Skill-to-MCP

# Contributing to skill-to-mcp Thank you for your interest in contributing to skill-to-mcp! ## Development Setup 1. Clone the repository: ```bash git clone https://github.com/biocontext-ai/skill-to-mcp.git cd skill-to-mcp ``` 2. Install in development mode with all dependencies: ```bash uv venv --python 3.13 source .venv/bin/activate uv sync --all-extras ``` 3. Install pre-commit hooks: ```bash pre-commit install ``` ## Running Tests The test suite uses the included `skills/` directory for testing: ```bash uv run pytest tests/ -v ``` With coverage: ```bash uv run pytest tests/ --cov=src/skill_to_mcp --cov-report=html ``` ## Code Style This project uses: - **ruff** for linting and formatting - **pre-commit** for automated checks Before committing, ensure your code passes all checks: ```bash uv run pre-commit run --all-files ``` ## Adding New Skills To contribute example skills to the repository, place them in the `skills/` directory. Each skill: 1. Must have its own subdirectory 2. Must contain a `SKILL.md` file with valid YAML frontmatter 3. Should follow the naming convention: lowercase-with-hyphens Example structure: ``` skills/ └── my-new-skill/ ├── SKILL.md # Required: Skill documentation with frontmatter ├── scripts/ # Optional: Implementation scripts │ └── main.py └── references/ # Optional: Reference materials └── docs.md ``` ### SKILL.md Format ```markdown --- name: my-new-skill description: Brief description of what this skill does and when to use it --- # Skill Title Detailed documentation here... ``` ## Project Structure ``` skill-to-mcp/ ├── src/skill_to_mcp/ │ ├── __init__.py │ ├── main.py # CLI entry point │ ├── mcp.py # FastMCP server configuration │ ├── skill_parser.py # Skill parsing logic │ └── tools/ │ ├── __init__.py │ └── _skills.py # MCP tool implementations ├── skills/ # Skill directories │ └── single-cell-rna-qc/ ├── tests/ │ ├── test_app.py # Integration tests │ └── test_skill_parser.py # Unit tests └── docs/ # Documentation ``` ## Pull Request Process 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Make your changes 4. Ensure tests pass (`uv run pytest tests/`) 5. Ensure code style is correct (`pre-commit run --all-files`) 6. Commit your changes (`git commit -m 'Add amazing feature'`) 7. Push to your branch (`git push origin feature/amazing-feature`) 8. Open a Pull Request ## Reporting Issues When reporting issues, please include: 1. A clear description of the problem 2. Steps to reproduce 3. Expected vs actual behavior 4. Your environment (Python version, OS, etc.) 5. Relevant error messages or logs ## License By contributing, you agree that your contributions will be licensed under the same license as the project.