AgentSkillsCN

refactoring-08-experiment-tracking

在组织Python研究代码的实验日志、结果与元数据时使用。

SKILL.md
--- frontmatter
name: refactoring-08-experiment-tracking
description: Use when organizing experiment logs, results, and metadata for Python research code.

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.