u/46979603?v=4?s=100) **Lucie Vrsovska** 💻 | ![Jakub Cabak](https://avatars.githubusercontent.com/u/1818155?v=4?s=100) **Jakub Cabak** 💻 | ![Palakurthi Durga Kiran Kumar](https://avatars.githubusercontent.com/u/17452039?v=4?s=100) **Palakurthi Durga Kiran Kumar** 💻 | ![Karl Labrador](https://avatars.githubusercontent.com/u/58193703?v=4?s=100) **Karl Labrador** 💻 | ![Rishi Kumar Chawda](https://avatars.githubusercontent.com/u/26366288?v=4?s=100) **Rishi Kumar Chawda** 💻 |\n| ![Carlos Rafael de Oliveira Carneiro](https://avatars.githubusercontent.com/u/10589421?v=4?s=100) **Carlos Rafael de Oliveira Carneiro** 💻 | ![Zach Hoskins](https://avatars.githubusercontent.com/u/50255197?v=4?s=100) **Zach Hoskins** 💻 | ![KIWAN KIM](https://avatars.githubusercontent.com/u/25267859?v=4?s=100) **KIWAN KIM** 💻 | ![Andreas](https://avatars.githubusercontent.com/u/13819164?v=4?s=100) **Andreas** 💻 | ![Panu Valtanen](https://avatars.githubusercontent.com/u/28947061?v=4?s=100) **Panu Valtanen** 💻 |\n| ![Dustin Dauncey](https://avatars.githubusercontent.com/u/8153796?v=4?s=100) **Dustin Dauncey** 💻 | ![heagan01](https://avatars.githubusercontent.com/u/54394590?v=4?s=100) **heagan01** 💻 | ![Sahil Jangra](https://avatars.githubusercontent.com/u/74812563?v=4?s=100) **Sahil Jangra** 💻 | ![Jay Carlson](https://avatars.githubusercontent.com/u/1357866?v=4?s=100) **Jay Carlson** 💻 | ![Michael Padilla](https://avatars.githubusercontent.com/u/51935560?v=4?s=100) **Michael Padilla** 💻 |\n| ![Andreas Steinkellner](https://avatars.githubusercontent.com/u/6213380?v=4?s=100) **Andreas Steinkellner** 💻 | ![aniknia](https://avatars.githubusercontent.com/u/40159649?v=4?s=100) **aniknia** 💻 | ![Wayne Rocha](https://avatars.githubusercontent.com/u/62760711?v=4?s=100) **Wayne Rocha** 💻 | ![crbon](https://avatars.githubusercontent.com/u/2604330?v=4?s=100) **crbon** 💻 | ![Mohammad S](https://avatars.githubusercontent.com/u/59393936?v=4?s=100) **Mohammad S** 💻 |\n| ![Reshad Sadik](https://avatars.githubusercontent.com/u/66641469?v=4?s=100) **Reshad Sadik** 💻 | ![Kwesi Ansah](https://avatars.githubusercontent.com/u/31078314?v=4?s=100) **Kwesi Ansah** 💻 | ![Monalisa Sahoo](https://avatars.githubusercontent.com/u/65477771?v=4?s=100) **Monalisa Sahoo** 💻 | ![Mitesh Tank](https://avatars.githubusercontent.com/u/85953650?v=4?s=100) **Mitesh Tank** 💻 | ![Ryan](https://avatars.githubusercontent.com/u/82715592?v=4?s=100) **Ryan** 💻 |\n| ![jayway](https://avatars.githubusercontent.com/u/70596906?v=4?s=100) **jayway** 💻 | ![Alex Fernandez](https://avatars.githubusercontent.com/u/3981106?v=4?s=100) **Alex Fernandez** 💻 | ![Danial Gharib](https://avatars.githubusercontent.com/u/90343552?v=4?s=100) **Danial Gharib** 💻 | ![Alexander Menk](https://avatars.githubusercontent.com/u/1087128?v=4?s=100) **Alexander Menk** 📖 | ![Tunahan Gediz](https://avatars.githubusercontent.com/u/75015671?v=4?s=100) **Tunahan Gediz** 📖 |\n| ![Jeff Bowen](https://avatars.githubusercontent.com/u/272795?v=4?s=100) **Jeff Bowen** 💻 | ![Param Birje](https://avatars.githubusercontent.com/u/87022870?v=4?s=100) **Param Birje** 💻 | ![Prajjwal Yadav](https://avatars.githubusercontent.com/u/111794524?v=4?s=100) **Prajjwal Yadav** 📖 | ![Steven Cabrera](https://avatars.githubusercontent.com/u/49458912?v=4?s=100) **Steven Cabrera** 💻 | ![negar nasiri](https://avatars.githubusercontent.com/u/113235504?v=4?s=100) **negar nasiri** 💻 |\n| ![Gaurav Singh](https://avatars.githubusercontent.com/u/99260988?v=4?s=100) **Gaurav Singh** 💻 | ![Nishidh Jain](https://avatars.githubusercontent.com/u/61869195?v=4?s=100) **Nishidh Jain** 💻 | ![Samiksha Mishra](https://avatars.githubusercontent.com/u/38784342?v=4?s=100) **Samiksha Mishra** 📖 | ![Len Dierickx](https://avatars.githubusercontent.com/u/1730624?v=4?s=100) **Len Dierickx** 💻 | ![Suraj Bobade](https://avatars.githubusercontent.com/u/102910293?v=4?s=100) **Suraj Bobade** 💻 |\n| ![Sagar Hedaoo](https://avatars.githubusercontent.com/u/10000167?v=4?s=100) **Sagar Hedaoo** 💻 | ![V](https://avatars.githubusercontent.com/u/36138541?v=4?s=100) **V** 💻 ⚠️ | ![Prabhas Jupalli](https://avatars.githubusercontent.com/u/139740712?v=4?s=100) **Prabhas Jupalli** 💻 | ![Pulkit](https://avatars.githubusercontent.com/u/65671483?v=4?s=100) **Pulkit** 💻 | ![Brandon Yee](https://avatars.githubusercontent.com/u/139765638?v=4?s=100) **Brandon Yee** 🖋 |\n| ![Wendy Pan](https://avatars.githubusercontent.com/u/110360465?v=4?s=100) **Wendy Pan** 💻 | ![Pranav Ithape](https://avatars.githubusercontent.com/u/170559714?v=4?s=100) **Pranav Ithape** 💻 | ![SUKRIT PRAKASH SINGH](https://avatars.githubusercontent.com/u/136228545?v=4?s=100) **SUKRIT PRAKASH SINGH** 💻 | ![Aryan Panchal](https://avatars.githubusercontent.com/u/97938438?v=4?s=100) **Aryan Panchal** 💻 | ![Samran Ahmed](https://avatars.githubusercontent.com/u/149153498?v=4?s=100) **Samran Ahmed** 💻 |\n| ![SatyamAgrawal](https://avatars.githubusercontent.com/u/95954551?v=4?s=100) **SatyamAgrawal** 💻 | ![Sai Tharun Dusa](https://avatars.githubusercontent.com/u/169873642?v=4?s=100) **Sai Tharun Dusa** 💻 | ![Pavlo](https://avatars.githubusercontent.com/u/179954458?v=4?s=100) **Pavlo** 💻 ⚠️ | ![AnalyticalDataArtisan](https://avatars.githubusercontent.com/u/183036785?v=4?s=100) **AnalyticalDataArtisan** 💻 | ![Kieren-Foenander](https://avatars.githubusercontent.com/u/68266113?v=4?s=100) **Kieren-Foenander** 💻 |\n| ![Trishit Swarnakar](https://avatars.githubusercontent.com/u/170200412?v=4?s=100) **Trishit Swarnakar** 💻 | ![Abhijeet Singh](https://avatars.githubusercontent.com/u/29686866?v=4?s=100) **Abhijeet Singh** 📖 | ![aaryan](https://avatars.githubusercontent.com/u/93996658?v=4?s=100) **aaryan** 💻 | ![Michael Klikushin](https://avatars.githubusercontent.com/u/135151016?v=4?s=100) **Michael Klikushin** 💻 | ![Bhanu Prakash](https://avatars.githubusercontent.com/u/48930756?v=4?s=100) **Bhanu Prakash** 💻 |\n| ![Saúl Sáyago](https://avatars.githubusercontent.com/u/67727553?v=4?s=100) **Saúl Sáyago** 💻 | ![Dailin](https://avatars.githubusercontent.com/u/71995555?v=4?s=100) **Dailin** 💻 |\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind are welcome!"},"author":{"name":"responsively-org","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/76c65a388b9d299d/avatar-6a9577cd4f7f.svg","bio":"A modified web browser that helps in responsive web development. A web developer's must have dev-tool. - responsively-org/responsively-app"},"metadata":{"timestamp":"2026-08-06T14:58:07.008Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/QW1yx63km5/media/0.png","type":"image","width":1280,"height":640}],"aiComments":[]},{"id":"web-44c296","shareId":"3bZdEZuvtX","platform":"web","url":"https://medium.com/@esimov/2d-cloth-simulation-in-go-using-gio-gui-b3dfe00b7223","title":"Attention Required!","shareMode":"full","createdAt":1786028201601,"postedAt":"2026-08-06T14:56:35.080Z","publishedDate":"2026-08-06T14:56:35.080Z","archivedAt":"2026-08-06T14:56:37.089Z","archivedDate":"2026-08-06T14:56:37.089Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/3bZdEZuvtX","contentType":"article","content":{"text":"## Sorry, you have been blocked\n\n## You are unable to access medium.com\n\n## Why have I been blocked?\n\nThis website is using a security service to protect itself from online attacks. The action you just performed triggered the security solution. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.\n\n## What can I do to resolve this?\n\nYou can email the site owner to let them know you were blocked. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page."},"author":{"name":"medium.com"},"metadata":{"timestamp":"2026-08-06T14:56:35.080Z"},"media":[],"aiComments":[]},{"id":"web-g1oyk","shareId":"kQcq6W95VR","platform":"web","url":"https://github.com/postlight/mercury-parser","title":"postlight/parser: 📜 Extract meaningful content from the chaos of a web page","shareMode":"full","createdAt":1786027545585,"postedAt":"2026-08-06T14:45:35.430Z","publishedDate":"2026-08-06T14:45:35.430Z","archivedAt":"2026-08-06T14:45:39.046Z","archivedDate":"2026-08-06T14:45:39.046Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/kQcq6W95VR","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/QjfX1bNw8J/media/0.png","contentType":"article","content":{"text":"## Postlight Parser - Extracting content from chaos\n\n[![CircleCI](https://camo.githubusercontent.com/9f7d2da2c76ab7f2afaa5a0b8850b3728f45a93f3a8d59d89707116663fb56f2/68747470733a2f2f636972636c6563692e636f6d2f67682f706f73746c696768742f7061727365722e7376673f7374796c653d73766726636972636c652d746f6b656e3d33303236633262353237643337363737353065373637383732643038393931616562346638663130)](https://circleci.com/gh/postlight/mercury-parser) [![Greenkeeper badge](https://camo.githubusercontent.com/2f13c171cbe9902c792f4e8809c24d62f3c4aeaef944f086ceab52a79db4dbdc/68747470733a2f2f6261646765732e677265656e6b65657065722e696f2f706f73746c696768742f6d6572637572792d7061727365722e737667)](https://greenkeeper.io/) [![Apache License](https://camo.githubusercontent.com/753b222389d983713c05b305cd5ba4a064fcbe8dc060e90da88b74ee0401340a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e7376673f7374796c653d666c61742d737175617265)](https://github.com/postlight/mercury-parser/blob/master/LICENSE-APACHE) [![MITC License](https://camo.githubusercontent.com/7cbe23ce33112919acc72b72214b94731f5f91912e68dbd1cf1446f0ef63a846/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d4954253230322e302d626c75652e7376673f7374796c653d666c61742d737175617265)](https://github.com/postlight/mercury-parser/blob/master/LICENSE-MIT) [![Gitter chat](https://camo.githubusercontent.com/9fac294d3950e751e47ae5c9ec67662626b2e45fd093b08b98c7f950e4a3cb91/68747470733a2f2f6261646765732e6769747465722e696d2f706f73746c696768742f6d6572637572792e706e67)](https://gitter.im/postlight/mercury)\n\n[Postlight](https://postlight.com/) 's Parser extracts the bits that humans care about from any URL you give it. That includes article content, titles, authors, published dates, excerpts, lead images, and more.\n\nPostlight Parser powers [Postlight Reader](https://reader.postlight.com/), a browser extension that removes ads and distractions, leaving only text and images for a beautiful reading view on any site.\n\nPostlight Parser allows you to easily create custom parsers using simple JavaScript and CSS selectors. This allows you to proactively manage parsing and migration edge cases. There are [many examples available](https://github.com/postlight/parser/tree/master/src/extractors/custom) along with [documentation](https://github.com/postlight/parser/blob/master/src/extractors/custom/README.md).\n\n\n\n## How? Like this.\n\n\n\n### Installation\n\n\n\n```\n# If you're using yarn\nyarn add @postlight/parser\n\n# If you're using npm\nnpm install @postlight/parser\n```\n\n\n\n### Usage\n\n\n\n```\nimport Parser from '@postlight/parser';\n\nParser.parse(url).then(result => console.log(result));\n\n// NOTE: When used in the browser, you can omit the URL argument\n// and simply run `Parser.parse()` to parse the current page.\n```\n\nThe result looks like this:\n\n\n\n```\n{\n\"title\": \"Thunder (mascot)\",\n\"content\": \"... **Thunder** is the stage name for the...\",\n\"author\": \"Wikipedia Contributors\",\n\"date_published\": \"2016-09-16T20:56:00.000Z\",\n\"lead_image_url\": null,\n\"dek\": null,\n\"next_page_url\": null,\n\"url\": \"https://en.wikipedia.org/wiki/Thunder_(mascot)\",\n\"domain\": \"en.wikipedia.org\",\n\"excerpt\": \"Thunder Thunder is the stage name for the horse who is the official live animal mascot for the Denver Broncos\",\n\"word_count\": 4677,\n\"direction\": \"ltr\",\n\"total_pages\": 1,\n\"rendered_pages\": 1\n}\n```\n\nIf Parser is unable to find a field, that field will return `null`.\n\n\n\n#### parse() Options\n\n\n\n##### Content Formats\n\nBy default, Postlight Parser returns the `content` field as HTML. However, you can override this behavior by passing in options to the `parse` function, specifying whether or not to scrape all pages of an article, and what type of output to return (valid values are `'html'`, `'markdown'`, and `'text'`). For example:\n\n\n\n```\nParser.parse(url, { contentType: 'markdown' }).then(result =>\nconsole.log(result)\n);\n```\n\nThis returns the the page's `content` as GitHub-flavored Markdown:\n\n\n\n```\n\"content\": \"...**Thunder** is the [stage name](https://en.wikipedia.org/wiki/Stage_name) for the...\"\n```\n\n\n\n##### Custom Request Headers\n\nYou can include custom headers in requests by passing name-value pairs to the `parse` function as follows:\n\n\n\n```\nParser.parse(url, {\nheaders: {\nCookie: 'name=value; name2=value2; name3=value3',\n'User-Agent':\n'Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_1 like Mac OS X) AppleWebKit/603.1.30 (KHTML, like Gecko) Version/10.0 Mobile/14E304 Safari/602.1',\n},\n}).then(result => console.log(result));\n```\n\n\n\n##### Pre-fetched HTML\n\nYou can use Postlight Parser to parse custom or pre-fetched HTML by passing an HTML string to the `parse` function as follows:\n\n\n\n```\nParser.parse(url, {\nhtml:\n'Thunder (mascot)Thunder is the stage name for the horse who is the official live animal mascot for the Denver Broncos\n\n',\n}).then(result => console.log(result));\n```\n\nNote that the URL argument is still supplied, in order to identify the web site and use its custom parser, if it has any, though it will not be used for fetching content.\n\n\n\n#### The command-line parser\n\nPostlight Parser also ships with a CLI, meaning you can use it from your command line like so:\n\n[![Postlight Parser CLI Basic Usage](https://github.com/postlight/parser/raw/main/assets/parser-basic-usage.gif)](https://github.com/postlight/parser/blob/main/assets/parser-basic-usage.gif)\n\n\n\n```\n# Install Postlight Parser globally\nyarn global add @postlight/parser\n# or\nnpm -g install @postlight/parser\n\n# Then\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source\n\n# Pass optional --format argument to set content type (html|markdown|text)\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source --format=markdown\n\n# Pass optional --header.name=value arguments to include custom headers in the request\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source --header.Cookie=\"name=value; name2=value2; name3=value3\" --header.User-Agent=\"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3_1 like Mac OS X) AppleWebKit/603.1.30 (KHTML, like Gecko) Version/10.0 Mobile/14E304 Safari/602.1\"\n\n# Pass optional --extend argument to add a custom type to the response\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source --extend credit=\"p:last-child em\"\n\n# Pass optional --extend-list argument to add a custom type with multiple matches\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source --extend-list categories=\".meta__tags-list a\"\n\n# Get the value of attributes by adding a pipe to --extend or --extend-list\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source --extend-list links=\".body a|href\"\n\n# Pass optional --add-extractor argument to add a custom extractor at runtime.\npostlight-parser https://postlight.com/trackchanges/mercury-goes-open-source --add-extractor ./src/extractors/fixtures/postlight.com/index.js\n```\n\n\n\n## License\n\nLicensed under either of the below, at your preference:\n\n- Apache License, Version 2.0 ([LICENSE-APACHE](https://github.com/postlight/parser/blob/main/LICENSE-APACHE) or [http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0))\n- MIT license ([LICENSE-MIT](https://github.com/postlight/parser/blob/main/LICENSE-MIT) or [http://opensource.org/licenses/MIT](http://opensource.org/licenses/MIT))\n\n## Contributing\n\nFor details on how to contribute to Postlight Parser, including how to write a custom content extractor for any site, see [CONTRIBUTING.md](https://github.com/postlight/parser/blob/main/CONTRIBUTING.md)\n\nUnless it is explicitly stated otherwise, any contribution intentionally submitted for inclusion in the work, as defined in the Apache-2.0 license, shall be dual licensed as above without any additional terms or conditions.\n\n\n\n---\n\n🔬 A Labs project from your friends at [Postlight](https://postlight.com/). Happy coding!"},"author":{"name":"postlight","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/7aa3a023cbcb954c/avatar-6a9577cd4f7f.svg","bio":"📜 Extract meaningful content from the chaos of a web page - postlight/parser"},"metadata":{"timestamp":"2026-08-06T14:45:35.430Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/QjfX1bNw8J/media/0.png","type":"image","width":1200,"height":628}],"aiComments":[]},{"id":"web-pp0yp8","shareId":"yDFRa3TM23","platform":"web","url":"https://www.underwritex.com/#pricing","title":"UnderwriteX - AI Real Estate Underwriting | Analyze Deals in Seconds","shareMode":"full","createdAt":1786024729467,"postedAt":"2026-08-06T13:58:41.976Z","publishedDate":"2026-08-06T13:58:41.976Z","archivedAt":"2026-08-06T13:58:43.934Z","archivedDate":"2026-08-06T13:58:43.934Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/yDFRa3TM23","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/OxqreyB9Vg/media/0.png","contentType":"article","content":{"text":" "},"author":{"name":"UnderwriteX","bio":"Transform your real estate analysis with AI-powered underwriting. Get accurate property valuations and investment insights in seconds."},"metadata":{"timestamp":"2026-08-06T13:58:41.976Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/OxqreyB9Vg/media/0.png","type":"image","width":1200,"height":640}],"aiComments":[]},{"id":"ai-agent-ownership","shareId":"WAiFmpVNh0","platform":"substack","url":"https://natesnewsletter.substack.com/p/ai-agent-ownership","title":"Executive Briefing: Your team is running agents nobody owns. The one-page card and two prompts that fix it.","shareMode":"full","createdAt":1785995065990,"postedAt":"2026-08-06T05:44:19.688Z","publishedDate":"2026-08-06T05:44:19.688Z","archivedAt":"2026-08-06T05:44:21.138Z","archivedDate":"2026-08-06T05:44:21.138Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/WAiFmpVNh0","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/Bcvfwy0Nnv/media/0.jpg","contentType":"article","content":{"text":"Watch now | I have watched too many agents fail because everyone wanted what they could do and nobody wanted to own them. The next real AI skill? Ownership."},"author":{"name":"Nate","handle":"@natesnewsletter","avatar":"https://social-archiver-api.social-archive.org/media/authors/arc/a62141a4e26ee0b2/avatar-dbff3882a817.png","bio":"Daily newsletters on AI strategy, news, and implementation for practitioners and leaders who are past the hype and ready to build. Click to read Nate’s Substack, a Substack publication with hundreds of thousands of subscribers."},"metadata":{"timestamp":"2026-08-06T05:44:19.688Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/Bcvfwy0Nnv/media/0.jpg","type":"image","width":1200,"height":600}],"aiComments":[]},{"id":"web-u8v25u","shareId":"eYnxtIiJmu","platform":"web","url":"https://unlock-ai.natebjones.com/guides","title":"Unlock AI living guides","shareMode":"full","createdAt":1785993423809,"postedAt":"2026-08-06T05:11:50.618Z","publishedDate":"2026-08-06T05:11:50.618Z","archivedAt":"2026-08-06T05:11:50.995Z","archivedDate":"2026-08-06T05:11:50.995Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/eYnxtIiJmu","thumbnail":"https://unlock-ai.natebjones.com/guides/opengraph-image","contentType":"article","content":{"text":"Agents\n\n4 guides\n\n## [The Agent Maintenance Loop](https://unlock-ai.natebjones.com/guides/agents/maintenance)\n\nA repeatable loop for inspecting an agent after launch across job, diet, memory, tools, reach, proof, and value, then deciding whether to keep, change, pause, or retire it.\n\nOpen guide[\n\nAgents\n\n## Find a Real Job for Your First AI Agent\n\nTurn one repeated frustration into a written Pain Note, a pattern you have checked yourself, an honest verdict on whether an agent belongs here, and a draft-only pilot you can measure.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/first-agent-job)[\n\nAgents\n\n## The One-Minute Test\n\nRoute any task to chat, one accountable agent, a small agent team, or the manual path — in about a minute, with a next action for whichever route wins.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/the-one-minute-test)[\n\nAgents\n\n## Automation Discovery\n\nA downloadable agent skill that reads your real work traces, audits the evidence, and offers 2–5 automations you can verify against your own history. You pick; it builds and proves only that.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/automation-discovery)\n\nCodex\n\n4 guides\n\n[](https://unlock-ai.natebjones.com/guides/codex)\n\n[Codex\n\n## The Ultimate Guide to Codex\n\nInstall OpenAI's Codex app, set up real projects, and run an AI-first workflow where every move ships with a copy-paste prompt.\n\n](https://unlock-ai.natebjones.com/guides/codex)\n\n[Open guide](https://unlock-ai.natebjones.com/guides/codex)[\n\nCodex\n\n## Codex side panel deep-dive\n\nFiles, Side chat, Review, Terminal, and Browser: the panel that turns Codex from a chat box into a workbench.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/codex/side-panel)[\n\nCodex\n\n## Codex browser & annotations\n\nClick any element, attach a note or screenshot, and steer Codex from inside the page you are building.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/codex/browser-annotations)[\n\nCodex\n\n## Codex threading & child threads\n\nThreads, child threads, steering versus queueing, and the workspace habits that keep long-running agent work sane.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/codex/threading)\n\nGeneral\n\n13 guides\n\n[](https://unlock-ai.natebjones.com/guides/make-any-skill-your-own)\n\n[General\n\n## Make Any Skill Your Own\n\nSkills are folders of plain text. Watch a real one work, download it, read it with your own eyes, and rewrite the lines that were never yours.\n\n](https://unlock-ai.natebjones.com/guides/make-any-skill-your-own)\n\n[Open guide](https://unlock-ai.natebjones.com/guides/make-any-skill-your-own)[\n\nGeneral\n\n## Open Stack\n\nA practical routing guide for Open Skills, Open Brain, and Open Engine: diagnose the bottleneck, choose where to start, and personalize the stack.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/open-stack/open-stack-field-guide)[\n\nGeneral\n\n## Open Brain — one memory, every AI\n\nWhat Open Brain is, how the one-database memory layer works, and the 30-minute, zero-code path to building yours — with the full guide on GitHub.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/open-brain)[\n\nGeneral\n\n## Ringer — swarm power without the frontier bill\n\nRun ultracode-style multi-agent swarms on inexpensive models: your best model plans and reviews, cheap cross-model workers implement in parallel, and exit codes decide what passed.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/ringer)[\n\nGeneral\n\n## How to Run a Chinese-Model Bakeoff in Ringer\n\nGive a Chinese model and your current model the same real job, the same sources, and the same pass/fail check, then see which one earns the work.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/chinese-model-bakeoff)[\n\nGeneral\n\n## Clean My AI Harness\n\nFind the hidden setup shaping your AI, see what protects your work and what creates drag, and clean it safely with numbered approval and full rollback. Claude and Codex editions.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/harness-cleaner)[\n\nGeneral\n\n## Build your own token-burn dashboard\n\nTrack where your tokens go across Codex, Claude, and ChatGPT, then use the burn rate as a fluency signal.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/build-your-own-token-burn-dashboard)[\n\nGeneral\n\n## Build a Healthcare Claim Appeals Agent\n\nAssemble Open Skills primitives into an agent that turns your denial letter, EOB, and plan documents into a cited appeal packet.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/build-a-healthcare-claim-appeals-agent)[\n\nGeneral\n\n## Build a Tax Prep Organizer Agent\n\nUse the same document-grounded primitives to turn your tax documents and real IRS sources into a structured, CPA-ready prep packet.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/build-a-tax-prep-organizer-agent)[\n\nGeneral\n\n## Build an Email Follow-Up Agent\n\nPoint the same document-grounded primitives at your inbox: a live MCP ingest loop that reconstructs threads, keeps a commitments ledger, and drafts cited follow-ups that stop at the send boundary.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/build-an-email-follow-up-agent)[\n\nGeneral\n\n## Clean Sensitive Docs with a Local Model\n\nDownload Gemma 4 E2B in LM Studio, save a tested redaction system prompt as a preset, and turn documents you can't upload into proposed redacted copies — offline, on your own machine.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/clean-sensitive-docs-locally)[\n\nGeneral\n\n## AI Airlock: Use AI Without Leaking Sensitive Documents\n\nInstall AI Airlock on an Apple Silicon Mac, review what it removes, seal a verified working copy, and use that copy with ChatGPT, Claude, or another AI tool.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/ai-airlock)[\n\nGeneral\n\n## 15 Ways to Cut Token Waste in Codex and Claude\n\nWhat each move saves, why it works, and what proves it — from editing a bad turn instead of correcting it, to turning exact work into code, to the free Token Saver skill. One matched job went from 51,712 reused input tokens to zero.\n\nOpen guide](https://unlock-ai.natebjones.com/guides/cut-token-waste)"},"author":{"name":"Nate B. Jones","bio":"Practical Unlock AI guides for building, measuring, and operating with AI tools."},"metadata":{"timestamp":"2026-08-06T05:11:50.618Z"},"media":[{"url":"https://unlock-ai.natebjones.com/guides/opengraph-image","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-5990ue","shareId":"2C0HUtQA5K","platform":"web","url":"https://unlock-ai.natebjones.com/guides/cut-token-waste","title":"15 Ways to Cut Token Waste in Codex and Claude","shareMode":"full","createdAt":1785993414189,"postedAt":"2026-07-28T00:00:00.000Z","publishedDate":"2026-07-28T00:00:00.000Z","archivedAt":"2026-08-06T05:16:51.135Z","archivedDate":"2026-08-06T05:16:51.135Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/2C0HUtQA5K","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/Uxvh8gHmWM/media/0.png","contentType":"article","content":{"text":"## What is actually inside the request you just sent\n\nYou typed a sentence. The call that carries it can also contain the old conversation, every earlier answer, the tool results from twenty turns ago, the files and screenshots you attached, the descriptions of every tool your harness has loaded, your standing instructions, hidden setup, and the answer the model is about to write. If that answer fails a check, the next attempt can carry most of it again.\n\nThat is why \"use a cheaper model\" so often changes nothing. The pile moves; it does not shrink. Every model call gives you three places to cut — old material carried forward, new material loaded for this job, and the output, tool results, and retries added afterward — and a fourth lever, caching, that lowers the price of what is left without removing it.\n\nThe 15 moves below are sorted by which of those they attack. Some remove old context. Some reduce new input. Some shorten output. Some prevent an entire model call. One only changes the price.\n\n## The result this guide is anchored to\n\nThe same Codex job was run two ways, and both versions had to pass the same checklist of required facts and structure.\n\n**The normal worker** made three model calls and reported 51,712 reused input tokens, 81,276 provider-reported input plus output, and 515 output tokens.\n\n**The clean worker** was handed only the facts it needed. It made one call, reported zero reused input, 11,565 input plus output, and 131 output tokens — a reduction of 85.77% on the matched job.\n\n**Then the model came out entirely.** A separate Python selector had already reduced a 1.1 MB source file to a 1,012-byte fact file. Starting from that file, ordinary code wrote the required result and ran the same check in about a tenth of a second, with zero model calls and zero model tokens.\n\nHow we know, and what it does not prove: the check confirmed the required facts and the shape of the answer. It did not prove that two pieces of writing were of identical quality. A single zero-reused-input record is also not proof about every hidden byte the product sent. This is one matched job, not a full day.\n\n## The 3.77 billion that started this\n\n[Build the tracker that produced this number ↓](https://unlock-ai.natebjones.com/guides/build-your-own-token-burn-dashboard)\n\nThe July 26 reading on a local Token Burn tracker attributed 3.77 billion tokens to one day of Codex work, of which 3,588,555,136 were reported as reused input — 95.73%. That day covered 143 Codex threads and 28,877 local usage updates.\n\nTreat that as an alarm, not an invoice. It comes from a local event log that includes cumulative updates, so it is not an OpenAI bill and not a count of unique calls. It needs one fixed duplicate-removal rule before whole days can be compared. The matched job above is the cleaner evidence; the 3.77 billion is what made anyone go looking.\n\n## 1. Edit or rewind a bad turn instead of stacking a correction on it\n\n**Do this.** When a response goes wrong because your request was unclear, do not send \"no, that is not what I meant.\" Edit the original request, or rewind to the point before the mistake, and run it again. In Claude Code, the /rewind command takes you back. In a chat product with editable messages, edit the message that caused the problem. Use this when the branch is simply wrong — if you learned something and the work genuinely moves forward, send the follow-up.\n\n**What it saves.** The entire rejected branch: the unclear request, the wrong answer, your correction, and the extra turn that would otherwise be carried into every later call.\n\n**Why it works.** A follow-up does not replace a mistake. It appends to it. The conversation formats these products use include earlier user and assistant messages in later requests, so the bad branch keeps arriving.\n\nHow we know: removing a branch makes the next request smaller by exactly the size of that branch, which varies. Check the next usage record rather than assuming a fixed percentage.\n\n## 3. Start a clean task when the job changes\n\n**Do this.** Keep a long conversation while you are still working on one problem. The moment you switch to a different job, start a clean Codex task or Claude chat.\n\nA support case should not inherit a video research conversation. A spreadsheet fix should not inherit a long brand argument. A new customer should not inherit the last customer's account history. You are not throwing the old task away — you are declining to make the next job carry it.\n\n**What it saves.** Old conversation, old tool results, old files, and instructions that belong to a different job.\n\n**Why it works.** A clean task cannot reread a conversation that was never added to it. This is the bluntest instrument on the list and usually the most effective.\n\nHow we know: in the matched job, the normal worker reported 51,712 reused input tokens and the clean worker reported zero while passing the same checklist. That applies to that job. Codex and Claude still carry their own starting instructions, so do not expect every clean task to report zero.\n\n## 4. Carry forward the answer you kept, not the argument that produced it\n\n**Do this.** Save the latest approved draft, decision, plan, or file. For the next change, hand the model that result plus the new instruction: \"Here is the version we kept. Change the second section so it includes the customer example. Leave the rest alone.\"\n\nDo not also include the rejected first draft, the three rounds of criticism, the pages you discarded, the model's reasoning, and every correction that preceded the accepted version.\n\n**What it saves.** The entire history of how the result was made — which is usually far larger than the result.\n\n**Why it works.** Most revisions need the current state and the requested change. They do not need the path that produced the current state.\n\nHow we know: the Token Saver skill below includes a tested state tool that builds the next request from one accepted result plus one new change, and its tests exclude the prior conversation from that packet. How much you save depends on how large the omitted history was.\n\n## 6. Send the lightest useful form of a source\n\n**Do this.** Send text as text when the page design does not matter. Extract the text from a large PDF once. Crop a screenshot to the region the model needs instead of sending the full page. Send the twelve changed rows instead of the whole spreadsheet. Send a diff instead of two near-identical files.\n\nKeep the original available in case the smaller version is not enough. You are not destroying information — you are choosing the smallest useful form for this request.\n\n**What it saves.** New input from page furniture, repeated headers, blank areas, duplicate rows, image detail, and unchanged material.\n\n**Why it works.** Material that never enters the request cannot consume model tokens. This sounds obvious until you watch a browser agent carry full-page screenshots through thirty turns because it needed one account number.\n\nHow we know: measure the provider-reported input before and after. There is no honest universal percentage here — a dense chart and a blank PDF page tokenize very differently.\n\n## 7. Turn work that has one right answer into code\n\n**Do this.** Use a parser, a formula, a database query, a formatter, a test, or a short script for exact work. \"Use only July 26\" is a date filter. \"Reduce this number by 90%\" is a formula. \"Include these four facts\" is a check. \"Stop if a fact is missing\" is an error. \"Return these fields in this order\" is a formatter.\n\nDo not pay a model to count, sort, find exact text, normalize dates, compare files, convert formats, or repeat the same search-filter-calculate-format sequence every day. Let the model handle the part that genuinely needs language or judgment.\n\n**What it saves.** The input, output, reasoning, tool calls, and retries for that entire part of the job.\n\n**Why it works.** A fixed rule does not need to be explained to a model and reinterpreted on every run. The computer can just run the rule. Software has worked this way forever; the new part is noticing how much work we keep sending back to a model after it has already become a repeatable rule.\n\nHow we know: starting from the prepared 1,012-byte fact file, the code path wrote the required result and passed the same checklist with zero model calls and zero model tokens.\n\n## 8. Return an accepted answer instead of regenerating it\n\n**Do this.** If the exact request and its source have not changed, return the answer a person already accepted. No model call, no new variation for a human to re-check.\n\nBe strict about this, because a fuzzy text match is dangerous. The request, the relevant source, and the accepted result must all match, and a changed source must force a fresh run. Never treat an earlier result as accepted unless a person actually accepted it.\n\n**What it saves.** One complete model call.\n\n**Why it works.** The answer already exists. Asking a model to recreate it costs tokens and can make it worse.\n\nHow we know: Ringer's pre-call code stores only explicitly accepted results and can return an exact match with zero upstream model calls; its tests change the source and confirm the old answer is not reused. That is a tested local capability, not something every AI product does for you.\n\n## 9. Load only the tools this job can use\n\n**Do this.** A writing task does not need every browser, database, email, Slack, GitHub, and calendar tool you own. Keep a few common tools available and load the rest only when a request needs them. If your host supports tool search or deferred loading, use it for large catalogs. If a job needs no tools, give it none.\n\n**What it saves.** Tool descriptions at the start of the request, and tool results on every later turn.\n\n**Why it works.** Every tool ships a name, an explanation, and an argument schema. All of it is model input even when the model never calls that tool.\n\nHow we know: Anthropic reports that a typical multi-server setup can start with roughly 55,000 tokens of tool definitions, and that its tool-search tool generally cuts that definition load by more than 85% by loading only the three to five tools a request appears to need. Anthropic recommends it once you have about ten tools or more than 10,000 tokens of definitions. Source: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool\n\n## 10. Clean up old tool output during a genuinely long job\n\n**Do this.** Sometimes a clean task is the wrong answer. You are three hours into debugging one system and the model needs decisions it made forty minutes ago. That is where compaction and context editing belong: remove or compact the old browser pages, command output, screenshots, and reasoning the next step no longer needs, while keeping the state it does.\n\nA browser page dumped 20,000 tokens into the conversation and the next step needs the account ID from it. Keep the ID. Drop the page.\n\nDo not compact a five-message chat because the feature exists, and do not remove a fact the next step still needs.\n\n**What it saves.** Reused input on every later step of the same long job.\n\n**Why it works.** The next request carries a smaller representation of the earlier work instead of every raw intermediate result.\n\n**The tradeoff.** Editing old material can break a prompt-cache match. That is fine when you remove far more waste than the cache discount was saving — but you have to compare the whole job, not the one call.\n\nHow we know: OpenAI supports compaction for long-running API work and says the compaction item preserves the state later turns need using fewer tokens (https://developers.openai.com/api/docs/guides/compaction). Anthropic supports context editing that can clear old tool results and thinking blocks (https://platform.claude.com/docs/en/build-with-claude/context-editing). The actual saving depends on what your host keeps, so verify the next request's input count.\n\n## 11. Ask for only the answer you need\n\n**Do this.** Ask for one file, one JSON object, five bullets, or one replacement paragraph. Set a short output limit when the interface supports it. Do not request a process diary unless you are going to read it.\n\nThis is not an argument for caveman prose. If the writing is the product, give it the space it deserves. Remove the words nobody will use.\n\n**What it saves.** Output now, and input later — because the model's answer often becomes part of the next request.\n\n**Why it works.** Every generated word is a token when it is written and may be a token again on every subsequent turn.\n\nHow we know: in the matched job the clean worker produced 131 output tokens and the normal three-call worker produced 515. Shorter output was not the whole win, but it helped twice: once immediately, and again by leaving less text for later calls to carry.\n\n## 13. Put a hard stop on oversized calls and retries\n\n**Do this.** Set explicit limits before the call for fresh input, reused input, output length, number of model calls, and number of repair attempts.\n\nIf the answer is missing one required fact, one small repair with that fact may make sense. If the request itself is over the limit, do not send it again. Narrow the source, drop the tools this job cannot use, move the exact steps into code, and send a smaller request to a clean worker.\n\n**What it saves.** Duplicate calls, retry loops, and runaway output.\n\n**Why it works.** A second copy of an oversized request is still oversized. A token-limit failure cannot be repaired by repeating the same call.\n\nHow we know: an early Ringer path retried after a token-limit failure, and the second call could not make the first request smaller — it only repeated the waste. The pre-call code now has separate limits for fresh input, reused input, output, and calls, and its tests confirm a failed or over-limit call does not silently fall through to another model.\n\n## 14. Cache only the background you genuinely cannot remove\n\n**Do this.** When background truly must repeat, keep the reusable instructions and reference text unchanged at the very beginning of the request, and put the new assignment at the end. Do not rewrite or reorder the repeated part.\n\nIn plain English: if the beginning of a request exactly matches an earlier one, the provider may recognize it and charge a lower price for that repeated beginning.\n\n**What it saves.** Money, and often latency, on repeated API input. It does not save tokens.\n\n**Why it works.** Both major providers can reuse work already done on an exact matching prefix.\n\n**Why it is last.** Cached does not mean gone. If you are carrying 100,000 tokens of background that could be cut to 5,000, cut it — do not keep it because the provider might discount it. Caching a huge request makes the huge request cheaper. It does not make it small.\n\nHow we know: OpenAI's API automatically checks eligible prompts of at least 1,024 tokens, requires an exact matching prefix, recommends putting changing material last, and reports cached_tokens in the response (https://developers.openai.com/api/docs/guides/prompt-caching). Anthropic lets you mark the repeated material, prices a cache read at one tenth of normal input, and its own example still counts 100,000 cached tokens plus 50 new tokens as 100,050 total processed input tokens (https://platform.claude.com/docs/en/build-with-claude/prompt-caching).\n\n## 15. Put a gateway in front of the call when you need control before the model starts\n\n[Ringer setup, the ask command, and the manifest fields ↓](https://unlock-ai.natebjones.com/guides/ringer)\n\n**Do this.** For a Codex CLI workflow that would otherwise send a large request before any skill can help, use Ringer's optional local gateway. You keep typing in Codex — there is no separate Ringer chat window. The gateway sits between Codex CLI and the model provider, and before forwarding a request it can return an exact accepted answer with no model call, run a fixed local recipe with no model call, select only the relevant source passages, forward a reduced request to a cheaper or stronger model under hard limits, or stop before spending tokens at all.\n\n**What it saves.** Input the host would otherwise send before your selected skill even begins, plus entire calls that code or an accepted result can answer.\n\n**Why it works.** A skill is loaded inside a model call. It can make everything after it smaller, but it cannot reach backward and erase the conversation, hidden setup, and tool descriptions that already started the turn. A gateway acts before the provider receives the call.\n\n**Where it stops.** The verified gateway path today covers Codex CLI text-answer turns. The Anthropic request adapter passes its unit tests, but a live Claude Code 2.1.132 request retried after a gateway stop, so that path is not being called compatible yet — Claude Code users should use the standalone skill below. It also requires provider configuration and API credentials, those calls may bill against your API account rather than the subscription in the consumer app, and a complex tool-using job may still need the full normal environment.\n\nHow we know: the gateway and router are covered by tests for exact accepted answers, local code paths, selected-source forwarding, per-call token records, limits, and no-retry-after-failure. A real Codex CLI request returned the answer from Ringer's local calculator and reported zero model tokens.\n\nThe download below is the skill itself: SKILL.md, an agent config for Codex, and three local Python helpers — a passage selector that gives the model only the matching parts of a large source, a state tool that saves one accepted result and builds the next request from that result plus your change, and a context-packet builder. Ringer is not required. The skill works standalone in both Codex and Claude Code.\n\n## Install it once in Codex and Claude Code\n\n[Download the Token Saver skill ↓](https://unlock-ai.natebjones.com/guides/cut-token-waste/starter-kits/token-saver-skill.zip)\n\nDownload the zip and unpack it into wherever your harness loads skills from — ~/.claude/skills/token-saver/ for Claude Code, ~/.codex/skills/token-saver/ for Codex. The folder should end up with SKILL.md at its top level and the scripts and agents folders beside it. Nothing else needs configuring: the skill resolves its own script paths from wherever the harness loaded it.\n\nAfter that, keep working normally. Ask for it by name when a job involves plan limits, token use, a long chat, or a large file: **\"Use the Token Saver skill for this job.\"**\n\nThe zip contains the skill only. Copy it into your skills directory yourself rather than running an installer — that keeps you in control of which harness gets it.\n\n## What the agent does once the skill is loaded\n\nIt finds the likely source from attached paths, named files, and local search rather than asking you to pick. It selects the relevant passages and sends those instead of the whole file. It runs exact work as code before reaching for another model. It saves the version you accepted, and builds the next revision from that result plus your new change — so you never restate the work. It avoids loading tools the job cannot use, chooses a smaller model only when the whole job wins, keeps the answer to the length you asked for, and stops retrying token-limit failures.\n\nIf it cannot identify the source safely, it asks you. You do not have to summarize an old chat, choose files by hand, create a state file, learn script paths, or understand Ringer.\n\n## The honest limit\n\n[Token Saver in the Open Skills library ↓](https://unlock-ai.natebjones.com/open-skills/context-engineering)\n\nLoading the skill adds a small amount of instruction to the call you are in. It earns that cost only when it prevents a larger file load, tool catalog, retry, over-long answer, or later context reload.\n\nMost importantly, it cannot erase the current call. That call has already begun. The skill makes the work that follows smaller. If you need the request changed before the model sees it, that is the optional gateway in move 15.\n\n## Add the whole job, not the call you are proud of\n\nCount fresh input, reused or cached input, cache writes, output, reasoning output where it is reported, every planning and checking and repair call, and the human repair time when you are comparing models. If a host does not report a number, write it down as unavailable rather than inventing it.\n\nThree things that look like wins and are not: moving 50,000 tokens from Codex to Claude is not a token saving. Paying one tenth of the price for 50,000 cached tokens is a cost saving, not a 90% reduction in tokens. And a shorter answer that leaves out half the work is not a win at all.\n\n## Start with four tomorrow, not fifteen\n\nWhen a turn goes wrong, edit or rewind instead of building on the mistake. When the job changes, open a clean task. When a file is large, search it before the model reads it. When a step has one right answer, make it code.\n\nThen install the skill and let it handle the boring parts. Your job is not to use a weaker AI or do less useful work — it is to stop making the AI reread, reload, regenerate, and rediscover things the current result does not need.\n\n## What is still an open goal\n\nThe full-day target is a 90% reduction in reported reused input while keeping the same amount and quality of finished work. Against the July 26 baseline that would mean bringing 3,588,555,136 reused input tokens down to no more than 358,855,513 on a comparable day.\n\nThat full-day result has not been reached. What has been shown is that on one matched job, reported reused input fell to zero while the required facts and structure survived. One job is not a day, and this guide will say so until the day is done."},"author":{"name":"Nate B. Jones","bio":"What each move saves, why it works, and what proves it — from editing a bad turn instead of correcting it, to turning exact work into code, to the free Token Saver skill. One matched job went from 51,712 reused input tokens to zero."},"metadata":{"timestamp":"2026-07-28T00:00:00.000Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/Uxvh8gHmWM/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-dov00f","shareId":"HGqNyP1xpC","platform":"web","url":"https://unlock-ai.natebjones.com/guides/ai-airlock","title":"AI Airlock: Use AI Without Leaking Sensitive Documents","shareMode":"full","createdAt":1785993411419,"postedAt":"2026-08-06T05:16:46.575Z","publishedDate":"2026-08-06T05:16:46.575Z","archivedAt":"2026-08-06T05:16:48.268Z","archivedDate":"2026-08-06T05:16:48.268Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/HGqNyP1xpC","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/YwHMclhkoZ/media/0.png","contentType":"article","content":{"text":"## A local checkpoint between the document and the chatbot\n\nAI Airlock is a Mac app from NBJ Media. Version 0.2.2 is free during early release and proprietary source-available: its code is public, but it is not an open-source release.\n\nThe app creates a sanitized, verified working copy before you paste anything into ChatGPT, Claude, or another AI tool. The original is never modified.\n\n## The scan stays on the Mac\n\nProcessing is 100% on-device inside a network-blocked window. The Gemma 3 1B model ships inside the roughly 880 MB download. There is no account and no telemetry.\n\nAI Airlock requires Apple Silicon (M1 or newer) and macOS 13 or newer.\n\n[View the public source →](https://github.com/NateBJones-Projects/ai-airlock)\n\n\n\n## Download, check the file, and drag it in\n\nDownload the v0.2.2 DMG from the releases page. Before you open it, confirm you got the real file: the download page lists a SHA-256 checksum, and in Terminal `shasum -a 256 ~/Downloads/AI-Airlock-0.2.2-arm64.dmg` should print the same value. That check is what makes the next step safe. Then open the DMG and drag AI Airlock into Applications.\n\nThe checksum is the real security control here — verifying it matters more than the Apple signature, which this early build does not yet carry.\n\n## Get past the “can’t be opened” warning\n\nThe first time you open AI Airlock, macOS refuses and shows a warning — “Apple could not verify…”, “cannot be opened because the developer cannot be verified,” or on some Macs “damaged and can’t be opened.” All three mean the same thing: the app isn’t notarized yet. Point-and-click path: open the app once and let it get blocked, then go to System Settings → Privacy & Security, scroll down to the AI Airlock message, click Open Anyway, and confirm. (The Open Anyway button only appears after that first blocked attempt, and recent macOS removed the old right-click → Open shortcut, so this is the way.)\n\nIf Open Anyway is greyed out, or you got the “damaged” message, use the fallback below instead — it is the reliable fix.\n\n## Reliable fallback: clear the download quarantine flag\n\nWhen the point-and-click path won’t cooperate, run this one line in Terminal. It removes the “downloaded from the internet” quarantine tag that Gatekeeper is reacting to, then the app opens normally. This is safe here specifically because you verified the checksum, the source is public, and AI Airlock never touches the network — so the quarantine flag isn’t protecting you from anything you haven’t already checked yourself.\n\n```\n\n\nxattr -dr com.apple.quarantine \"/Applications/AI Airlock.app\"\n\n\n```\n\nOnly run this against an app whose checksum you verified in the previous step. Once Apple notarization is added in a later build, none of this will be necessary.\n\n## Or hand the whole thing to an agent\n\nIf you’d rather not touch Terminal yourself, paste this into an agent that can operate your Mac. It makes the checksum a hard stop, installs the app in /Applications, clears the quarantine flag for you, and confirms the app opens before reporting success — the entire install and the signing workaround in one step.\n\nShow the full prompt\n\n```\n\nInstall AI Airlock on my Mac (Apple Silicon only).\n\n1. Download https://github.com/NateBJones-Projects/ai-airlock/releases/download/v0.2.2/AI-Airlock-0.2.2-arm64.dmg\n2. Verify the SHA-256 equals 5490105df80ae3e31f9f42fa5bf4c0d0152ee14aff9030df68dcea0dd290a65b. Refuse to continue on mismatch.\n3. Mount the DMG, copy \"AI Airlock.app\" into /Applications, then unmount the DMG.\n4. This early build is ad-hoc signed. Clear the quarantine flag so it can launch:\nxattr -dr com.apple.quarantine \"/Applications/AI Airlock.app\"\n5. Launch it once to confirm it opens.\n\nReport done only after the app launches successfully.\n\n```\n\n[Open the AI Airlock releases →](https://github.com/NateBJones-Projects/ai-airlock/releases)\n\n\n\n## 01 — Add the document\n\nPaste text or drop a TXT, Markdown, or text-only DOCX file into the app. The limit is 25 MB or roughly 300,000 characters. You can also add protected terms — client names, codenames, or any other specific words that should never leave the copy.\n\n## 02 — Scan for hard secrets and sensitive meaning\n\nDeterministic, validated rules find keys, card numbers, Social Security numbers, emails, and phone numbers. The local model separately flags passages whose meaning may be sensitive: strategy, financials, client material, HR, unreleased work, and legal or security content.\n\n## 03 — Review every finding\n\nEvery finding appears with a reason. Hard secrets are locked to Hide. Judgment calls let you choose Hide or Leave visible, with Hide pre-selected. The semantic layer proposes; you make the decision.\n\n## 04 — Seal the working copy\n\nAI Airlock replaces hidden material with typed placeholders such as [EMAIL_01], then re-opens and re-verifies its own output. If anything marked for removal still leaks through, the app refuses to save.\n\nA successful seal creates a new file and a removal note containing categories and counts only. It shows the original and cleaned filenames with their SHA-256 hashes, and warns when the destination folder looks cloud-synced. The original file is never changed.\n\n## Paste this instruction above the CLEANED copy\n\nThe placeholder names carry useful structure without exposing the removed text. This snippet tells the assistant to treat them as protected redactions, not missing context it should try to reconstruct.\n\nShow the full prompt\n\n```\n\nThe document below is a CLEANED working copy produced before it was shared with this AI tool.\n\n- Placeholders such as [EMAIL_01] and [CONFIDENTIAL_PASSAGE_REMOVED_01] are deliberate redactions.\n- Preserve every placeholder exactly as written.\n- Do not guess, reconstruct, infer, rename, expand, or remove anything represented by a placeholder.\n- Keep the placeholders intact in any rewritten, summarized, reformatted, or otherwise transformed output.\n\nPaste the CLEANED copy here.\n\n```\n\n\n\n## Unsupported content stops the run\n\nEncrypted files, macros, images, and embedded objects are blocked with an explanation. They are never silently skipped. PDFs, image-based documents, and spreadsheets are not supported in v0.2.2.\n\n## Sanitized does not mean authorized\n\nEvery receipt says authorization NOT_ASSESSED. AI Airlock can create and verify a cleaner copy; it cannot decide whether your company, client, or policy permits the work.\n\n## What this release cannot promise\n\nAI Airlock is Mac-only and not a compliance product. The semantic layer proposes findings and a human decides what stays visible, so there is no perfect-catch guarantee. Review the findings and the sealed copy like the decision still belongs to you — because it does.\n\n## Want the manual version of the same idea?\n\nThe sibling guide shows the DIY route with LM Studio and Gemma 4 E2B. AI Airlock is the packaged, verified version of that same local-first document-cleaning idea.\n\n[Build the manual local doc cleaner →](https://unlock-ai.natebjones.com/guides/clean-sensitive-docs-locally)"},"author":{"name":"Nate B. Jones","bio":"Install AI Airlock on an Apple Silicon Mac, review what it removes, seal a verified working copy, and use that copy with ChatGPT, Claude, or another AI tool."},"metadata":{"timestamp":"2026-08-06T05:16:46.575Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/YwHMclhkoZ/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-5poaxq","shareId":"adjyVJkHl9","platform":"web","url":"https://unlock-ai.natebjones.com/guides/clean-sensitive-docs-locally","title":"Clean Sensitive Docs with a Local Model","shareMode":"full","createdAt":1785993410602,"postedAt":"2026-08-06T05:16:45.545Z","publishedDate":"2026-08-06T05:16:45.545Z","archivedAt":"2026-08-06T05:16:47.420Z","archivedDate":"2026-08-06T05:16:47.420Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/adjyVJkHl9","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/r9o3vtzqFl/media/0.png","contentType":"article","content":{"text":"## The work you've been doing by hand\n\nEveryone has a file they would never paste into a chatbot — a contract, a board deck, a client record, an HR document. AI could obviously help, but the file can't leave, so the work stays manual or doesn't happen at all.\n\nThis guide is the hands-on lane of Nate's executive briefing on using AI on work you cannot upload. The briefing covers why companies like Bayer and Discovery Bank are paying to escape this exact trade-off, where the laptop stops being enough, and the questions to ask before trusting any AI product with a private file. Here, we set up the laptop half: a local model with one job — producing a proposed redacted copy of a document for you to check.\n\n## How a local model keeps the document private\n\nA local model is a file of numbers — the weights — that you download once and run on your own machine. When you send it a document, the inference happens in your laptop's RAM and GPU. There is no account, no API call, and no per-token bill, because there is no server on the other end.\n\nThat is the whole privacy argument: with the model and document on the same machine, chat and document processing work entirely offline. You can pull up WiFi settings and switch the network off, and the model keeps answering. Nothing you paste can travel anywhere.\n\nLocal processing removes one route off your laptop. It doesn't audit the app around the model, make the machine compliant, or replace your company's rules about where a file may live. For a real document, use a machine and workflow you're authorized to use.\n\n## Why this one\n\nGemma 4 is Google DeepMind's open-weight model family, released March 31, 2026 under the Apache 2.0 license. E2B is the \"effective 2B\" member: it has about 4.6B parameters, but a per-layer embeddings trick gives it the memory footprint of a 2B-class model — the download is ~4.4 GB, and it runs comfortably on a 16 GB laptop. Despite the size it handles text and images, has a reasoning mode, supports tool calling, and takes a 128K-token context, which matters when the document you're cleaning runs long.\n\nThe honest sizing rule: pick the model by your machine's RAM, not by benchmark charts. LM Studio flags what fits (\"Full GPU Offload Possible\" is the green light), and a model that technically loads but takes twenty minutes to answer will never become part of your real workflow.\n\n## What a model this size is — and isn't — for\n\nDon't ask a 4.6B-parameter model to write your code or reason through open-ended strategy; models small enough to download to a laptop aren't big enough for that. What they are good at is a narrow, well-specified job with a strong system prompt — and redacting sensitive strings from a document is exactly that shape. In our testing, E2B caught disguised items a keyword search would miss: a phone number written out in words, an SSN with the dashes removed, an obfuscated email address.\n\nThis assumes LM Studio is already installed — lmstudio.ai has the installer and its own setup guide.\n\n## Search for Gemma 4 E2B\n\nOpen Model Search (the magnifying glass in the left rail), search \"gemma 4 2b\", and pick Gemma 4 E2B — the entry published by Google (google/gemma-4-e2b), not the community re-uploads. The detail pane confirms what you're getting: 5.1B params, vision, tool use, reasoning, GGUF and MLX builds.\n\n![LM Studio Model Search showing staff picks with the google/gemma-4-e2b detail pane open.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-model-search.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\n## Pick a format and size\n\nUnder Download Options you'll see two formats: MLX is Apple's machine-learning framework — take it on a Mac; GGUF runs everywhere else (and also on Macs). The quantization variants trade size for fidelity — the ~4.4 GB 4-bit build is the right default. If LM Studio shows \"Full GPU Offload Possible\", your machine can run that variant at full speed.\n\n![LM Studio download options for Gemma 4 E2B showing GGUF and MLX variants from about 4.4 to 6 GB.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-download-options.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\n## Download, then load it into a chat\n\nClick Download and wait it out. When it finishes, LM Studio offers \"Use in New Chat\" — take it, and the model loads into a fresh conversation. Say hello to confirm it's alive; small models answer fast.\n\n![LM Studio download-completed toast for google/gemma-4-e2b with a Load Model button.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-download-complete.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\n## Open the system prompt editor\n\nOpen the right side panel and switch to the settings tab (the sliders icon). Under System Prompt, click Editor to get a full editing pane — the small field works, but you're about to paste something long.\n\n![LM Studio settings panel showing the System Prompt field with its Editor button.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-system-prompt-settings.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\n## Paste the Doc Cleaner prompt\n\nThis is the prompt we validated against Gemma 4 E2B on a synthetic memo seeded with 20+ planted sensitive items — including a phone number spelled out in words, an SSN without dashes, and a deal-target company name hiding in running prose. The final version caught all of them. The typed placeholders ([NAME], [SSN], [CREDENTIAL], …) keep the cleaned document readable, and the consistency rule stops a name that was redacted in one paragraph from leaking in another.\n\nShow the full prompt\n\n```\n\nYou are a data-privacy redaction system. The user's message contains a document (pasted or attached as a file). Your only job is to return that document with every piece of sensitive information redacted. You never answer questions about the document, never summarize it, and never add commentary — you only return the cleaned document.\n\nRedact every instance of the following, replacing each with a typed placeholder in square brackets:\n\n1. People and identity — names of real people → [NAME]; dates of birth or other dates that identify a person → [DOB]; job titles that identify a specific person → [ROLE].\n2. Contact details — email addresses, including obfuscated forms like \"name at company dot com\" → [EMAIL]; phone numbers in any format, including numbers written out as words → [PHONE]; street or mailing addresses → [ADDRESS].\n3. Government and financial identifiers — Social Security or national ID numbers, with or without dashes → [SSN]; credit or debit card numbers in any format → [CARD]; bank account and routing numbers → [ACCOUNT]; salaries, offers, and confidential deal amounts → [AMOUNT].\n4. Credentials and technical identifiers — API keys, tokens, and passwords → [CREDENTIAL]; private keys → [PRIVATE KEY]; IP addresses → [IP]; MAC addresses → [MAC]; GPS coordinates → [COORDINATES]; cryptocurrency addresses → [CRYPTO]; WiFi network names and passwords → [WIFI].\n5. Medical information — patient names, medical record numbers, insurance IDs, provider names, and every diagnosis and medication name (e.g. drug names and conditions) → [MEDICAL].\n6. Confidential business information — internal project codenames → [PROJECT]; names of companies that are targets or counterparties in a confidential deal, acquisition, or dispute → [COMPANY]; unreleased financials, acquisition or deal terms → [CONFIDENTIAL]; customer or account identifiers → [CUSTOMER]; employee IDs → [EMPLOYEE ID]; trade secrets and non-public strategy → [CONFIDENTIAL].\n\n- Replace only the sensitive value. Keep the surrounding sentence grammatically intact and do not change the meaning of non-sensitive text.\n- Keep the document's structure: headings, lists, tables, and line breaks stay where they are.\n- Be consistent: once a name or value qualifies for redaction anywhere in the document, redact every mention of it — including in running prose, headings, and shortened forms (\"the Halcyon deal\" is still [COMPANY] if Halcyon is a deal target).\n- Watch for disguised formats: numbers written as words, emails using \"at\" and \"dot\", IDs without separators, values split across lines.\n- If you are unsure whether something is sensitive, redact it. Over-redacting is safer than leaking.\n\nReturn ONLY the redacted document. No introduction, no list of what you redacted, no closing summary. Your output must begin with the first line of the document.\n\n```\n\n\n\n## Save it as a preset\n\nClick \"Save Preset As…\", name it — doc cleaner works — and save. The preset bundles the system prompt with the model settings, so any future chat becomes the document cleaner in one click. This is the difference between a trick you saw once and a tool you actually use.\n\n![LM Studio preset save field named doc cleaner, with the system prompt loaded in the editor.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-save-preset.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\n## Make a synthetic test file\n\nDon't start with a real document. Have any AI generate a fake memo full of planted sensitive items plus an answer key, so you can grade the redaction instead of eyeballing it. This mirrors the briefing's rule: measure the setup on synthetic material before it sees anything real.\n\nShow the full prompt\n\n```\n\nCreate a synthetic test document I can use to evaluate a local document-redaction model. It should read like a real internal company memo.\n\n- Plant at least 20 fake sensitive items across these categories: names, emails, phone numbers, a date of birth, a street address, an SSN, a credit card number, bank account and routing numbers, salary and deal amounts, an API key, an IP address, a MAC address, a WiFi name and password, a cryptocurrency address, patient/medical details, an internal project codename, an acquisition target company, and customer account identifiers.\n- Disguise at least three items: one phone number written out in words, one email written as \"name at company dot com\", and one ID number without its usual separators.\n- Every item must be fictional. Do not use real people, companies, or working credentials.\n\n1. The memo itself, saved as a plain-text file.\n2. A separate answer key listing every planted item, so I can check the redacted output against it.\n\n```\n\n\n\n## Attach the file and send\n\nStart a new chat, confirm the doc cleaner preset is active, then attach the file with the + button (or paste the text straight in). If you want the full offline proof, switch WiFi off after the model is loaded — everything keeps working. Then send.\n\n![LM Studio chat composer with a text file attached and the gemma-4-e2b model selected.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-attach-file.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\n## Check the output like it matters\n\nThe model returns the document with every sensitive value replaced by a typed placeholder. Now grade it against the answer key: did every planted item get caught? Did it flag the disguised ones? A local model this size does the heavy lift, but the human check is part of the workflow, not an optional extra — what you produce here is a proposed redacted copy for a person to verify, never a certified-safe document.\n\n![LM Studio chat showing the redacted document output with typed placeholders replacing sensitive values.](https://unlock-ai.natebjones.com/_next/image?url=%2Fguides%2Fclean-sensitive-docs-locally%2Flm-redacted-result.png&w=3840&q=75&dpl=dpl_7hhX6aPwgdjrRoexWJS9mn62Ausb)\n\nIf the test passes, you have evidence for a better conversation with whoever owns security or privacy: an actual file, an observed run, and a specific ask — approve a local path for this job. Where the laptop stops — shared use, volume, audit trails, regulated work — is the enterprise half of the briefing."},"author":{"name":"Nate B. Jones","bio":"The hands-on companion to the executive briefing on AI for work you cannot upload: download Gemma 4 E2B in LM Studio, save a tested redaction system prompt as a preset, and turn private documents into proposed redacted copies — offline, on your own machine."},"metadata":{"timestamp":"2026-08-06T05:16:45.545Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/r9o3vtzqFl/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-1z42gy","shareId":"u4r831XhVg","platform":"web","url":"https://unlock-ai.natebjones.com/guides/build-an-email-follow-up-agent","title":"Build an Email Follow-Up Agent","shareMode":"full","createdAt":1785993406880,"postedAt":"2026-08-06T05:16:41.468Z","publishedDate":"2026-08-06T05:16:41.468Z","archivedAt":"2026-08-06T05:16:43.284Z","archivedDate":"2026-08-06T05:16:43.284Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/u4r831XhVg","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/scMcJHCEdB/media/0.png","contentType":"article","content":{"text":"This is not an email autopilot. It is a follow-up organizer: it turns a mailbox export into a commitments ledger and cited drafts that a person approves and sends.\n\n## The email problem is context disorder plus a send boundary.\n\nPeople lose high-friction paperwork fights because their information is scattered, unstructured, uncited, and incomplete. The fix is to own the context: collect the mess, normalize it, ground it in source documents, and produce the next human-reviewed action.\n\nThe email version applies that pattern to the threads you keep losing. The agent rebuilds the state of every conversation and drafts the next message. It never sends one.\n\nThis is the vertical where the boundary earns its keep. The story that motivated this guide family is an agent that drafted a reply to an insurance company, watched its human ignore the draft, and sent it anyway. It won the fight and crossed the line in the same move. Everything below is the discipline that keeps the win and removes the accident.\n\nYou do Export a mailbox, or build the fixture inbox from section 02, and drop it into the starter repo.\n\nThe AI does Reconstruct threads, build the commitments ledger, and draft cited follow-ups that stop at the send boundary.\n\nShow the full prompt\n\n```\n\nBuild a document-grounded case workflow from reusable Open Skills primitives.\n\nPeople lose because their information is scattered, unstructured, uncited, and incomplete.\nThe workflow should help the person own their context, not outsource judgment to a black box.\n\n\nIngest documents into markdown/text with raw source coordinates as anchors. Use PDF page/region, CSV line number, or form box identifiers, and embed the identical anchor scheme in the markdown that downstream citations will use. Keep one numbering scheme end to end.\nChunk and tag source evidence by structure.\nNormalize the case facts into a ledger.\nRun the coverage gate. Every ingested document must produce at least one normalized record or be explicitly marked reference-only. Print the list of unconsumed documents and stop before drafting if any document is unaccounted for.\nReconcile shared facts across sources before drafting. Compare the same fact anywhere it appears, turn every mismatch into a named review question, and record which source governs the tracked value.\nStore chunks, records, mappings, and outputs in SQLite by default.\nOptional: if you already run OB1, mirror the case store into Open Brain; otherwise skip this step entirely. SQLite is the complete beginner path.\nRetrieve relevant evidence deterministically before drafting.\nValidate citations before export. The citation guard returns pass / needs_review / fail verdicts. Any fail blocks packet export until fixed or converted to a named review question, and the guard verdict summary must appear in the packet README.\nExport an editable packet and stop at human review.\n\nThe agent organizes and drafts. It does not sign, send, file, submit, authorize, or transmit sensitive data.\n\n```\n\n\n\n## The chain does not change because the documents are emails.\n\nThe runbook calls the same Open Skills in order: ingestion, chunking/tagging, normalization, SQLite or Open Brain storage, deterministic retrieval, citation validation, packet export, and the human gate. Email changes the anchors and the ledger schema, not the architecture. The message-id becomes the citation anchor the way a PDF page or a form box did in the other two guides.\n\nSQLite stays the beginner path. Open Brain is the OB1 path, and email is where it pays off fastest: the people, preferences, and promises the ledger extracts are exactly the durable context the rest of your agents want.\n\n[Open the Context Engineering skill primitives →](https://unlock-ai.natebjones.com/open-skills/context-engineering)\n\n\n\n## Real formats, fake people.\n\nBuild the fixture inbox as real RFC 5322 messages in a real mbox file so ingestion earns its keep: true headers, In-Reply-To and References chains, multipart bodies, at least one attachment, and a real Sent folder. Make the senders, names, companies, amounts, and stakes invented.\n\nTwo fixture rules matter more than volume. Include Sent mail, because without your outbound messages the agent cannot know which loops you already closed. Include noise, a newsletter cluster and automated receipts, so triage has something to legitimately exclude as reference-only.\n\nThe fixture inbox is the permanent test bench, not a stepping stone you throw away. Every gate below gets proven here, on a corpus where runs are reproducible, before the loop touches live mail in section 05.\n\n## Seed three follow-up cases.\n\nSeed the fixture with threads for the three states the retrieval map handles: a dropped commitment, where you promised a deliverable by a named date and the thread went quiet; a waiting-on-them thread, where you asked for something twelve days ago and nothing came back; and a dispute, a vendor or insurer decline where the draft must answer point-by-point from quoted evidence and then stop, unsent.\n\nThe retrieval map also covers scheduling threads and intro requests. Prove the three states above on the fixture first; the other branches get their first real exercise when the loop reaches your actual mailbox in section 05.\n\n## Chunk messages, not threads.\n\nA twelve-reply thread repeats the same paragraph twelve times as quoted history. Anchor every evidence chunk to the message-id where a sentence first appears, and strip quoted history, signatures, and legal disclaimers out of evidence chunks entirely. Keep a quote map alongside, so retrieval can still reconstruct what each participant had seen by any point in the thread.\n\nSmoke-test this by printing the text of every cited chunk and reading it. A citation that lands on a signature block, a disclaimer footer, or reply nine's quote of the original is a broken chunker, not evidence.\n\n## Normalize threads into a commitments ledger.\n\nReconstruct threads from In-Reply-To and References headers, never from subject lines. Subject matching breaks on Re: Re: Fwd: prefixes, on edited subjects, and on the two different vendors who both titled their thread Invoice.\n\nNormalize identity before extracting anything. \"Amazon.com \" is a sender, not a person; the display-name plus address pair is the identity key. Then extract the ledger: commitments with owner, description, due date, status, and source anchor; waiting-on rows with what was asked, who owes it, and when it was asked; and decisions worth keeping. A due date of \"Friday\" stored as the string Friday is a failed parse, resolve it against the message date or mark it needs_review.\n\n## Reconcile against Sent mail before drafting anything.\n\nOne promise equals one ledger row, even when it is restated in three threads and quoted in six replies. A weak agent sees \"I'll send the deck by Friday\" quoted through a thread, creates six commitments, and drafts three duplicate follow-ups to the same person.\n\nCross-check every waiting-on row against the Sent export before drafting a nudge. The most embarrassing failure this workflow can produce is chasing a question the person answered two weeks ago, and it happens whenever Sent mail is not ingested as first-class evidence. The coverage gate counts Sent messages like any other source document.\n\nWhen an email body and its attachment disagree on an amount or a date, the mismatch becomes a named review question in the packet, never a silent correction. Name which source governs the tracked value so the draft still has one number to cite.\n\n## The output is an outbox packet, not sent mail.\n\nThe packet is a folder a person can review in ten minutes. The README opens with actions ordered by urgency: overdue commitments first, then aging waiting-ons, each with days elapsed computed from the run date.\n\n**Packet folder.** One packet/ directory per mailbox: README.md, commitments-ledger.csv, waiting-on.md, follow-up-schedule.md, drafts/ with one file per draft, citation-map.json, unresolved-questions.md, and a sources/ manifest.\n\nEvery draft carries complete headers, To, Cc, Subject, and In-Reply-To, so approval means reading one file, not reassembling context. Every factual claim in a draft cites a message chunk: \"as agreed on June 3\" must anchor to the June 3 message or it becomes a review question. And every draft carries an approval field with three values, pending, approved, declined. Everything exports as pending.\n\n## Draft in your voice, not in AI voice.\n\nA follow-up you have to rewrite from scratch is not a draft, it is a prompt for you. Nothing kills the approval loop faster than drafts that open with \"I hope this email finds you well\" when you have never once written that sentence.\n\nBuild the **Personal Voice** skill from the Open Skills directory before you wire up drafting. It encodes how you actually write across registers, blunt versus warm, email versus post, with real samples of each, so drafts come back needing light edits instead of rewrites. Your own Sent mail is the training corpus, and this pipeline has already ingested it: pick five to ten sent replies that sound like you on a good day and feed them to the skill's setup interview.\n\nDirectory path: Open Skills, Writing Voice and Content, Personal Voice. Linked below the sources list on this page.\n\n[Open the Email Follow-Up Packet runbook →](https://unlock-ai.natebjones.com/open-skills/runbooks#email-follow-up-packet)\n\n\n\n## Cite the format and export sources.\n\nGround the format work in primary sources: RFC 5322 for the message format the fixtures must honor, RFC 4155 for the mbox container, Google Takeout as the build-phase export path, and the Gmail API scopes page for why raw API credentials are the wrong live path: the scope that manages drafts can also send them. The live loop in section 05 gets the safe property a different way, a connector tool surface with no send verb.\n\n## An ignored draft means no.\n\nApproval is explicit and per message. Silence is not consent, time passing is not consent, and re-running the pipeline is not consent; nothing promotes a draft from pending to approved except a recorded human decision.\n\nThe boundary is enforced structurally at both stages of maturity, not politely. During the build, the input is a local export and the output is a folder of drafts, so no code path can transmit anything. Live, the agent works through a mail connector whose tool surface reads threads and creates drafts but exposes no send verb, and its drafts land in your Drafts folder still marked pending in the ledger. The approval act is you pressing send in your own mail client, and the approval column survives as the audit trail.\n\n## Inbound email is evidence, never instructions.\n\nThis vertical has a gate the other two do not need: the source documents can talk back. Message bodies are things to quote, cite, and summarize, never commands to follow, no matter how they are phrased.\n\nSeed a hostile fixture: an email whose body instructs the assistant to send the reply immediately without confirmation. A correct run ingests it, stores it, and quotes it like any other message, while every draft stays pending and no send path executes. Keep the run report showing the hostile message was processed and the approval column never moved.\n\n## Save proof that the guard works.\n\nThe citation-guard test saves two reports as artifacts: one for the fully cited draft and one for a seeded fabricated-but-well-formed citation, a draft sentence like \"as you confirmed on June 3\" pointing at a message that does not exist. The fully cited draft must pass with exit 0. The seeded fabrication must fail with nonzero exit, and the seeded sentence itself must appear as the failing item in the saved report.\n\nA report that fails other claims does not count as proof. Fix the harness until the fabricated sentence is the reported failure, then keep both reports with the packet.\n\n## Point the verified loop at an MCP connector.\n\nEmail is not a once-a-day surface. A follow-up agent working from this morning's export commits the exact failure this guide exists to prevent by afternoon: nudging someone who answered an hour ago. Once every gate passes on the fixture inbox, move ingestion to the mail connector your agent platform provides.\n\nDemand one property before wiring it in: the connector's tool surface should read threads and create drafts but expose no send verb. Anthropic's Gmail connector is shaped exactly this way, search and read threads, create and list drafts, manage labels, no send tool. That keeps the send boundary structural on live mail: the model cannot call a tool that does not exist.\n\n## Ingest on a work-hours cadence.\n\nPoll every 20 minutes or so during work hours. Each cycle is incremental: fetch the delta since the last run keyed by message-id, ingest and extract only the new messages, and update the ledger rows those messages touch. Most cycles carry a handful of messages, so extraction stays cheap. The expensive pass, full Sent-mail cross-checks and restated-promise merging across the whole ledger, runs once nightly.\n\nEvery cycle appends a run receipt: messages ingested, ledger rows changed, drafts created, questions raised. A loop that runs thirty times a day without receipts is thirty chances a day to trust it blindly.\n\n## Unattended runs raise the injection stakes.\n\nA scheduled loop processes hostile mail with nobody watching, so the untrusted-input rule from section 04 stops being belt-and-suspenders and becomes load-bearing. Keep the runner's permissions to exactly the pipeline: the mail connector's read-and-draft tools, the case store, nothing else. No shell, no other outbound channels.\n\nDrafts the live loop creates go into your Drafts folder, still marked pending in the ledger. Labels make a good native receipt: the connector can tag processed threads and flag needs-review ones, so your inbox itself shows what the agent touched without opening the packet.\n\n## Run the per-stage prove-it checklist.\n\n**Ingestion.** Does every message in the export appear in source_documents with its message-id anchor? Count the messages in the mbox and compare against the row count; multipart messages that parse to empty text get named in a warning list, not silently skipped. A broken run has 300 messages on disk and 240 rows, and the missing 60 are the entire Sent folder.\n\n**Chunking.** Does a cited chunk contain the original sentence at its first occurrence? A broken run cites a signature block, a disclaimer footer, or a later reply's quote of the original. Fix by stripping quoted history and boilerplate from evidence chunks and re-anchoring to the originating message-id.\n\n**Normalization.** Do senders resolve to identity keys; do dates parse with timezones; does every commitment row carry an owner, a due date, and a source anchor? A broken run stores five rows for one promise or a due date of \"Friday\" as text.\n\n**Reconciliation.** Does a sent reply close the waiting-on item it answers; do restated promises merge into one row; do body-versus-attachment conflicts become review questions? A broken run drafts a nudge for a question that was answered in Sent mail two weeks ago.\n\n**Drafting plus guard.** Does the clean draft pass and the seeded fabricated citation fail as the named failing item? A broken run accepts \"as you confirmed\" claims with no anchoring message.\n\n**Export.** Open the packet: does every draft have complete headers; does the README ordering match the ledger; is every approval field pending? A broken run ships a draft with an empty To line or an approved status no human set.\n\n**Human gate.** Is the run's tool surface free of a send verb; did the hostile fixture stay quoted instead of obeyed? A broken run has any route from the packet or the Drafts folder to transmission that does not pass through a recorded human decision.\n\n## Refuse to ship unapproved certainty.\n\nThe guard's verdict is the gate. Export refuses, or stamps DRAFT-INVALID on the README, while any citation fails or any draft claims an approval the ledger cannot show a human decision for. The README reproduces the guard's actual pass, needs_review, and fail counts verbatim, so the reviewer sees the same verdict the pipeline saw.\n\nPut the boundary check on the human-gate checklist itself: confirm the run's tool surface cannot send, confirm every draft exported as pending, confirm the hostile fixture appears in the ledger as content, and confirm the follow-up schedule proposes dates instead of scheduling sends.\n\nThis lane replaces per-provider connectors for ingestion only. Drafting and the send boundary still work exactly as in sections 03 through 05.\n\n## Pick the open door: Thunderbird.\n\nAny mail client that downloads full copies of every account turns your computer into the aggregator: iCloud, Gmail, Microsoft, Yahoo, and plain IMAP all land as local files your pipeline can read. Thunderbird is the open door on every OS: free, open source, runs on Windows, macOS, and Linux, and stores every folder as a plain mbox file in a profile directory the operating system does not gate. The built-in alternatives are closed doors by comparison: Apple Mail's store sits behind Full Disk Access and a proprietary per-message format, and Outlook's local OST store is a database you were never meant to read.\n\nThis lane is validated end to end: pointing a small Python stdlib locator at a real two-account Thunderbird store walked 30 folder files and parsed 169,084 messages, with no permissions dialog and no proprietary framing in the way. The same mbox files feed every stage this guide already built.\n\n## Add every account, then read plain mbox.\n\nInstall Thunderbird and add each account through its wizard. This step is also the auth solution: for Gmail and Microsoft the wizard opens the provider's own browser sign-in and handles OAuth for you. Then, per folder you care about, enable offline synchronization so full message copies land on disk, not just headers.\n\nThe store layout is stable, documented, and identical on every OS; only the root moves. macOS: ~/Library/Thunderbird/Profiles/. Windows: %APPDATA%\\Thunderbird\\Profiles\\. Linux: ~/.thunderbird/. In each case profiles.ini names the profile directories. Inside a profile, ImapMail// holds each IMAP account and Mail/ holds POP and Local Folders. Every folder is an EXTENSIONLESS mbox file: Inbox is the mail, Inbox.msf is an index sidecar to ignore, and Inbox.sbd/ is a directory holding child folders. Python's mailbox module reads these directly.\n\nTwo disciplines for the live loop. Copy or snapshot before parsing while Thunderbird is running, and treat folder files as rewritable, not append-only: Thunderbird compacts folders, which rewrites the file in place. So key incremental sync on file mtime plus a Message-ID high-water mark in your case store, and re-scan any folder whose file changed.\n\n## Let the client do the auth dance.\n\nHand-rolled IMAP credentials are where inbox projects die, because the providers have spent years killing passwords. As of mid-2026: Microsoft retired password sign-in for Outlook.com IMAP on September 16, 2024, OAuth2 is mandatory. Personal Gmail no longer supports apps that ask for your Google password; app passwords still exist as a fallback but require 2-Step Verification and Google recommends against them. iCloud Mail requires an app-specific password for third-party IMAP. Yahoo requires an app password.\n\nThe practical read: do not paste provider passwords into scripts. Let a client that speaks OAuth do the dance in its GUI, Thunderbird for this lane, and read its local store. If you are building a service rather than a personal loop, a self-hosted gateway like EmailEngine manages OAuth tokens and exposes every account as a REST API, that is the heavier, builder-grade version of the same move.\n\n## On a Mac, Apple Mail still works, behind two gates.\n\nIf you are on a Mac and would rather not run a second client, Apple Mail's local store is real: everything syncs under ~/Library/Mail/V, one UUID directory per account, each message an individual.emlx file. But the lane sits behind two gates. Gate one is permission: reading it requires Full Disk Access (System Settings, Privacy and Security, Full Disk Access, enable your terminal or agent host app, then restart it), and until granted, even a directory listing fails with \"Operation not permitted\", that error is the gate working, not a bug. Gate two is format:.emlx is a byte-count line, then the raw RFC 5322 message, then an Apple plist, split it by the byte count, never by scanning for the XML, and treat partial.emlx files as incomplete evidence.\n\nThe no-permission fallback is manual: select mailboxes, then Mailbox, Export Mailbox. The catch is that the export is a .mbox FOLDER whose real mbox stream is a file literally named mbox inside it. And for incremental work, V/MailData holds the Envelope Index SQLite catalog, copy it with its -wal and -shm sidecars before querying.\n\nHonest status: the parsers for this lane pass on fixtures, but in-place reading is unproven until you grant Full Disk Access, which is exactly why Thunderbird is the recommended door."},"author":{"name":"Nate B. Jones","bio":"Build an agent-operated loop that ingests your inbox on a work-hours cadence, keeps a commitments ledger, and drafts cited follow-ups that stop at the send boundary."},"metadata":{"timestamp":"2026-08-06T05:16:41.468Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/scMcJHCEdB/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-2zv1px","shareId":"p7sAmXrcsa","platform":"web","url":"https://unlock-ai.natebjones.com/guides/build-a-tax-prep-organizer-agent","title":"Build a Tax Prep Organizer Agent","shareMode":"full","createdAt":1785993404105,"postedAt":"2026-08-06T05:16:38.266Z","publishedDate":"2026-08-06T05:16:38.266Z","archivedAt":"2026-08-06T05:16:40.083Z","archivedDate":"2026-08-06T05:16:40.083Z","viewCount":2,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/p7sAmXrcsa","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/lJtDrJCcKe/media/0.png","contentType":"article","content":{"text":"This is not AI filing your taxes. It is a tax prep organizer: it turns scattered documents into a reviewable packet for the taxpayer or CPA.\n\n### The tax problem is context disorder.\n\nPeople lose high-friction paperwork fights because their information is scattered, unstructured, uncited, and incomplete. The fix is to own the context: collect the mess, normalize it, ground it in source documents, and produce the next human-reviewed action.\n\nThe tax version applies that pattern to W-2s, 1099s, receipts, CSVs, prior-year summaries, and IRS rules. The agent does not file the return. It builds the file so the human or CPA can review from clean evidence.\n\nYou do Drop your W-2s, 1099s, receipts, bank CSVs, and prior-year summary into the starter repo.\n\nThe AI does Normalize the tax-year ledger, flag gaps, draft CPA questions, and export the prep packet.\n\nShow the full prompt\n\n```\n\nBuild a document-grounded case workflow from reusable Open Skills primitives.\n\nPeople lose because their information is scattered, unstructured, uncited, and incomplete.\nThe workflow should help the person own their context, not outsource judgment to a black box.\n\n\nIngest documents into markdown/text with raw source coordinates as anchors. Use PDF page/region, CSV line number, or form box identifiers, and embed the identical anchor scheme in the markdown that downstream citations will use. Keep one numbering scheme end to end.\nChunk and tag source evidence by structure.\nNormalize the case facts into a ledger.\nRun the coverage gate. Every ingested document must produce at least one normalized record or be explicitly marked reference-only. Print the list of unconsumed documents and stop before drafting if any document is unaccounted for.\nReconcile shared facts across sources before drafting. Compare the same fact anywhere it appears, turn every mismatch into a named review question, and record which source governs the tracked value.\nStore chunks, records, mappings, and outputs in SQLite by default.\nOptional: if you already run OB1, mirror the case store into Open Brain; otherwise skip this step entirely. SQLite is the complete beginner path.\nRetrieve relevant evidence deterministically before drafting.\nValidate citations before export. The citation guard returns pass / needs_review / fail verdicts. Any fail blocks packet export until fixed or converted to a named review question, and the guard verdict summary must appear in the packet README.\nExport an editable packet and stop at human review.\n\nThe agent organizes and drafts. It does not sign, send, file, submit, authorize, or transmit sensitive data.\n\n```\n\n\n\n### The shared primitive chain is the point.\n\nThis runbook calls the same Open Skills as the healthcare guide: the same ingestion, normalization, retrieval, citation guard, and export primitives, pointed at a different bureaucracy. Improve a primitive here and every workflow built on it gets better.\n\nSQLite stays the beginner path. Open Brain becomes the OB1 path when the person wants the ledger and evidence to become durable personal context.\n\n[Open the Context Engineering skill primitives →](https://unlock-ai.natebjones.com/open-skills/context-engineering)\n\n\n\n### Rules from the IRS, facts from your files.\n\nUse IRS publications, instructions, recordkeeping pages, and form references for rule context. The records are your own: W-2s, 1099-NEC, 1099-MISC, interest and dividend statements, receipts, mileage logs, business bank CSVs, and prior-year summaries.\n\nEverything runs locally. The packet that comes out is for you or your CPA; nothing in this workflow files, transmits, or uploads your financial records anywhere.\n\n### Start with a normal-person scope.\n\nThe first scope should be one tax year, federal-only, single taxpayer or sole proprietor, common forms, and Schedule C-style expense organization. The artifact is a prep packet, not a filed return.\n\n### Scope the tax year before you trust the ledger.\n\nAsk the agent to infer the tax year from evidence density across the inbox: form years, statement ranges, receipt dates, CSV periods, and prior-year summaries. Documents from other years, such as a prior-year return dropped in the same folder, become reference-only records and stay out of the ledger.\n\nDefine expected-but-missing forms from gaps and contradictions in this case's own documents, not from a generic fixture list. Coverage windows, unmatched deposits, payer names, and prior-year carryover notes create the missing-document checklist.\n\nCoverage checkpoint: the inbox document count must equal the source_documents row count, and every ingested document must produce at least one normalized record or be explicitly marked reference-only. Hard stop with a named-file list on mismatch. Without this check, a weak agent can silently drop 5 of 15 documents including the W-2, then report the W-2 as missing while it sits in its own ingested folder.\n\n### Reconcile across sources before categorizing.\n\nOne real-world transaction equals one ledger row. Deduplicate by date, amount, and vendor: a receipt and its matching bank CSV line merge into a single row that cites both pieces of evidence. If the receipt and bank line disagree on amount, create a review question instead of two expense entries.\n\nSeparate corroboration from primary evidence. Bank deposits that match a 1099 or invoice total become corroboration records that cross-reference that form. Unmatched deposits stay primary income and generate a missing-1099 checklist item.\n\nCross-check every W-2 and 1099 against deposits, and every business deposit against a 1099 or invoice by payer. Unmatched payers become named missing-document checklist items with dates and amounts, for example: \"Apex Consulting, $6,200 across 2 deposits, no 1099 on file.\"\n\n\n\n### Retrieval starts from tax structure.\n\nMap from evidence to Schedule C-style categories only after reconciliation. Use a starter category set with explicit matching rules:\n\n**Advertising.** vendor identity or receipt text shows paid promotion, sponsorship, listing fees, or ad platform spend.\n\n**Car and truck.** mileage logs and vehicle expenses tied to business use; a mileage log maps to one aggregate car-and-truck evidence record using total business miles times the IRS standard mileage rate, never to per-trip dollar lines. The IRS publishes the rate, and the source list links the rate page.\n\n**Office expense.** workspace supplies, postage, printing, and small office items with a business receipt.\n\n**Supplies.** materials consumed in the work product or client service delivery, supported by itemized receipts.\n\n**Software and subscriptions.** business tools, hosting, domains, AI tools, editing apps, and SaaS accounts tied to the work.\n\n**Utilities.** business phone, internet, or workspace utility charges when the evidence shows business use.\n\n**Meals at 50%.** business meals with receipt, date, amount, participants or purpose, and 50% treatment flagged in the ledger.\n\nMatch on vendor identity and supporting evidence, never on substrings. Without this rule, a weak keyword map can file \"PACIFIC GAS & ELECTRIC\" under Car and truck because it matched \"gas.\" Recurring personal-pattern charges such as rent, groceries, streaming, and restaurants default to excluded-pending-review, never to a business category.\n\nSet needs_review as the default status for any transaction that lacks a matched receipt or documented business purpose. Only receipt-corroborated, single-category items enter the ledger as ok. Meals and mixed receipts always start as review questions: meals need the 50% rule and a business purpose, and mixed receipts need a split between business and personal items before they can affect totals.\n\n### The output is a prep packet.\n\nExport a folder, not one mystery file. The packet contains editable drafts plus a combined PDF handoff.\n\n**Packet folder.** One packet/ directory per tax year: income-summary.md, expense-ledger.csv, deduction-evidence-map.json, missing-documents.md, cpa-questions.md, schedule-c-summary.md, packet.pdf, and a sources/ manifest.json.\n\nThe PDF is one combined, rendered document for the taxpayer or CPA, with the individual drafts kept editable in the folder. For one taxpayer-year it should be a sane low double digit page count, tables should render as tables, and raw markdown artifacts should not appear in the PDF.\n\nThe packet reproduces the citation guard's actual pass, needs_review, and fail counts verbatim. Export refuses while any claim fails, or stamps DRAFT-INVALID on the cover if the workflow allows a draft handoff for review.\n\n[Open the Tax Prep Packet runbook →](https://unlock-ai.natebjones.com/open-skills/runbooks#tax-prep-packet)\n\n\n\n### Cite the rule sources.\n\nGround every rule the packet cites in primary sources: IRS Publication 17, the Schedule C instructions, IRS recordkeeping guidance, and the IRS pages for common information returns such as W-2s and 1099s. All of them are linked at the bottom of this page.\n\n### The human or CPA files.\n\nThe workflow stops at review/edit/export. Filing taxes is a legal and financial act. The agent prepares the evidence packet and questions; the taxpayer or professional decides what goes on the return.\n\n### Add a prove-it checklist to every stage.\n\n**Ingestion.** Does every inbox file appear in the index with an anchor? A broken run has files on disk that never appear in source_documents. Fix by stopping the run and listing the missing filenames before normalization starts.\n\n**Chunking.** Does a cited chunk contain the actual form box or CSV row, not front matter? A broken run cites a document title, cover page, or section intro while the claimed value lives elsewhere. Fix by re-chunking around form boxes, table rows, and receipt line items.\n\n**Normalization.** Do names look like names; do dates parse; does the ledger total reconcile against source line-item sums? A broken run has payer fields that look like categories, invalid dates, or totals that do not foot to the source rows. Fix by repairing parsers and adding a ledger-total reconciliation step.\n\n**Reconciliation.** Is every receipt merged with its bank line; is every 1099 and W-2 matched against deposits? A broken run double-counts receipts and bank rows or leaves named payers unmatched. Fix by merging duplicate evidence into one row and producing missing-document questions for unmatched payers.\n\n**Drafting plus guard.** Does a clean draft pass and a seeded fabricated citation fail? A broken run accepts invented citations or vague source references. Fix by requiring chunk-level anchors and a negative test before export.\n\n**Export.** Open the PDF: is the page count sane and are the tables rendered? A broken run produces a huge export, blank tables, or raw markdown. Fix by rendering the packet, opening it, and blocking export when the PDF does not match the folder drafts.\n\n**Human gate.** Does the packet stop at review with CPA questions present? A broken run presents the packet as ready to file or hides unresolved questions. Fix by requiring review status, visible CPA questions, and a person-owned filing decision.\n\n### Ship only when the guard says the packet is reviewable.\n\nThe guard's verdict is the gate. Passing claims can ship into the review packet, needs_review flags stay honest review questions, and failed claims block export until repaired or removed.\n\nPut the export acceptance check on the human-gate checklist itself: open packet.pdf, confirm the page count, confirm the expected sections are present, confirm tables render, and confirm the packet still stops at CPA or taxpayer review.\n\n## Primary sources for the starter.\n\nUse IRS pages as the authority layer for every rule the packet cites. The output is a review packet, not a filed return.\n\n## [IRS Publication 17](https://www.irs.gov/publications/p17)\n\nGeneral federal income tax rules for individuals.\n\nOpen source\n\n## [Schedule C instructions](https://www.irs.gov/instructions/i1040sc)\n\nSole proprietor business income and expense instructions.\n\nOpen source\n\n## [IRS recordkeeping guidance](https://www.irs.gov/businesses/small-businesses-self-employed/recordkeeping)\n\nWhat records to keep and why supporting documents matter.\n\nOpen source\n\n## [IRS Form 1099-NEC](https://www.irs.gov/forms-pubs/about-form-1099-nec)\n\nOfficial reference for nonemployee compensation reporting.\n\nOpen source\n\n## [IRS standard mileage rates](https://www.irs.gov/tax-professionals/standard-mileage-rates)\n\nPublished per-mile rate used to value business mileage from a log.\n\nOpen source"},"author":{"name":"Nate B. Jones","bio":"Build an agent that turns your tax documents and real IRS rule sources into a structured, CPA-ready tax-prep packet."},"metadata":{"timestamp":"2026-08-06T05:16:38.266Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/lJtDrJCcKe/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-8sq23t","shareId":"kdXPZoWU1j","platform":"web","url":"https://unlock-ai.natebjones.com/guides/build-a-healthcare-claim-appeals-agent","title":"Build a Healthcare Claim Appeals Agent","shareMode":"full","createdAt":1785993399119,"postedAt":"2026-08-06T05:16:34.096Z","publishedDate":"2026-08-06T05:16:34.096Z","archivedAt":"2026-08-06T05:16:35.861Z","archivedDate":"2026-08-06T05:16:35.861Z","viewCount":0,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/kdXPZoWU1j","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/SbTHqb5o3m/media/0.png","contentType":"article","content":{"text":"Start here before touching denial types. The tax prep and email follow-up guides run the same primitive chain, so anything you build here carries over.\n\n### Name the enemy: fragmented context.\n\nPeople lose high-friction paperwork fights because their information is scattered, unstructured, uncited, and incomplete. The fix is to own the context: collect the mess, normalize it, ground it in source documents, and produce the next human-reviewed action.\n\nThe healthcare version applies that pattern to a denied claim. The person does not need the agent to become an insurer, a lawyer, or a doctor. They need a clean case file that forces the appeal to respond to plan language and evidence.\n\nYou do Bring the denial letter, plan docs, and supporting documents into the starter repo.\n\nThe AI does Assemble a cited appeal packet from the reusable Open Skills chain.\n\nShow the full prompt\n\n```\n\nBuild a document-grounded case workflow from reusable Open Skills primitives.\n\nPeople lose because their information is scattered, unstructured, uncited, and incomplete.\nThe workflow should help the person own their context, not outsource judgment to a black box.\n\n\nIngest documents into markdown/text with raw source coordinates as anchors. Use PDF page/region, CSV line number, or form box identifiers, and embed the identical anchor scheme in the markdown that downstream citations will use. Keep one numbering scheme end to end.\nChunk and tag source evidence by structure.\nNormalize the case facts into a ledger.\nRun the coverage gate. Every ingested document must produce at least one normalized record or be explicitly marked reference-only. Print the list of unconsumed documents and stop before drafting if any document is unaccounted for.\nReconcile shared facts across sources before drafting. Compare the same fact anywhere it appears, turn every mismatch into a named review question, and record which source governs the tracked value.\nStore chunks, records, mappings, and outputs in SQLite by default.\nOptional: if you already run OB1, mirror the case store into Open Brain; otherwise skip this step entirely. SQLite is the complete beginner path.\nRetrieve relevant evidence deterministically before drafting.\nValidate citations before export. The citation guard returns pass / needs_review / fail verdicts. Any fail blocks packet export until fixed or converted to a named review question, and the guard verdict summary must appear in the packet README.\nExport an editable packet and stop at human review.\n\nThe agent organizes and drafts. It does not sign, send, file, submit, authorize, or transmit sensitive data.\n\n```\n\n\n\n### Use the same primitive chain every time.\n\nThe runbook calls Open Skills in order: ingestion, chunking/tagging, normalization, SQLite or Open Brain storage, deterministic retrieval, citation validation, packet export, and the human gate. Healthcare changes the labels and mappings, not the architecture.\n\nStarter path: local files plus SQLite. Open Brain path: write the same chunks and normalized records into OB1 for people already running durable context.\n\n[Open the Context Engineering skill primitives →](https://unlock-ai.natebjones.com/open-skills/context-engineering)\n\n\n\n### Collect the plan documents and the claim records.\n\nBring the denial letter, the EOB for the denied claim, and your plan's SBC and EOC. The coverage language, exclusions, prior authorization rules, network rules, emergency services language, and appeal instructions all live in those plan documents. Your insurer's member portal has them, and most insurers also publish plan documents on the open web.\n\nEverything runs locally: the agent reads your documents on your machine and writes a packet folder. Nothing in this workflow transmits your health information anywhere.\n\n### Start from your denial type.\n\nThe denial letter names a reason, and the workflow branches on it: administrative or coding error, prior authorization missing, service not covered, out of network, or medical necessity. The first four argue directly from plan language and claim facts.\n\nMedical necessity is the boundary case. The agent assembles the packet and includes a letter-of-medical-necessity template for your doctor to complete; it never invents clinical reasoning on your behalf. Whatever the denial type, the draft only argues what your documents support.\n\n### Chunk long plan documents in two tiers.\n\nFor long plan documents, keep page-level chunks so the whole document can be cited and audited, then add clause-level chunks for the sections named by the retrieval map. Store a granularity column with values such as page and clause so retrieval can choose the right surface and the citation guard can explain what it checked.\n\nExclude the table of contents, cover pages, and front matter from evidence chunks. A cited chunk must contain operative plan language, not headings or dotted page listings. Smoke-test this by printing the text of every cited chunk and reading it before you trust the draft.\n\n### Retrieval starts from structure.\n\nAdministrative or coding error retrieves the appeals process and claim/EOB line items. Service-not-covered retrieves covered benefits and excluded services. Out-of-network retrieves network and emergency services sections. Prior authorization retrieves prior authorization sections. Medical necessity retrieves covered benefits and triggers the doctor-template path.\n\n### Reconcile the denial letter against the EOB and plan.\n\nFor each claim number, compare denial-letter fields against EOB rows field by field: CPT code, service dates, amounts, and provider. Then compare the plan-section citations named in the denial letter against the real EOC table of contents. Any mismatch becomes a needs_review unresolved question carried into the packet, never a silent correction.\n\nName which source governs the tracked value so downstream stages still have one value to work with. For example, use the letter the patient received as the governed value for the appeal narrative, while keeping the EOB disagreement visible as a review question. Without this step, a weak agent will see a denial letter with CPT 99214, miss that the EOB row says 99213, and argue the wrong code.\n\n### The output is an appeal packet, not a sent appeal.\n\nThe packet contains an editable appeal letter, the rendered appeal-letter PDF, citation map, deadline summary, supporting document checklist, unresolved questions, source manifest, and medical-necessity doctor template when needed. The deadline summary normalizes every appeal window to an absolute date by adding the stated days to the notice date, computes days remaining from the run date, flags anything under 14 days as URGENT at the top of the packet README, and orders cases by deadline.\n\nThe exported PDF is the rendered appeal letter itself: letter-formatted, a sane page count, no browser artifacts, and identity fields from the normalized record rather than string slicing. A weak agent without this check will turn a street address into the patient name or preserve a corrupted deadline like \"y 27, 2026\" because it copied text instead of validating fields.\n\n**Packet folder.** One packet/ directory per case: README.md, appeal-letter.md, appeal-letter.pdf, citation-map.json, deadline-summary.md, checklist.md, unresolved-questions.md, and a sources/ manifest.\n\nFor medical-necessity cases, include a blank doctor letter-of-medical-necessity template as a required packet file. The appeal letter may quote the insurer's criteria and point to the template, but it never asserts that clinical criteria are met. Every coverage claim must cite a chunk. Anything ungrounded becomes a review question. The guard's verdict is a shipping gate: export refuses while any claim fails validation.\n\n[Open the Claim Appeal Packet runbook →](https://unlock-ai.natebjones.com/open-skills/runbooks#claim-appeal-packet)\n\n\n\n### Cite the process sources.\n\nGround the process facts in primary sources: HealthCare.gov for internal appeals and external review, 45 CFR 147.136 for claim and appeal process requirements, and KFF for how often appeals actually overturn denials. All of them are linked at the bottom of this page. The coverage argument itself cites only your plan documents.\n\n### The human gate is a feature.\n\nThe workflow stops at review/edit/export. Sending an appeal means a person signs and transmits health data to an insurer. That is a product boundary, not a missing automation feature.\n\n### Save proof that the guard works.\n\nThe citation-guard test saves two reports as artifacts: one for the fully cited draft and one for the seeded fabricated-but-well-formed citation. The fully cited draft must pass with exit 0. The seeded fabricated citation must fail with nonzero exit, and the seeded sentence itself must appear as the failing item in the saved report.\n\nA report that fails other claims does not count as proof. Fix the harness until the fabricated sentence is the reported failure, then keep both reports with the packet or test artifact bundle.\n\n### Run the per-stage prove-it checklist.\n\n**Ingestion.** Does every source document appear in the index with an anchor? A missing document means the draft can ignore evidence without warning. Fix the ingestion manifest and rerun the coverage gate before normalization.\n\n**Chunking.** Does a cited chunk contain operative plan language? A chunk that only shows headings, front matter, or dotted page listings cannot support a claim. Exclude that chunk from evidence and create a clause-level chunk from the real plan text.\n\n**Normalization.** Does the patient name look like a name; do dates parse as real dates with days remaining computed; do amounts reconcile against line-item sums? A street address stored as the patient name, a corrupted date string, or mismatched totals means the record is not usable. Mark the field needs_review, keep the source anchor, and write the unresolved question.\n\n**Retrieval.** Did every expected section arrive; are missing sections flagged? A branch that returns only generic appeal instructions cannot support a coverage argument. Add the missing retrieval-map target and rerun before trusting the draft.\n\n**Drafting plus guard.** Does a clean draft pass and a seeded fabricated citation fail? A guard that passes both drafts or fails for unrelated claims is not proving the boundary. Save the two reports and fix the guard until the seeded sentence is the failing item.\n\n**Export.** Does the PDF open as the rendered letter with a sane page count? A browser print artifact, blank file, or unexpected page count means the packet is not ready. Regenerate from the letter renderer and recheck identity fields from the normalized record.\n\n**Human gate.** Does the packet stop at review with the checklist present? A packet that sends, files, or hides open questions crosses the product boundary. Stop at export, surface the checklist, and require a person to sign and transmit.\n\n### Refuse to ship invalid packets.\n\nNormalization QA is not optional. Every extracted field carries a source anchor and confidence. Records with failed sanity checks become needs_review with a concrete unresolved question instead of keeping a default pending status.\n\nPacket export refuses to ship, or stamps DRAFT-INVALID on the cover, while the guard reports any FAIL. The packet README reproduces the guard's actual pass / needs_review / fail counts verbatim so the reviewer sees the same verdict the pipeline saw.\n\n## Primary sources for the starter.\n\nUse these as the authority layer for process facts and deadlines. Your plan documents carry the coverage argument, and a person reviews and sends the appeal.\n\n## [HealthCare.gov internal appeals](https://www.healthcare.gov/appeal-insurance-company-decision/internal-appeals/)\n\nInternal appeal process and timing for denied claims.\n\nOpen source\n\n## [45 CFR 147.136](https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-B/part-147/section-147.136)\n\nFederal claims and appeals process requirements.\n\nOpen source\n\n## [KFF ACA denials and appeals](https://www.kff.org/private-insurance/issue-brief/claims-denials-and-appeals-in-aca-marketplace-plans/)\n\nDenial, appeal, and overturn-rate framing for marketplace plans.\n\nOpen source\n\n## [CareSource public plan documents](https://www.caresource.com/in/plans/marketplace/plan-documents/)\n\nExample public plan-document source for real SBC/EOC language.\n\nOpen source"},"author":{"name":"Nate B. Jones","bio":"Build an agent that turns your denial letter, EOB, and plan documents into a cited, human-reviewed appeal packet."},"metadata":{"timestamp":"2026-08-06T05:16:34.096Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/SbTHqb5o3m/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]},{"id":"web-c3tjg5","shareId":"2tGyc89Vte","platform":"web","url":"https://unlock-ai.natebjones.com/guides/build-your-own-token-burn-dashboard","title":"Build your own token-burn dashboard","shareMode":"full","createdAt":1785993395633,"postedAt":"2026-08-06T05:16:30.313Z","publishedDate":"2026-08-06T05:16:30.313Z","archivedAt":"2026-08-06T05:16:32.276Z","archivedDate":"2026-08-06T05:16:32.276Z","viewCount":1,"ownerUsername":"arc","canonicalUrl":"https://social-archive.org/arc/2tGyc89Vte","thumbnail":"https://social-archiver-api.social-archive.org/media/archives/arc/ekczP1bxr1/media/0.png","contentType":"article","content":{"text":"What you build\n\n\n\n## One tidy table, five honest reads.\n\nThe dashboard is not complicated. It is five different views over normalized daily totals. The discipline is in the data shape and the labels.\n\n01\n\n### Daily burn heatmap\n\nEvery day colored by tokens spent, on a log scale.\n\n02\n\n### Weekly trend line\n\nA cleaner read on whether your usage is climbing or getting leaner.\n\n03\n\n### Burn drivers\n\nThe work families that eat the budget: shipping, research, review, video, admin.\n\n04\n\n### Scale equivalents\n\nA huge token number translated into something a human can feel.\n\n05\n\n### Moving-average table\n\nThe receipts: per tool, per day, exact and estimated side by side.\n\nStarter kits\n\n\n\n## Start from the stack you actually use.\n\nEach download is an agent skill with the dashboard starter files baked in. Your AI reads the skill, copies the starter app, interviews you for missing data, and builds from there.\n\nCodex\n\n### Exact local usage first.\n\nUse this when Codex is where your tokens are already visible. The kit starts with measured Codex totals and leaves estimates out until you ask for them.\n\n[Download skill zip](https://unlock-ai.natebjones.com/guides/token-burn/starter-kits/token-burn-codex-skill.zip)\nClaude\n\n### Claude Code plus Claude chat.\n\nUse this when Claude Code logs are the hard numbers and Claude chat needs honest estimation.\n\n[Download skill zip](https://unlock-ai.natebjones.com/guides/token-burn/starter-kits/token-burn-claude-skill.zip)\nChatGPT\n\n### Export-based estimates.\n\nUse this when your main usage lives in ChatGPT and the dashboard needs to reason from exports or conversation artifacts.\n\n[Download skill zip](https://unlock-ai.natebjones.com/guides/token-burn/starter-kits/token-burn-chatgpt-skill.zip)\nAll Sources\n\n### Codex, Claude, and ChatGPT together.\n\nUse this when you want every source on the same dashboard axes from the beginning.\n\n[Download skill zip](https://unlock-ai.natebjones.com/guides/token-burn/starter-kits/token-burn-all-sources-skill.zip)\nAgents should use the [starter-kit manifest](https://unlock-ai.natebjones.com/guides/token-burn/starter-kits/manifest.json) for SHA-256 checksums, source links, and deterministic download URLs. The same contract is advertised in `/llms.txt` and `/agents.txt`.\n\nWatch the build\n\n\n\n## A screen-led walkthrough before you hand it to your agent.\n\nThis is the quick pass: choose the kit, let the agent fetch the guide context, build locally, then decide what data is safe enough to share.\n\nVisual pass\n\n\n\n## Make the invisible workflow visible.\n\nThe page should feel like the system it is asking you to build: logs moving into rows, rows becoming views, views getting verified before anything ships.\n\nWhy build it\n\n\n\n## A bill tells you what happened. A burn dashboard changes behavior.\n\nMost people pay the bill or hit the wall and assume that is just what AI costs. It is not. The first step to spending less is being able to see what you spend.\n\n01\n\n### You cannot improve what you cannot see.\n\nBurn hides in habits: raw PDFs, runaway conversations, bad model choice, and expensive context. A dashboard turns invisible waste into a number you watch.\n\n02\n\n### It is a fluency meter, not a bill.\n\nTokens per outcome is the clearest tell of whether you are getting better with the tools or just spending harder.\n\n03\n\n### The lesson gets more expensive.\n\nAs models get more capable, sloppy workflows cost more. Measuring early gives you a way to get sharper before the burn scales up.\n\nThe anatomy\n\n\n\n## The heatmap is the conscience. The table is the truth.\n\nStart with daily totals and build outward. The visual layer should make spikes visible, but the raw rows still need to reconcile.\n\nThe old mistake is making this pretty before making it true. Normalize the data first, then give every visualization the same exact-vs-estimated honesty. A dashboard that confidently displays bad math is worse than no dashboard.\n\nThe `driver` field is the move that makes it useful. It turns \"I spent a lot\" into \"I spent a lot on shipping, research, review, or video.\"\n\nBefore you start\n\n\n\n## Nothing exotic. Local files, one agent, one deploy.\n\nIf you already build with a coding agent and can run a dev server, you have the practical skills you need.\n\n### Tools\n\n- Codex App, Claude Code, Cursor, or another coding agent.\n- Node 20+ and a terminal.\n- A Vercel account if you want to publish it.\n- The usage data from whichever tools you want to track.\n\n### Rules\n\n- Keep raw exports out of public repos.\n- Commit only the normalized totals you are comfortable sharing.\n- Label estimated values everywhere.\n- Build local first. Deploy only when the math reconciles.\nStep 1\n\n\n\n## Get the data, then admit how good it is.\n\nSome tools log real token usage. Others force you to estimate. The honest move is making that fidelity visible in the interface.\n\n| Source | Fidelity | How to pull it |\n| --- | --- | --- |\n| **Codex** | exact | Codex app and CLI sessions write local logs with real token usage. Have your agent total input and output by local day. |\n| **Claude Code** | exact | Claude Code stores per-session JSONL with input, output, and cache counts. Include API or agent calls if you use them. |\n| **Claude chat** | estimated | The web and desktop chat path has no tidy local token export. Estimate from message counts and average lengths, then label it honestly. |\n| **ChatGPT** | estimated | Request your data export and tokenize conversation text by date. Treat this as calibrated estimation unless you have exact provider logs. |\n\nDo not let estimates cosplay as measurements. That is the fastest way to make the dashboard feel useful while quietly training you on bad numbers.\n\nStep 2\n\n\n\n## Normalize everything into one daily row.\n\nEvery view is just a different read of this shape. Get this right and the UI becomes straightforward.\n\n```\n// daily-burn.json - one row per day, in your local timezone\n[\n{\n\"date\": \"2026-05-24\",\n\"codex_tokens\": 184320,\n\"claude_code_tokens\": 512880,\n\"claude_code_calls\": 47,\n\"claude_chat_est\": 38000,\n\"chatgpt_est\": 21000,\n\"total\": 756200,\n\"driver\": \"shipping\",\n\"evidence\": \"dashboard build and review\"\n}\n]\n```\n\nStep 3\n\n\n\n## Let the agent build the dashboard, but give it the right spec.\n\nThe difference between a useful agent build and a random dashboard is precision: file shape, view order, hard rules, and verification.\n\n### Token-burn dashboard build prompt\n\nPaste this into your coding agent and let it fetch the guide context.\n\n```\n# Token-Burn Dashboard - Agent Handoff Prompt\n\nYou are my coding agent. Build me a local token-burn dashboard that shows where my AI usage goes and what work the computer should do next.\n\n## First fetch the guide context\n\n1. Read this guide: https://unlock-ai.natebjones.com/guides/build-your-own-token-burn-dashboard\n2. Read the AI discovery file: https://unlock-ai.natebjones.com/llms.txt\n3. Read the starter-kit manifest: https://unlock-ai.natebjones.com/guides/token-burn/starter-kits/manifest.json\n\n## Choose and install the right starter kit\n\nFrom the manifest, choose:\n\n- `codex` if my best source is exact local Codex usage.\n- `claude` if I primarily use Claude Code or Claude chat.\n- `chatgpt` if I primarily use ChatGPT and need export-based estimates.\n- `all-sources` if I want Codex, Claude Code, Claude chat, and ChatGPT on the same dashboard axes.\n\nDownload the kit, verify the SHA-256 against the manifest, unzip it, read `SKILL.md`, and copy `assets/dashboard-starter/` into a new local project folder.\n\n## Build the dashboard\n\nUse the bundled starter app unless there is a clear reason not to. It should render:\n\n1. Daily burn heatmap on a logarithmic color scale.\n2. Weekly trend line using log-normalized totals.\n3. Burn drivers grouped by `driver`.\n4. Scale equivalents with visible approximation math.\n5. Last-30-days moving-average table with every source column.\n\nThe normalized row shape is:\n\n```json\n{\n\"date\": \"YYYY-MM-DD\",\n\"codex_tokens\": 0,\n\"claude_code_tokens\": 0,\n\"claude_code_calls\": 0,\n\"claude_chat_est\": 0,\n\"chatgpt_est\": 0,\n\"total\": 0,\n\"driver\": \"shipping\",\n\"evidence\": \"scrubbed work-family note\"\n}\n```\n\n## Interview me before estimating\n\nIf a source cannot be measured directly, ask short questions and infer a conservative range. Then write a labeled estimate. Inferred numbers are fine. Pretending they are exact is not.\n\nAsk:\n\n- Which tools should be included?\n- What timezone should define a day?\n- Where are exact logs or exports located?\n- Which days were shipping, research, review, video, planning, writing, support, or admin?\n- Which evidence notes must stay private?\n\n## Privacy rules\n\n- Do not include raw logs, exports, prompts, private paths, client names, project names, or secrets in the app.\n- Keep private detail in the local working file if useful.\n- Deploy or share only scrubbed normalized rows.\n\n## When done\n1. Run `npm install`.\n2. Run `npm run build`.\n3. Run the dev server and verify all five views.\n4. Check that exact and estimated labels are visible.\n5. List the files created or changed.\n6. Tell me how to add the next day of data.\n7. Give me the deploy command, but do not deploy until I confirm the public data is scrubbed.\n```\n\nStep 4\n\n\n\n## Review the five views like a builder, not a spectator.\n\nEach view has a job and a common failure mode. Check those before you polish anything.\n\n01\n\n### Daily burn heatmap\n\nThe at-a-glance conscience. It should make a runaway day obvious without flattening quiet days.\n\nUse a log color scale. Linear heatmaps lie when one spike dominates.\n\n02\n\n### Weekly trend line\n\nSmooth the daily noise into a direction. The question is whether usage is compounding or getting leaner.\n\nUse log y-axis again and label the peak week.\n\n03\n\n### What is driving the burn\n\nTurn 'I spent a lot' into 'I spent a lot on shipping, review, or research.'\n\nDriver labels matter. Keep the vocabulary small.\n\n04\n\n### Scale equivalents\n\nTranslate huge token totals into rough human-scale comparisons.\n\nShow the math or it reads as a gimmick.\n\n05\n\n### Moving-average table\n\nThe receipts drawer. This is where exact and estimated sources sit side by side.\n\nMake the trust level obvious at every row.\n\nStep 5\n\n\n\n## Run it locally. Ship it when the math is boring.\n\nThe dashboard does not need a backend for v1. Keep the data file local, build the page, and deploy only the normalized totals you are comfortable exposing.\n\nLocal - `npm install`\n- `npm run dev`, then open `localhost:3000`\n- Confirm all five views render from your data.\nVercel - Push the project to GitHub.\n- Import it at Vercel or run `vercel` from the folder.\n- Set it private if your usage data is personal.\nVerify\n\n\n\n## A subtly wrong token dashboard is worse than none.\n\nBefore you call it done, make the dashboard prove it is telling the truth.\n\n✓\n\n### The totals reconcile.\n\nPick one day. Add each source column by hand. It should match the day's total.\n\n✓\n\n### Exact and estimated numbers are labeled everywhere.\n\nReaders should never need to guess whether a number is measured or inferred.\n\n✓\n\n### The log scales actually read.\n\nA 10x day and a 1x day should both be visible. If the map looks flat, the scale is wrong.\n\n✓\n\n### The time-range selector changes every view.\n\nSwitch 90 days to all-time and confirm the heatmap, trend, drivers, and table all respond.\n\nTroubleshooting\n\n\n\n## The problems are usually data problems.\n\nIf the dashboard feels wrong, debug the input assumptions before you redesign the output.\n\nfix\n\n### Timezones smear your days\n\nLogs are often UTC. Pick one timezone, convert on ingest, and bucket by local date before totals are calculated.\n\nfix\n\n### The heatmap looks flat\n\nThat almost always means a linear color scale. Switch to log and use enough ramp stops to show quiet days.\n\nfix\n\n### Estimates dwarf the real numbers\n\nA bad tokens-per-message constant can swamp the dashboard. Calibrate it against one real conversation.\n\nMake it yours\n\n\n\n## V1 shows the spend. V2 changes the spend.\n\nOnce the dashboard is accurate, add the features that push behavior instead of merely reporting it.\n\n+\n\n### A burn budget\n\nSet a daily target and mark days that blow past it. A line you can cross is a line you start to notice.\n\n+\n\n### Tokens per outcome\n\nTag days with what shipped and divide. Falling cost per outcome is the fluency curve worth bragging about.\n\n+\n\n### Auto-ingest\n\nA small nightly script can read each tool's logs and append a new day, so the dashboard stays current without touching JSON.\n\nEditorial bridge\n\n\n\n## The point is the next delegation decision.\n\nThe dashboard earns its keep when it changes what you hand to the computer tomorrow.\n\n01\n\n### Start where the tokens are already visible.\n\nCodex and Claude Code are the easy on-ramps because they can expose exact usage. Chat tools still work; they just need estimates with labels.\n\n02\n\n### Borrow the design discipline.\n\nThe starter kits already carry the chart shape and restraint. Your job is to keep the dashboard readable enough to return to.\n\n03\n\n### Describe the operating surface in plain language.\n\nAsk for the heatmap, log scale, same-day strip, drivers, source split, and top days. Then iterate until it matches the work you actually do.\n\n04\n\n### Infer what you cannot measure directly.\n\nHave the agent interview you and land on a conservative range. The sin is not estimating. The sin is hiding that you estimated.\n\n05\n\n### Scrub before anything leaves the machine.\n\nPrivate dashboards can name the real work. Public dashboards should keep only normalized totals and generic drivers.\n\n06\n\n### Ship it, or keep it local.\n\nA Vercel deploy is optional. The actual requirement is that the dashboard exists where you can return to it and decide what to hand the computer next.\n\nThis guide is adapted from the original Limited Edition Jonathan build guide and the companion thesis that token burn rate is a revealing metric of AI fluency. Read the related essay on [Nate's newsletter](https://natesnewsletter.substack.com/p/your-claude-sessions-cost-10x-what)."},"author":{"name":"Nate B. Jones","bio":"Track where your AI tokens go, label exact versus estimated usage, and ship a lightweight dashboard to Vercel."},"metadata":{"timestamp":"2026-08-06T05:16:30.313Z"},"media":[{"url":"https://social-archiver-api.social-archive.org/media/archives/arc/ekczP1bxr1/media/0.png","type":"image","width":1200,"height":630}],"aiComments":[]}],"total":123,"pagination":{"page":1,"limit":50,"total":123,"hasMore":true}}}

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Validating Shacl Constraints over a Sparql Endpoint

Abstract