Is this skill only for Python-based notebooks?

While most common in Python data science, these referencing patterns apply to any Jupyter-based environment regardless of the programming language used.

What does this skill change in Claude's behavior?

It prompts Claude to identify cells by their actual content or purpose, such as 'the normalization cell' or 'the cell starting with sc.read_h5ad,' rather than a generic index.

Why should I avoid using cell numbers when talking to Claude?

Cell numbers are often not visible in the notebook interface and change whenever cells are added or deleted, which leads to confusion and errors.

How does this help with debugging?

By pinpointing cells by their specific function or variables, you and Claude can ensure you are both looking at the exact same logic block, even if the notebook structure has changed.

Does this work with the VS Code Jupyter extension?

Yes, it improves clarity across any platform or IDE that utilizes the Jupyter (.ipynb) notebook format.

Jupyter Notebook Cell Referencing

Name: Jupyter Notebook Cell Referencing
Author: smith6jt-cop

bysmith6jt-cop

データサイエンスとML

Standardizes how AI identifies and communicates about specific Jupyter notebook cells using stable, identifiable characteristics instead of volatile cell numbers.

概要

This skill optimizes Claude's interaction with Jupyter notebooks by replacing unreliable cell numbering with context-aware identification. Because cell numbers are often hidden or subject to change as notebooks evolve, this skill directs Claude to reference cells by their section headers, specific code snippets, functional purpose, or defined variables. This ensures clear, unambiguous communication during complex data science workflows, debugging sessions, and collaborative analysis projects.

主な機能

Tracking cells through primary variable definitions
0 GitHub stars
Contextual identification using markdown section headers
Referencing via specific code snippets and function definitions
Reduction of errors caused by shifting cell indices
Identification based on functional purpose like plotting or normalization

ユースケース

Refactoring data visualization cells within a shared notebook
Debugging specific segments of a large machine learning pipeline
Providing clear instructions for updating data loading and preprocessing steps

概要

主な機能

Tracking cells through primary variable definitions
0 GitHub stars
Contextual identification using markdown section headers
Referencing via specific code snippets and function definitions
Reduction of errors caused by shifting cell indices
Identification based on functional purpose like plotting or normalization

ユースケース

Refactoring data visualization cells within a shared notebook
Debugging specific segments of a large machine learning pipeline
Providing clear instructions for updating data loading and preprocessing steps