# Data Notebook Refiner

> Standards for high-quality Jupyter Notebooks. Focuses on readability, reproducibility, and educational value.

- Skill: `majiayu000/data-notebook-refiner-2` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/data-notebook-refiner-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/data-notebook-refiner-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/majiayu000/data-notebook-refiner-2

---


# Notebook Refiner Standards

## Purpose
To ensure Jupyter Notebooks are not just "functioning code dumps" but **educational learning materials** and **reproducible assets**.

## Core Philosophy: "Readability & Reproducibility"
A notebook is a document meant to be read by humans, not just a script for machines.

## Refactoring Standards

### 1. Structure (Flow)
- **Imports**: All imports must be in the first cell.
- **Logical Flow**: Data Load → EDA → Preprocessing → Modeling → Evaluation.
- **Kernel Check**: Must specify required environment (e.g., `venv`, python version).

### 2. Code Quality (Refactor)
- **Naming**: Use descriptive names (`titanic_df`) over generic ones (`df`). Follow conventions (`X`, `y`, `model`).
- **Granularity**: One logical step per cell. Don't mix loading and training in one massive cell.
- **Output**: Suppress verbose logs (e.g., strict `fit()` output).

### 3. Documentation (Context)
**"Explain Why, Not What"**
- **Bad**: "This code splits the data." (Redundant)
- **Good**: "We use `stratify=y` to maintain class balance in the test set." (Insightful)
- **Headers**: Use clear Markdown headers (`#`, `##`) to navigate structure.

### 4. Verification (Reproducibility)
- **Restart & Run All**: The notebook must run from top to bottom without error after a kernel restart.
- **Visuals**: All plots must have Titles, Axis Labels, and Legends.