ml-workflow

Use when designing end-to-end ML workflows. Covers experiment tracking, feature engineering and storage, model training pipelines, serving and deployment, A/B testing, and drift monitoring. Do not use for data warehouse schema design (use schema-evaluation) or ETL pipeline architecture (use pipeline-design).

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仓库: tomevault-io/skills-registry
最近来源活动: 2026年5月11日 15:30
检测到的 SKILL.md 语言: 英语
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分支: 0

安装方式

默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

直接命令不会经过审查 Prompt；运行前请先检查来源。

npx skills add https://github.com/tomevault-io/skills-registry --skill ml-workflow

命令会保持在同一行。复制前请横向滚动并检查完整内容。

想先保存到本地？可下载 SkillsMP 当前能够提供的文件。

检查来源文件

决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。

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2 个文件

SKILL.md

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name	ml-workflow
description	Use when designing end-to-end ML workflows. Covers experiment tracking, feature engineering and storage, model training pipelines, serving and deployment, A/B testing, and drift monitoring. Do not use for data warehouse schema design (use schema-evaluation) or ETL pipeline architecture (use pipeline-design).
metadata	{"author":"dtsong"}

name	ml-workflow
description	Use when designing end-to-end ML workflows. Covers experiment tracking, feature engineering and storage, model training pipelines, serving and deployment, A/B testing, and drift monitoring. Do not use for data warehouse schema design (use schema-evaluation) or ETL pipeline architecture (use pipeline-design).
metadata	{"author":"dtsong"}

ml-workflow

安装方式

检查来源文件

ML Workflow

Purpose

Scope Constraints

Inputs

Input Sanitization

Procedure

Progress Checklist

Step 1: Define the ML Problem Clearly

Step 2: Design the Feature Engineering Pipeline

Step 3: Design Experiment Tracking

Step 4: Design the Training Pipeline

Step 5: Design Model Serving

Step 6: Design A/B Testing for Models

Step 7: Design Monitoring and Drift Detection

Handoff

Output Format

Quality Checks

Evolution Notes

ML Workflow

Purpose

Scope Constraints

Inputs

Input Sanitization

Procedure

Progress Checklist

Step 1: Define the ML Problem Clearly

Step 2: Design the Feature Engineering Pipeline

Step 3: Design Experiment Tracking

Step 4: Design the Training Pipeline

Step 5: Design Model Serving

Step 6: Design A/B Testing for Models

Step 7: Design Monitoring and Drift Detection

Handoff

Output Format

Quality Checks

Evolution Notes

ml-workflow

来源信息

安装方式

检查来源文件

同仓库更多 Skills

同仓库更多 Skills

ML Workflow

Purpose

Scope Constraints

Inputs

Input Sanitization

Procedure

Progress Checklist

Step 1: Define the ML Problem Clearly

Step 2: Design the Feature Engineering Pipeline

Step 3: Design Experiment Tracking

Step 4: Design the Training Pipeline

Step 5: Design Model Serving

Step 6: Design A/B Testing for Models

Step 7: Design Monitoring and Drift Detection

Handoff

Output Format

Quality Checks

Evolution Notes

ML Workflow

Purpose

Scope Constraints

Inputs

Input Sanitization

Procedure

Progress Checklist

Step 1: Define the ML Problem Clearly

Step 2: Design the Feature Engineering Pipeline

Step 3: Design Experiment Tracking

Step 4: Design the Training Pipeline

Step 5: Design Model Serving

Step 6: Design A/B Testing for Models

Step 7: Design Monitoring and Drift Detection

Handoff

Output Format

Quality Checks

Evolution Notes