The "fabric" project, hosted by Daniel Miessler on GitHub, is an open-source framework designed to enhance human capabilities through AI. It provides a modular system for integrating AI into daily tasks via a collection of AI prompts called Patterns. The project is written in Go and supports multiple AI models, offering both web and CLI interfaces. With over 29,000 stars and 3,011 forks, the project is well-received and actively maintained.
Summary
Quantified Reports
Detailed Reportsextract_domains and updating the README.pattern_explanations.md, detailing pattern functionalities.| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 6 | 1 | 1 | 0 | 1 |
| 30 Days | 20 | 3 | 27 | 1 | 1 |
| 90 Days | 63 | 18 | 154 | 3 | 1 |
| All Time | 585 | 411 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Tvisha |  |
1 | 1/1/0 | 1 | 14 | 3206 |
| github-actions[bot] |  |
1 | 0/0/0 | 2 | 15 | 846 |
| Daniel Miessler |  |
1 | 0/0/0 | 12 | 17 | 266 |
| Perchycs |  |
1 | 1/1/0 | 1 | 1 | 209 |
| None (dependabot[bot]) | 1 | 1/0/0 | 1 | 2 | 86 | |
| Krzysztof Łuczak |  |
1 | 1/1/0 | 1 | 3 | 71 |
| Eugen Eisler |  |
0 | 0/0/0 | 0 | 0 | 0 |
| JM (jmd1010) | 0 | 1/0/2 | 0 | 0 | 0 | |
| David (verebes1) | 0 | 1/0/0 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
| Risk | Level (1-5) | Rationale |
|---|---|---|
| Delivery | 4 | The project faces significant delivery risks due to a growing backlog of unresolved issues. Over the past 90 days, 63 issues were opened while only 18 were closed, indicating a trend of accumulating unresolved problems. This backlog could impede progress towards project goals, as unresolved issues may block critical features or bug fixes. Additionally, the presence of unmerged pull requests and the closure of some without merging suggest potential misalignment in contributions, further complicating timely delivery. |
| Velocity | 4 | The project's velocity is at risk due to the slow pace of issue resolution compared to new openings. With only 1 issue closed in the last 7 days against 6 opened, and similar trends over longer periods, the backlog continues to grow. The reliance on large commits by individual contributors and limited parallel development activities also suggest potential bottlenecks. Furthermore, the transition from Python to Go has introduced setup difficulties for users, potentially slowing down adoption and contribution rates. |
| Dependency | 3 | Dependency risks are present but somewhat mitigated by routine updates managed by Dependabot. However, the limited number of such updates suggests potential gaps in automated dependency management. The transition challenges from Python to Go and integration issues with AI models indicate areas where dependencies could pose risks if not properly managed. The breaking change in 'vite' due to security fixes highlights the importance of careful dependency management. |
| Team | 3 | Team risks are moderate due to potential burnout or conflict arising from the uneven distribution of contributions. Daniel Miessler is a key contributor with significant activity, which could indicate dependency on a single developer for progress. The presence of large commits by Tvisha also suggests potential risks if changes are not thoroughly reviewed by others. Additionally, the closure of pull requests without merging might reflect misalignment within the team. |
| Code Quality | 3 | Code quality risks are moderate due to the presence of large-scale commits that could introduce complexity if not properly reviewed. While there are efforts to improve functionality through pattern additions and typo corrections, the lack of broader team involvement in these changes poses risks. The rollback of version numbers without clear context also suggests potential inconsistencies that could affect code quality. |
| Technical Debt | 4 | Technical debt is a concern given the backlog of unresolved issues and reliance on large commits for significant changes. The persistence of similar issues despite resolutions indicates potential underlying problems that are not fully addressed. The transition from Python to Go has introduced additional complexity that may contribute to technical debt if not managed effectively. |
| Test Coverage | 2 | Test coverage risks have been significantly reduced by Tvisha's commit improving unit test coverage from 0% to 100% for the AI module using Keploy's agent. This enhancement addresses previous gaps and reduces risks related to undetected errors or bugs. However, the magnitude of changes in a single commit could pose risks if not thoroughly reviewed. |
| Error Handling | 3 | Error handling risks are moderate as there is a need for improved documentation and error handling processes to support users through transitions and integrations. Issues related to template variables and subshell behavior suggest gaps in testing or unexpected changes that need better error management strategies. |
Recent activity on the "fabric" GitHub repository indicates a high level of engagement with 174 open issues. The issues range from bug reports and feature requests to questions about usage and installation problems. There is a notable focus on integration with various AI models, installation challenges, and the transition from Python to Go.
Several issues highlight anomalies, such as missing or incorrect configuration options, particularly concerning AI model integration and environment setup. For instance, issues like #1307 and #1306 report bugs related to template variables and subshell behavior, respectively. These indicate potential gaps in documentation or unexpected changes in recent updates.
A recurring theme is the challenge users face with setting up and configuring the environment, particularly with the transition from Python to Go (e.g., issues #884 and #906). Users also report difficulties with specific features like YouTube transcript extraction (#1037) and local LLM integration (#663), suggesting areas where user guidance could be improved.
Overall, the issues suggest a need for improved documentation, particularly around setup and configuration, as well as more robust error handling and user feedback mechanisms within the tool itself.
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.env file. It adds LM Studio as a new plugin and updates the plugin registry and configuration.go-git library version.nanoid and vite packages in the /web directory.vite due to security fixes.Overall, the "fabric" project continues to evolve with contributions focusing on expanding functionality, improving user experience, and maintaining robust code quality through enhanced testing practices.
patterns/pattern_explanations.mdpatterns/extract_domains/system.mdcli/flags.goversion.gocoverage.outgo.modgo.sumgo.mod by providing integrity verification for dependencies.go.mod.cli/cli.goplugins/tools/youtube/youtube.gopatterns/t_extract_panel_topics/system.mdOverall, the source code files exhibit strong organization and adherence to best practices in both Go programming and documentation. Each component serves its intended purpose effectively within the broader framework of the project.
extract_domains, h3 TELOS pattern, challenge handling pattern, and year in review pattern. Also involved in enhancing patterns like panel topic extractor and updating the README.pattern_explanations.md.github.com/go-git/go-git/v5.Focus on Patterns and Features:
Testing and Quality Assurance:
Collaboration and Merging Activities:
Automation and Dependency Management:
Community Engagement and Contributions: