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ml-experiment-tracker
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install 0x-professor/ml-experiment-trackerInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Skill instructions
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.
Source information
- Source
- github
- Repository
- openclaw/skills
- Path
- skills/0x-professor/ml-experiment-tracker
- Version
- —
- Commit
- e35e314163a86a0c680a4b67047a4af977e56078
- Classification source
- rules
- Status
- active
- Visibility
- public
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
Extracted content
SKILL.md
# 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.
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