Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.
Install
npx skills add microsoft/hve-core --skill ml-experimentationnpx skills add microsoft/hve-core --skill ml-experimentation -a claude-code -gnpx skills add microsoft/hve-core --skill ml-experimentation -a codex -ggemini skills install https://github.com/microsoft/hve-core.git --path .github/skills/data-science-engineering/ml-experimentationgh skill install microsoft/hve-core ml-experimentationgit clone --depth 1 https://github.com/microsoft/hve-core /tmp/microsoft__hve-core
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/microsoft__hve-core/.github/skills/data-science-engineering/ml-experimentation ~/.claude/skills/ml-experimentation # Claude Code
cp -R /tmp/microsoft__hve-core/.github/skills/data-science-engineering/ml-experimentation ~/.agents/skills/ml-experimentation # Codex, Cursor, Gemini CLI, Copilot, OpenCode