ml-experiment-tracker

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

作者: admin | 来源: ClawHub

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ClawHub

版本

V 0.1.0

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概述

安装方式

版本历史

ml-experiment-tracker

# 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.

通过对话安装

该技能支持在以下平台通过对话安装：

OpenClaw WorkBuddy QClaw Kimi Claude

方式一：安装 SkillHub 和技能

帮我安装 SkillHub 和 ml-experiment-tracker-1776296793 技能

方式二：设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源，然后帮我安装 ml-experiment-tracker-1776296793 技能

通过命令行安装

skillhub install ml-experiment-tracker-1776296793

下载 Zip 包

⬇ 下载 ml-experiment-tracker v0.1.0

文件大小: 3.43 KB | 发布时间: 2026-4-16 17:45

v0.1.0 最新 2026-4-16 17:45

Initial release of ml-experiment-tracker.

- Plan ML experiment runs with explicit parameters, metrics, and artifacts.
- Standardize experiment definitions for tracking and reproducibility.
- Generate structured run plans before model training.
- Provides scripts for creating run plans and reference guides for best practices.