Automatically generate high-quality markdown context files for AI coding agents like Claude, Cursor, and Gemini. Analyzes public GitHub repositories to create documentation for AI agents to use. Connects to GitHub and AI coding agents.
git clone https://github.com/saharmor/sidekick-dev-web.gitAutomatically generate high-quality markdown context files for AI coding agents like Claude, Cursor, and Gemini. Analyzes public GitHub repositories to create documentation for AI agents to use. Connects to GitHub and AI coding agents.
1. **Install the Sidekick tool**: Add the Sidekick extension to your preferred AI coding agent (Claude, Cursor, or Gemini) via their respective extension marketplaces. 2. **Configure GitHub access**: Ensure your AI agent has access to public repositories (no private repo support in free tier). Grant read permissions to the Sidekick tool. 3. **Run the analysis**: Execute the command `/sidekick-dev-web analyze [REPOSITORY_URL]` in your AI agent's chat interface. For example: `/sidekick-dev-web analyze https://github.com/example/fastapi-ecommerce` 4. **Review and refine**: The tool will generate a markdown context file. Review it for accuracy, then ask your AI agent to perform specific tasks using this context. For example: "Based on this context, help me implement a new payment provider integration." 5. **Iterate as needed**: If the AI agent gets stuck, regenerate the context with updated instructions: `/sidekick-dev-web analyze [REPOSITORY_URL] --focus [SPECIFIC_AREA]` (e.g., `--focus order_service`) **Pro Tips:** - For large repositories (>10k lines), specify a subdirectory to focus analysis: `/sidekick-dev-web analyze [REPO_URL] --path src/backend` - Combine with your agent's context window by pasting the generated file into the chat before asking questions - Use the `--include-diagrams` flag to generate Mermaid diagrams for architecture visualization
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/saharmor/sidekick-dev-webCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Generate a comprehensive markdown context file for [REPOSITORY_URL] to help an AI coding agent understand the project structure, dependencies, and key components. Include sections for: project overview, architecture diagram (ASCII or Mermaid), key modules with their purposes, environment setup instructions, API endpoints (if applicable), test suites, and common troubleshooting steps. Prioritize clarity for an AI agent working on this codebase.
```markdown
# Project Context: FastAPI E-commerce Backend
**Repository:** https://github.com/example/fastapi-ecommerce
**Last Updated:** 2024-05-15
## Project Overview
A production-ready e-commerce backend built with FastAPI, PostgreSQL, and Redis. Supports user authentication, product catalog, shopping cart, and order processing. Designed for horizontal scalability with Docker and Kubernetes manifests included.
## Architecture Diagram
```mermaid
graph TD
A[Client] -->|HTTPS| B[Load Balancer]
B --> C[API Gateway]
C --> D[Auth Service]
C --> E[Product Service]
C --> F[Order Service]
D --> G[(PostgreSQL)]
E --> G
F --> G
F --> H[(Redis)]
```
## Key Modules
1. **auth_service/**
- JWT token generation/validation
- Password hashing (bcrypt)
- Role-based access control
- Rate limiting (100 requests/minute)
2. **product_service/**
- CRUD operations for products
- Image upload to S3
- Search via Elasticsearch
- Inventory tracking
3. **order_service/**
- State machine for order lifecycle
- Payment gateway integration (Stripe)
- Webhook handling for payment events
- Async task queue (Celery)
## Environment Setup
```bash
# Clone and setup
git clone https://github.com/example/fastapi-ecommerce
cd fastapi-ecommerce
# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt
# Environment variables
export DATABASE_URL=postgresql://user:pass@localhost:5432/ecommerce
export JWT_SECRET=your-secret-key
export STRIPE_API_KEY=sk_test_...
# Run services
docker-compose up -d postgres redis
airflow db upgrade
uvicorn main:app --reload
```
## API Endpoints
- `POST /auth/register` - User registration
- `POST /auth/login` - JWT token generation
- `GET /products` - List products with pagination
- `POST /orders` - Create new order
- `GET /orders/{id}` - Order details
## Test Suites
- Unit tests: `pytest tests/unit/` (85% coverage)
- Integration tests: `pytest tests/integration/` (Docker required)
- Load tests: `locustfile.py` (simulates 1000 concurrent users)
## Common Issues
1. **Database connection fails**
- Verify `DATABASE_URL` environment variable
- Check PostgreSQL is running: `docker ps | grep postgres`
- Reset migrations: `alembic downgrade base && alembic upgrade head`
2. **Celery worker not processing tasks**
- Verify Redis is accessible
- Check worker logs: `docker logs celery_worker`
- Restart worker: `celery -A app.celery worker --loglevel=info`
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