github-repo-hunter

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

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/hunt_repos.py - Main hunter with GitHub API

scripts/evaluate_repo.py - Scoring algorithm

scripts/integrate_repo.py - Auto-add to repos

references/keyword_library.md - Search keywords for both domains

references/github_api.md - GitHub Search API docs

references/scoring_rubric.md - Detailed evaluation criteria

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)

Primary:

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

Secondary:

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

```

Discovered Repo

|

├─ Score ≥70? → Auto-add + Alert (FYI)

├─ Score 50-69? → Supabase alert + Review needed

└─ Score <50? → Skip (log rejection)

```