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

7w4.net小葱技能。

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.

这个Skill质量中等偏下，提供了实验计划生成脚本和跟踪指南，但整体功能较为基础。最实用的部分是支持生成多种格式的实验计划文件，以及包含可复现性检查清单。不足之处在于缺少详细的使用说明文档和示例，新手难以快速上手。对于需要完整实验跟踪方案的用户来说，这个工具还需要进一步完善。