Claude-skill-registry github-repo-hunter

Autonomous GitHub repository discovery and integration agent that hunts relevant open-source projects for BidDeed.AI (foreclosure auctions) and Life OS (productivity/ADHD). Searches GitHub for repos matching domain keywords, evaluates relevance via README/tech stack analysis, auto-adds as submodules or archives to target repos, and alerts Ariel with actionable summaries. Use when (1) User requests "find relevant GitHub repos", (2) Exploring new integrations for BidDeed.AI workflows, (3) Discovering productivity tools for Life OS, (4) Keeping repositories updated with latest open-source innovations.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/github-repo-hunter" ~/.claude/skills/majiayu000-claude-skill-registry-github-repo-hunter && rm -rf "$T"

manifest: skills/data/github-repo-hunter/SKILL.md

GitHub Repository Hunter

Autonomous agent for discovering, evaluating, and integrating relevant GitHub repositories into BidDeed.AI and Life OS ecosystems.

Mission

Hunt GitHub for open-source projects that could enhance:

BidDeed.AI - Foreclosure auction intelligence (scrapers, ML models, document processing, workflow automation)
Life OS - Productivity, ADHD management, swimming analytics, dual-timezone coordination

Core Workflow

1. Discovery Phase

Search GitHub API using domain-specific keywords.

Use

scripts/hunt_repos.py

with keywords from

references/keyword_library.md

2. Evaluation Phase

Score each discovered repo (1-100) using

scripts/evaluate_repo.py

Quick Filters (Auto-reject):

Last commit >1 year ago
<10 stars AND <5 forks
No README
Archived repository

Scoring Rubric:

Tech Stack Match (30 pts) - Python/Rust/JS, Supabase, LangGraph, GitHub Actions
Domain Relevance (40 pts) - Foreclosure tools, ADHD/productivity, swimming analytics
Code Quality (20 pts) - Tests, docs, active issues
Community (10 pts) - Stars, forks, contributors

Thresholds:

Score ≥70 → AUTO_ADD to integrations/
Score 50-69 → ALERT_ARIEL (needs review)
Score <50 → SKIP (log to rejected_repos.txt)

3. Integration Phase

Execute

scripts/integrate_repo.py

for AUTO_ADD repos.

Integration Methods: A. Git submodule in integrations/ folder B. Reference entry in docs/integrations.md

4. Alert Phase

Insert discovery notification to Supabase insights table.

Alert Format:

🔍 GitHub Repo Hunter - Found: {repo_name}

Score: {score}/100 (AUTO_ADD / REVIEW_NEEDED)
Repository: https://github.com/{user}/{repo}
Language: {languages}
Last Updated: {last_commit}
Stats: ⭐ {stars} | 🍴 {forks} | 👥 {contributors}

What It Does:
{readme_summary}

Potential Use Cases:
- BidDeed.AI: {use_case}
- Life OS: {use_case}

Tech Stack Match:
✅ {matched_tech}
❌ {missing_tech}

Recommendation: {action}
Next Steps: {specific_action}

Scripts

scripts/hunt_repos.py

- Main hunter with GitHub API

scripts/evaluate_repo.py

- Scoring algorithm

scripts/integrate_repo.py

- Auto-add to repos

References

references/keyword_library.md

- Search keywords for both domains

references/github_api.md

- GitHub Search API docs

references/scoring_rubric.md

- Detailed evaluation criteria

Critical Rules

Never auto-add without evaluation
Respect GitHub API rate limits (5000 req/hr with auth)
Test before integrating
Document every integration in docs/integrations.md
Alert on threshold (50-69 MUST notify Ariel)

Target Repositories

Primary:

```
breverdbidder/biddeed-conversational-ai
```
```
breverdbidder/life-os
```

Secondary:

```
breverdbidder/brevard-bidder-scraper
```
(archive-only)

Decision Tree

Discovered Repo
    |
    ├─ Score ≥70? → Auto-add + Alert (FYI)
    ├─ Score 50-69? → Supabase alert + Review needed
    └─ Score <50? → Skip (log rejection)