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)