GitScope

An MCP-powered AI agent that analyzes GitHub repositories and surfaces structured insights — built to help product managers and founders make faster, evidence-based decisions about repos they don't have time to read.

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Problem & Why

When evaluating a new library, vendor repo, or open-source dependency, PMs and founders typically spend 20–30 minutes manually scanning commits, issues, and PRs — and still miss key signals like maintenance gaps, bus factor risk, or stale PRs.

GitScope automates that scan and returns a structured decision brief in under a minute.

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What This Is

A local Python agent that uses GitHub's API + an MCP-connected LLM to generate repo health reports across three lenses: activity health, PR quality, and bug/issue signals.

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Architecture

Input: GitHub repo URL + analysis mode (health / PRs / bugs)
    ↓
GitHub API → fetch commits, PRs, issues, contributor data
    ↓
Chunked context assembly (avoids token limits for large repos)
    ↓
MCP-connected LLM (local) → structured prompt per analysis mode
    ↓
Output: Executive summary + Key findings + Risk signals + Recommended actions

Key files:

• github_agent.py — core agent logic, API calls, prompt construction
• requirements.txt — dependencies (Python)

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What It Produces

For each repo, GitScope returns:

Section	What It Contains
Executive Summary	2–3 sentence repo health verdict
Key Findings	Top 3–5 signals (commit frequency, contributor count, PR merge rate)
Risk Signals	Maintenance gaps, stale issues, single-contributor risk
Recommended Actions	Adopt / Evaluate / Avoid with reasoning

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How to Use

git clone https://github.com/Ruthwik-Data/gitscope
cd gitscope
pip install -r requirements.txt
python github_agent.py --repo https://github.com/owner/repo --mode health

Modes: health | prs | bugs

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Lessons Learned

1. Selective context beats full-repo dumps. Sending all raw API data exceeded token limits and degraded output quality. Chunking by recency and relevance produced sharper summaries.

2. Mode-specific prompts matter. A single generic prompt produced mediocre output across all three lenses. Separate prompt templates per mode (health, PRs, bugs) improved specificity significantly.

3. GitHub API inconsistency is a real constraint. PR data is unreliable for repos that close issues externally or use squash merges. Any metric built on PR count alone will mislead.

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Known Limitations

• No quantitative benchmark yet — output quality is assessed manually, not with a formal eval
• GitHub API rate limits constrain analysis of very active repos (5000 req/hr)
• Does not yet analyze file-level code quality or test coverage
• LLM outputs vary run-to-run without temperature pinning; reproducibility is limited
• No caching — repeated analysis of the same repo re-fetches all data

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Why This Exists

Built as a personal productivity tool during AI stack evaluation sprints — when I needed to assess 10+ repos in a day and wanted structured, comparable output rather than ad hoc notes.

The evaluation gap it exposes: most AI developer tools optimize for code generation, not repo comprehension. This is a small step toward AI-assisted technical due diligence.

adapted

from - https://github.com/Shubhamsaboo/awesome-llm-apps