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Ml experiment tracker

Skill 0x-Professor/Agent-Skills-Hub/skills/ml-experiment-tracker

Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.From its SKILL.md

Install
npx -y skills add 0x-Professor/Agent-Skills-Hub --skill ml-experiment-tracker

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 10 stars10 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 1 command, including `scripts/build_experiment_plan.py`.

SKILL.md

0.9 KB, 131 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it: 3 files

4.4 KB alongside SKILL.md, 1 of them executable

agents/

references/

scripts/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.