github-repo-hunter（GitHub repo hunter 搜索）

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:

1. BidDeed.AI - Foreclosure auction intelligence (scrapers, ML models, document processing, workflow automation)
2. 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

1. Never auto-add without evaluation
2. Respect GitHub API rate limits (5000 req/hr with auth)
3. Test before integrating
4. Document every integration in docs/integrations.md
5. 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)