OpenClaw

mlops-prototyping-cn

Structured Jupyter notebook prototyping with pipeline integrity

guohongbin-git0 来源安装数☆ 0 仓库 Starsv1.0.0

查看来源 ↗安装与使用 SKILL.md ↗

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 guohongbin-git/mlops-prototyping-cn

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。

查看来源 ↗使用指南

技能说明

MLOps Prototyping 🔬

Create standardized, reproducible Jupyter notebooks.

Features

1. Notebook Structure Check ✅

Validate notebook follows best practices:

./scripts/check-notebook.sh notebook.ipynb

Checks for:

H1 title
Imports section
Config/Constants
Data loading
Pipeline usage

2. Template 📝

Use this structure:

Title & Purpose
Imports (standard → third-party → local)
Configs (all constants at top)
Datasets (load, validate, split)
Analysis (EDA)
Modeling (use sklearn.pipeline.Pipeline)
Evaluations (metrics on test data)

Quick Start

# Check your notebook
./scripts/check-notebook.sh my-notebook.ipynb

# Follow structure in notebook
# Use Pipeline for all transforms
# Set RANDOM_STATE everywhere

Key Rules

✅ DO:

Put all params in Config section
Use sklearn.pipeline.Pipeline
Split data BEFORE any transforms
Set random_state everywhere

❌ DON'T:

Magic numbers in code
Manual transforms (use Pipeline)
Fit on full dataset (data leakage)

Author

Converted from MLOps Coding Course

Changelog

v1.0.0 (2026-02-18)

Initial OpenClaw conversion
Added notebook checker

来源信息

Source: github
Repository: openclaw/skills
Path: skills/guohongbin-git/mlops-prototyping-cn
Version: 1.0.0
Commit: e7ccae70454c918fb12c7bcda0b6a192eda99e95
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

# MLOps Prototyping 🔬

Create standardized, reproducible Jupyter notebooks.

## Features

### 1. Notebook Structure Check ✅

Validate notebook follows best practices:

```bash
./scripts/check-notebook.sh notebook.ipynb
```

Checks for:
- H1 title
- Imports section
- Config/Constants
- Data loading
- Pipeline usage

### 2. Template 📝

Use this structure:

1. **Title & Purpose**
2. **Imports** (standard → third-party → local)
3. **Configs** (all constants at top)
4. **Datasets** (load, validate, split)
5. **Analysis** (EDA)
6. **Modeling** (use `sklearn.pipeline.Pipeline`)
7. **Evaluations** (metrics on test data)

## Quick Start

```bash
# Check your notebook
./scripts/check-notebook.sh my-notebook.ipynb

# Follow structure in notebook
# Use Pipeline for all transforms
# Set RANDOM_STATE everywhere
```

## Key Rules

✅ **DO:**
- Put all params in Config section
- Use `sklearn.pipeline.Pipeline`
- Split data BEFORE any transforms
- Set `random_state` everywhere

❌ **DON'T:**
- Magic numbers in code
- Manual transforms (use Pipeline)
- Fit on full dataset (data leakage)

## Author

Converted from [MLOps Coding Course](https://github.com/MLOps-Courses/mlops-coding-skills)

## Changelog

### v1.0.0 (2026-02-18)
- Initial OpenClaw conversion
- Added notebook checker

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