Agent skill

refactoring-08-experiment-tracking

Use when organizing experiment logs, results, and metadata for Python research code.

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/refactoring-08-experiment-tracking

SKILL.md

Refactoring 08: Experiment Tracking

Goal

Make runs comparable by logging results, configs, and metadata in a consistent structure.

Sequence

  • Order: 08
  • Previous: refactoring-07-documentation-usage
  • Next: refactoring-09-performance-profiling

Workflow

  • Define a run ID scheme and a consistent output directory layout.
    • Success: Each run has a unique ID and predictable output path.
  • Log metrics and key artifacts (plots, model weights, predictions).
    • Success: Metrics and artifacts are saved per run.
  • Save config snapshots and environment info with each run.
    • Success: Run outputs include config and environment details.
  • Provide a simple summary index (CSV/JSON) for comparing runs.
    • Success: Runs can be compared from a single index file.
  • Keep logging lightweight unless a tracking system already exists.
    • Success: Logging adds minimal overhead to runs.

Guardrails

  • Avoid adding heavy tracking frameworks unless requested.
  • Do not store large raw data in run outputs.
  • Keep the logging format stable once introduced.

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