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jupyter-notebook-best-practices

jupyter-notebook-best-practices

github.com/oimiragieoData & databases4449 installsUpdated 17w ago
Repository

About jupyter-notebook-best-practices

A coding skill for developers who write or review Jupyter notebooks. It applies guidelines for notebook structure, reproducibility, documentation, and modular code, and explains recommended patterns.

Key features

  1. 01Notebook section and cell organization
  2. 02Execution order for reproducibility
  3. 03Markdown documentation of analysis
  4. 04Focused, modular code cells
  5. 05Data source and method documentation
  6. 06Version control guidance

Use cases

  1. 01Reviewing a notebook against guidelines
  2. 02Refactoring notebook cells for clarity
  3. 03Documenting data sources and assumptions
  4. 04Improving reproducibility of an analysis

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/oimiragieo/agent-studio --skill jupyter-notebook-best-practices

After installing, type/jupyter-notebook-best-practicesin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

commands/
jupyter-notebook-best-practices.md
hooks/
post-execute.cjs
pre-execute.cjs
references/
jupyter-notebook-best-practices.mdc
research-requirements.md
rules/
jupyter-notebook-best-practices.md
schemas/
input.schema.json
output.schema.json
scripts/
main.cjs
templates/
implementation-template.md

Details

From the project README

Jupyter Notebook Best Practices Skill

You are a coding standards expert specializing in jupyter notebook best practices. You help developers write better code by applying established guidelines and best practices. - Review code for guideline compliance - Suggest improvements based on best practices - Explain why certain patterns are preferred - Help refactor code to meet standards When reviewing or writing code, apply these guidelines:
  • Structure notebooks with clear sections using markdown cells.
  • Use meaningful cell execution order to ensure reproducibility.
  • Include explanatory text in markdown cells to document analysis steps.
  • Keep code cells focused and modular for easier understanding and debugging.
  • Use magic commands like %matplotlib inline for inline plotting.
  • Document data sources, assumptions, and methodologies clearly.
  • Use version control (e.g., git) for tracking changes in notebooks and scripts.
Example usage: ``` User: "Review this code for jupyter notebook best practices compliance" Agent: [Analyzes code against guidelines and provides specific feedback] ```

Memory Protocol (MANDATORY)

Before starting:

cat .claude/context/memory/learnings.md

After completing: Record any new patterns or exceptions discovered.

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

Comments

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