github-repo-hunter

name

github-repo-hunter

description

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.

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)