Eureka

AI-first engineer onboarding factory. Repo in, onboarding package out.

What It Does

Point Eureka at any GitHub repo and get back a complete onboarding package:

1. Architecture Overview — how the codebase works, not just what files exist
2. CLAUDE.md — AI-tuned onboarding guide with "Start Here" and "AI Working Patterns" sections
3. Suggested Hooks — stack-appropriate Claude Code hooks, ready to paste
4. Starter Skills File — teaches AI agents how to work in this specific codebase

How It Works

A multi-agent pipeline orchestrated with LangGraph:

GitHub URL → Explorer → Architect → CLAUDE.md Writer → Hooks Generator → Skills Writer → Package

Each agent has a specialized Skill file (in .claude/skills/) that defines its behavior and output quality. Agents communicate through a shared memory file (memory/{run_id}.md) — each agent reads previous outputs and appends its own.

The Explorer runs locally as a Python process to clone and analyze repos (50 files max, 200 lines per file). The other four agents call the Anthropic API with context from their Skill files and the shared memory.

Setup

# Clone
git clone https://github.com/dawnkelly09/eureka.git
cd eureka

# Install dependencies
pip install -r requirements.txt

# Configure
cp .env.example .env
# Edit .env with your keys:
#   ANTHROPIC_API_KEY  — required, powers the four AI agents
#   GITHUB_TOKEN       — required, for cloning repos via the Explorer
#   LANGCHAIN_API_KEY  — optional, enables LangSmith tracing

# Run the API
uvicorn orchestrator:app --reload

# Run the UI (separate terminal)
cd ui && npm install && npm run dev

Test the Explorer directly

python -m orchestrator.nodes.explorer https://github.com/fastapi/fastapi

API

# Health check
curl http://localhost:8000/health

# Start analysis
curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"repo_url": "https://github.com/fastapi/fastapi"}'

# Check results
curl http://localhost:8000/results/{run_id}

Agent Skills

The quality of Eureka's output is driven by Skill files — structured prompts that teach each agent how to produce repo-specific artifacts:

Agent	Skill File	What It Produces
Explorer	.claude/skills/explorer/SKILL.md	Repo structure, stack detection, file summaries
Architect	.claude/skills/architect/SKILL.md	Architecture overview with data flow and patterns
CLAUDE.md Writer	.claude/skills/claude-md-writer/SKILL.md	Onboarding-focused CLAUDE.md
Hooks Generator	.claude/skills/hooks-generator/SKILL.md	Stack-appropriate Claude Code hooks
Skills Writer	.claude/skills/skills-writer/SKILL.md	Starter Skills file for the target repo

Tech Stack

• Pipeline: Python, LangGraph, Anthropic API
• API: FastAPI
• Frontend: Vite + React + TypeScript
• Tracing: LangSmith (optional)

Note: this project used https://github.com/ashtilawat/minimum-viable-factory created by Gauntlet AI's Ashalesh Tilawat (@ashtilawat) for a Night School session where attendees were invited to take the factory and extend, modify, and make it their own.