home / skills / derklinke / codex-config / jupyter-notebook
This skill creates clean, reproducible Jupyter notebooks from templates or sketches, organizing experiments or tutorials with a structured, runnable workflow.
npx playbooks add skill derklinke/codex-config --skill jupyter-notebookReview the files below or copy the command above to add this skill to your agents.
---
name: "jupyter-notebook"
description: "Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook."
---
# Jupyter Notebook Skill
Create clean, reproducible Jupyter notebooks for two primary modes:
- Experiments and exploratory analysis
- Tutorials and teaching-oriented walkthroughs
Prefer the bundled templates and the helper script for consistent structure and fewer JSON mistakes.
## When to use
- Create a new `.ipynb` notebook from scratch.
- Convert rough notes or scripts into a structured notebook.
- Refactor an existing notebook to be more reproducible and skimmable.
- Build experiments or tutorials that will be read or re-run by other people.
## Decision tree
- If the request is exploratory, analytical, or hypothesis-driven, choose `experiment`.
- If the request is instructional, step-by-step, or audience-specific, choose `tutorial`.
- If editing an existing notebook, treat it as a refactor: preserve intent and improve structure.
## Skill path (set once)
```bash
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export JUPYTER_NOTEBOOK_CLI="$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py"
```
User-scoped skills install under `$CODEX_HOME/skills` (default: `~/.codex/skills`).
## Workflow
1. Lock the intent.
Identify the notebook kind: `experiment` or `tutorial`.
Capture the objective, audience, and what "done" looks like.
2. Scaffold from the template.
Use the helper script to avoid hand-authoring raw notebook JSON.
```bash
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
--kind experiment \
--title "Compare prompt variants" \
--out output/jupyter-notebook/compare-prompt-variants.ipynb
```
```bash
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
--kind tutorial \
--title "Intro to embeddings" \
--out output/jupyter-notebook/intro-to-embeddings.ipynb
```
3. Fill the notebook with small, runnable steps.
Keep each code cell focused on one step.
Add short markdown cells that explain the purpose and expected result.
Avoid large, noisy outputs when a short summary works.
4. Apply the right pattern.
For experiments, follow `references/experiment-patterns.md`.
For tutorials, follow `references/tutorial-patterns.md`.
5. Edit safely when working with existing notebooks.
Preserve the notebook structure; avoid reordering cells unless it improves the top-to-bottom story.
Prefer targeted edits over full rewrites.
If you must edit raw JSON, review `references/notebook-structure.md` first.
6. Validate the result.
Run the notebook top-to-bottom when the environment allows.
If execution is not possible, say so explicitly and call out how to validate locally.
Use the final pass checklist in `references/quality-checklist.md`.
## Templates and helper script
- Templates live in `assets/experiment-template.ipynb` and `assets/tutorial-template.ipynb`.
- The helper script loads a template, updates the title cell, and writes a notebook.
Script path:
- `$JUPYTER_NOTEBOOK_CLI` (installed default: `$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py`)
## Temp and output conventions
- Use `tmp/jupyter-notebook/` for intermediate files; delete when done.
- Write final artifacts under `output/jupyter-notebook/` when working in this repo.
- Use stable, descriptive filenames (for example, `ablation-temperature.ipynb`).
## Dependencies (install only when needed)
Prefer `uv` for dependency management.
Optional Python packages for local notebook execution:
```bash
uv pip install jupyterlab ipykernel
```
The bundled scaffold script uses only the Python standard library and does not require extra dependencies.
## Environment
No required environment variables.
## Reference map
- `references/experiment-patterns.md`: experiment structure and heuristics.
- `references/tutorial-patterns.md`: tutorial structure and teaching flow.
- `references/notebook-structure.md`: notebook JSON shape and safe editing rules.
- `references/quality-checklist.md`: final validation checklist.
This skill creates, scaffolds, and safely edits Jupyter notebooks (.ipynb) for experiments and tutorials. It prefers bundled templates and a helper script to generate clean starting notebooks and avoid manual JSON edits. The workflow focuses on small runnable steps, clear markdown, and validation to produce reproducible artifacts.
The skill inspects the user's intent to choose a mode: experiment or tutorial, then uses the bundled templates and the helper script new_notebook.py to generate a scaffolded .ipynb file. For edits, it treats existing notebooks as refactors—preserving intent and structure—and recommends targeted changes over raw JSON rewrites. It also provides conventions for temp/output locations and a validation checklist to confirm quality.
Do I need extra Python packages to run the scaffold script?
No. The helper script uses only the Python standard library. Optional packages like jupyterlab and ipykernel are only needed to run notebooks locally.
How do I choose between experiment and tutorial modes?
If the goal is analysis, hypothesis testing, or exploratory work, choose experiment. If the goal is instruction, step-by-step learning, or audience-specific teaching, choose tutorial.