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GitHub Stars
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Bundled Files
5 months ago
Catalog Refreshed
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First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill openclaw/skills --skill github-cred- _meta.json276 B
- README.md1.6 KB
- SKILL.md2.2 KB
Overview
This skill analyzes a GitHub user's contribution quality and produces a concise credibility score. It evaluates owned repositories, external contributions, output quality metrics, and social influence to generate a 0–100 score and a grade. Use the triggers "github cred" or "analyze github" followed by a username to run the analysis.
How this skill works
The skill inspects the user's owned repositories for high-star projects, counts merged pull requests in external projects, and measures output quality like merge rate, code vs. non-code activity, and contribution diversity. It also reads follower counts and social signals to compute a four-tier score (Owned repos, External contributions, Output quality, Social influence) with configurable caps per tier. The final report formats the numeric score, tier breakdowns, and highlights the strongest contributions or projects.
When to use it
- Assess a maintainer or contributor before forming a collaboration
- Screen candidates for open-source roles or contractor work
- Compare community standing between multiple GitHub profiles
- Decide whether to trust or adopt a library based on its creator
- Quick lookup with triggers like "github cred <username>" or "analyze <username>'s github"
Best practices
- Run the analysis with a recent activity window to reflect current contribution state
- Interpret the score as a summary signal, then review highlighted repos and PRs manually
- Combine with code review of core contributions for hiring or security decisions
- Consider domain context: niche projects may have lower stars but high technical value
- Use the two paths (creator vs contributor) to understand strength origin
Example use cases
- Hiring manager checks an applicant's open-source influence and merge history
- Project maintainer evaluates potential core contributors before granting write access
- Developer compares two library authors to decide which to follow or sponsor
- Community moderator spots high-impact contributors for recognition or invites
- Technical lead prioritizes candidates who demonstrate sustained merged PRs in top projects
FAQ
It uses public GitHub metadata: repo star counts, PR merge records, follower counts, and commit/PR sizes to compute metrics.
Can the score be gamed by artificial activity?
The metric mix reduces simple gaming: owned high-star repos and external merged PRs carry heavy weight, and output quality factors penalize low merge rates and low-code activity.