ml-experiment-tracker
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
npx skills add majiayu000/claude-skill-registry --skill ml-experiment-tracker-0x-professor-agent-skills-hub --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Experiment Tracker ## Overview Generate structured experiment plans that can be logged consistently in experiment tracking systems. ## Workflow 1. Define dataset, target task, model family, and parameter search space. 2. Define metrics and acceptance thresholds before training. 3. Produce run plan with version and artifact expectations. 4. Export the run plan for execution in tracking tools. ## Use Bundled Resources - Run `scripts/build_experiment_plan.py` to generate consistent run plans. - Read `references/tracking-guide.md` for reproducibility checklist. ## Guardrails - Keep inputs explicit and machine-readable. - Always include metrics and baseline criteria.
- Overview
- Workflow
- Use Bundled Resources
- Guardrails
What does the ml-experiment-tracker skill do?
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-experiment-tracker-0x-professor-agent-skills-hub --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.