ml-setup

To set up an ML development environment, this Skill installs uv or pixi, adds ML dependencies like PyTorch, and configures ruff, mypy, and pytest. It detects existing configuration files to standardize your repository automatically.

The best way to bootstrap a reproducible ML project is using a modern package manager. This Skill chooses between uv and pixi based on your needs, installs PyTorch and Hydra dependencies, and generates standardized testing configurations.

Yes, ML environment setup works with existing files by performing project-state detection. The Skill identifies existing configuration files like pyproject.toml, pixi.toml, or requirements.txt before deciding to initialize or reconfigure your workspace.

You configure ruff, mypy, and pytest for a PyTorch project by requesting dev tooling setup during initialization. The Skill generates the necessary configuration files to standardize code quality and validation for your new ML experimentation workflow.

Yes, you can use pixi instead of uv for ML dependencies. The Skill chooses between uv or pixi based on your specific needs, then installs required PyTorch and Hydra tooling while applying development environment configurations.

Automating pre-commit and linting configuration for ML repositories prevents code quality issues and ensures reproducibility. This Skill optionally sets up pre-commit hooks and generates ruff and mypy configurations to validate the resulting environment automatically.