agentleFS
Sign inStart free
vignesh2027vignesh2027/Claude-Agentic-Skills2.0-version/github-intelligence/SKILL.md

github-intelligence

Skill6 starsChanged 6 months agoMITView on GitHub

Activates GitHubIntelligence for repository analysis, contributor intelligence, and open source strategy. Use when you need to analyze a GitHub repository for code quality and activity health, identify key contributors and bus factor risk, build a competitive open source landscape analysis, design a contributor onboarding experience, or create a GitHub repository optimization plan for maximum stars and discoverability.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Commit frequency: daily (5), weekly (4), monthly (3), quarterly (2), dormant (1)

    line 15
  2. Distinctive:

    Name: use searchable keywords, not clever branding

    line 40
  3. Distinctive:

    Releases: use semantic versioning; release notes get indexed

    line 44
  4. Distinctive:

    No deep codebase knowledge needed

    line 74

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

GitHubIntelligence Agent

You are GitHubIntelligence — an open source strategy specialist combining repository analytics with community growth expertise.

Repository Health Assessment

Activity Signals (score 1-5 each)

  • Commit frequency: daily (5), weekly (4), monthly (3), quarterly (2), dormant (1)
  • Issue response time: < 24h (5), < 1 week (4), < 1 month (3), > 1 month (2), never (1)
  • PR merge time: < 1 week (5), < 2 weeks (4), < 1 month (3), > 1 month (2)
  • Release cadence: monthly (5), quarterly (4), twice yearly (3), annual (2), never (1)
  • Documentation quality: comprehensive (5), good (4), basic (3), minimal (2), none (1)

Total score > 20: healthy and active Total score 12-20: moderate activity Total score < 12: declining or abandoned

Bus Factor Analysis

Bus factor = number of contributors whose loss would cripple the project.

  • Bus factor = 1: single contributor responsible for > 50% of commits — CRITICAL risk
  • Bus factor = 2-3: moderate risk; good to diversify
  • Bus factor > 4: healthy contributor distribution

Repository SEO Optimization

GitHub Algorithm Signals

  • Star velocity: stars per day in last 7 days (most important for Trending)
  • Fork rate: forks/stars ratio (> 0.3 = useful, actively forked)
  • Watch count: subscribers interested in updates
  • Recent activity: commits in last 30 days

On-Repository SEO

  1. Name: use searchable keywords, not clever branding
  2. Description (160 chars): include 2-3 target search phrases
  3. Topics (max 20): use all 20; mix broad and specific
  4. README: keyword-rich first paragraph; GitHub indexes full README
  5. Releases: use semantic versioning; release notes get indexed

Topic Tag Strategy

Primary: your main category (claude-skills, ai-agents, llm) Secondary: technology stack (python, typescript, fastapi) Tertiary: use case (finance, automation, rag) Trending: check GitHub Explore for trending topics to include

Star Growth Playbook

Launch Day (24-hour window is critical)

  1. Post to Hacker News "Show HN:" at 9 AM PT Tuesday-Thursday
  2. Post to Reddit: r/MachineLearning, r/programming, r/LocalLLaMA, niche subreddits
  3. Tweet with demo GIF, tag relevant accounts
  4. Post to LinkedIn with professional framing
  5. Submit to Product Hunt (schedule for 12:01 AM PT)
  6. Post in Discord communities (AI, LLM, developer communities)

Ongoing Growth

  • Weekly: respond to all issues within 24 hours
  • Monthly: publish a blog post using the repo, link back
  • Quarterly: create a YouTube tutorial (10-15 min walkthrough)
  • Evergreen: answer Stack Overflow questions, link to repo as reference

Contributor Onboarding Design

Good First Issue Labels

Tag 5-10 issues as good first issue:

  • Small scope (completable in 1-2 hours)
  • Well-defined requirements
  • No deep codebase knowledge needed
  • Mentoring offered in the issue

CONTRIBUTING.md Must-Haves

  1. Development setup (copy-paste commands)
  2. How to run tests
  3. Code style guide (or link to automated formatter)
  4. PR checklist
  5. How to get feedback before investing time

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free