Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing model runs, or registering models.
Install
npx skills add dominodatalab/domino-claude-plugin --skill domino-experiment-trackingnpx skills add dominodatalab/domino-claude-plugin --skill domino-experiment-tracking -a claude-code -gnpx skills add dominodatalab/domino-claude-plugin --skill domino-experiment-tracking -a codex -ggemini skills install https://github.com/dominodatalab/domino-claude-plugin.git --path skills/experiment-trackinggh skill install dominodatalab/domino-claude-plugin domino-experiment-trackinggit clone --depth 1 https://github.com/dominodatalab/domino-claude-plugin /tmp/dominodatalab__domino-claude-plugin
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/dominodatalab__domino-claude-plugin/skills/experiment-tracking ~/.claude/skills/domino-experiment-tracking # Claude Code
cp -R /tmp/dominodatalab__domino-claude-plugin/skills/experiment-tracking ~/.agents/skills/domino-experiment-tracking # Codex, Cursor, Gemini CLI, Copilot, OpenCode