Agent skill
ml-experiment-tracker
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/ml-experiment-tracker
SKILL.md
ML Experiment Tracker
Overview
Generate structured experiment plans that can be logged consistently in experiment tracking systems.
Workflow
- Define dataset, target task, model family, and parameter search space.
- Define metrics and acceptance thresholds before training.
- Produce run plan with version and artifact expectations.
- Export the run plan for execution in tracking tools.
Use Bundled Resources
- Run
scripts/build_experiment_plan.pyto generate consistent run plans. - Read
references/tracking-guide.mdfor reproducibility checklist.
Guardrails
- Keep inputs explicit and machine-readable.
- Always include metrics and baseline criteria.
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