# Jupyter Notebooks

Create, edit, or validate reproducible SQL or Python notebooks.

- Repository: https://github.com/XiaomiMiMo/MiMo-Code (13741 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add XiaomiMiMo/MiMo-Code --skill jupyter-notebooks -g -a claude-code -y`
Codex: `npx skills add XiaomiMiMo/MiMo-Code --skill jupyter-notebooks -g -a codex -y`
Cursor: `npx skills add XiaomiMiMo/MiMo-Code --skill jupyter-notebooks -g -a cursor -y`
Gemini CLI: `npx skills add XiaomiMiMo/MiMo-Code --skill jupyter-notebooks -g -a gemini-cli -y`

## Description

Create, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Related Skills

Use $validate-data when notebook results support a recommendation, shared claim, or decision.

# Jupyter Notebooks

Create clean, reproducible Jupyter notebooks that are easy to skim, rerun, and handoff. Treat the notebook as a reader-facing analysis artifact, not a scratchpad dump. Notebook work is not complete until the notebook executes successfully top-to-bottom, or the execution gap is called out with the exact validation steps needed to reproduce it.

## Workflow

1. Lock the notebook mode and scope.

   Decide whether the notebook is an analysis report, experiment log, diagnostic notebook, data-quality check, market-sizing calculation, model exploration,
   tutorial, or companion artifact for a report. Identify the reader, decision,
   expected handoff, required inputs, and whether the task calls for a new notebook or targeted edits to an existing one.

2. Inspect or scaffold with notebook-safe tooling.

   Prefer JupyterLab, `nbformat`, `nbclient`, or an existing scaffold utility over hand-editing raw JSON. When editing an existing notebook, preserve its intent and minimize JSON churn. Avoid reordering cells unless it clearly improves the top-to-bottom story. If raw JSON editing is unavoidable,
   validate the notebook structure before finishing.

3. Structure the notebook for the chosen mode.

   For analytical notebooks, default to:

   1. `## tl;dr`
   2. `## Context & Methods`
   3. `## Data`
   4. `## Results`
   5. `## Takeaways`

   Write `tl;dr` and takeaways after reviewing executed outputs. Use concrete observed values, visible patterns, rows, or charts, not assumptions. Include a `### Key Assumptions` subsection in `Context & Methods` when assumptions affect correctness.

   For tutorials or walkthroughs, adapt the same discipline to a teaching flow:

   1. `## Goal`
   2. `## Setup`
   3. `## Steps`
   4. `## Checks`
   5. `## Next Steps`

4. Build a clear data and computation path.

The rest: https://github.com/XiaomiMiMo/MiMo-Code/blob/HEAD/packages/cli/src/skill/builtin/.bundle/data-analytics/workflows/jupyter-notebooks/SKILL.md