ML Experimentation

This skill guides a hypothesis-driven ML experiment life cycle: planning, fast iteration, script execution, targeted logging, journaling, diagnostic visualization, and scientific report writing.

Usage

Use this skill when the user wants to run an ML experiment, test a model or idea, or write up experiment results. First decide: new experiment (different question → new experiment directory) or new run (same question, tweaks → new run under runs/). See references/experiment-setup.md for that disambiguation, hypothesis scoping, and the fast-iteration checklist.

Requirements

• Python 3.11+ with uv or pixi for running scripts: uv run script.py or, when pixi is the environment manager, pixi run python script.py (pixi reads pyproject.toml or pixi.toml).
• Dependencies declared via PEP723 inline script metadata in each script (or, with pixi, in pyproject.toml / pixi.toml).
• Respect the user's training framework (PyTorch, JAX, TensorFlow, etc.). Run scripts in a GPU-enabled environment wherever possible: with uv use GPU-enabled deps (e.g. JAX GPU extras, PyTorch via [[tool.uv.index]] CUDA index in the script block); with pixi use a GPU-enabled environment defined in pyproject.toml or pixi.toml. Fall back to CPU only when GPU is unavailable. See references/script-patterns.md.

What It Does