cookiecutter-uv-lite

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A lightweight, modern Python project template for packages, CLIs, and notebooks — powered by uv.

Choose your project type:

• 📦 Package: Python libraries and packages
• 🚀 CLI: Command-line applications with Typer and Rich
• 📊 Notebooks: Data science projects with JupyterLab, pandas, and visualization tools

Features:

• uv for dependency management
• Code quality with ruff, mypy, deptry and prettier
• Testing with pytest and pytest-xdist for parallel execution
• Documentation with MkDocs
• Compatibility testing for multiple versions of Python with tox-uv
• GitHub Actions CI/CD with automated testing, documentation deployment, and security scanning
• Enterprise GitHub support with SSH-first authentication

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Documentation

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Quickstart

On your local machine, navigate to the directory in which you want to create a project directory, and run the following command:

uvx cookiecutter gh:matrig/cookiecutter-uv-lite

You'll be prompted to configure your project (12 questions):

1. project_type: Choose between package (library), cli (command-line app), or notebooks (data science)
2. project_name: Your project name (e.g., my-awesome-project)
3. project_description: Short description of your project
4. author: Your name
5. author_email: Your email
6. author_username: Your GitHub username
7. git_repo [y/n]: Initialize git repository and optionally create remote on GitHub
8. git_server: GitHub server (default: github.com, or your enterprise domain)
9. private_repo [y/n]: Create private repository
10. mkdocs [y/n]: Include MkDocs documentation
11. github_actions [y/n]: Enable GitHub Actions CI/CD
12. codecov [y/n]: Enable Codecov integration

Project Types

📦 Package - Python libraries and packages

• Includes example module with function
• Standard package structure
• Ready for PyPI publishing

🚀 CLI - Command-line applications

• Built with Typer (modern CLI framework)
• Styled output with Rich
• Executable entry point configured
• make run command with argument support

📊 Notebooks - Data science and analysis projects

• JupyterLab for interactive development
• Data science stack: pandas, numpy, matplotlib, seaborn
• Sample notebooks with exploratory analysis and visualization examples
• notebooks/ directory for analysis, data/ directory for datasets
• Notebook testing with nbval
• Helper utilities module for reusable code
• make jupyter to launch JupyterLab

GitHub Actions Features

When you enable GitHub Actions (github_actions: y), your generated project includes:

🚀 Continuous Integration (CI)

• Multi-Python testing: Automatically tests your code on Python 3.9-3.13
• Multi-OS testing: Tests on Ubuntu, macOS, and Windows
• Code quality checks: Automated linting (ruff), formatting, and type checking (mypy)
• Coverage reporting: Optional integration with Codecov

📚 Documentation Deployment

• Auto-deploy to GitHub Pages: Documentation automatically updates when you push to main
• MkDocs integration: Works seamlessly if you enable MkDocs

🔒 Security Scanning

• CodeQL analysis: GitHub's native security scanning
• Dependency scanning: Automated vulnerability detection with Safety
• Scheduled security checks: Weekly automated scans

📈 Professional Setup

Your generated project gets enterprise-grade CI/CD that:

• ✅ Prevents bad code from being merged
• ✅ Automatically deploys documentation
• ✅ Monitors for security vulnerabilities
• ✅ Works with both GitHub.com and GitHub Enterprise

Acknowledgements

This project is based on https://github.com/patrickmineault/true-neutral-cookiecutter and https://github.com/fpgmaas/cookiecutter-uv.