name: ml-experiment-tracker description: Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.

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.

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Guardrails

• Keep inputs explicit and machine-readable.
• Always include metrics and baseline criteria.

這個Skill質量中等偏下，提供了實驗計劃生成指令碼和跟蹤指南，但整體功能較為基礎。最實用的部分是支援生成多種格式的實驗計劃檔案，以及包含可復現性檢查清單。不足之處在於缺少詳細的使用說明文件和示例，新手難以快速上手。對於需要完整實驗跟蹤方案的使用者來說，這個工具還需要進一步完善。