OpenClaw
Task Router Skill
Task Router
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ml-experiment-tracker
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
安装与使用
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。
SkillHub CLI
skillhub install 0x-professor/ml-experiment-tracker打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。
技能说明
ML Experiment Tracker
Overview
Generate structured experiment plans that can be logged consistently in experiment tracking systems.
Workflow
- Define dataset, target task, model family, and parameter search space.
- Define metrics and acceptance thresholds before training.
- Produce run plan with version and artifact expectations.
- Export the run plan for execution in tracking tools.
Use Bundled Resources
- Run
scripts/build_experiment_plan.pyto generate consistent run plans. - Read
references/tracking-guide.mdfor reproducibility checklist.
Guardrails
- Keep inputs explicit and machine-readable.
- Always include metrics and baseline criteria.
来源信息
- Source
- github
- Repository
- openclaw/skills
- Path
- skills/0x-professor/ml-experiment-tracker
- Version
- —
- Commit
- e35e314163a86a0c680a4b67047a4af977e56078
- 分类来源
- rules
- 状态
- active
- 可见性
- public
这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。
来源内容
SKILL.md
# 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.
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