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)

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

Lock the intent.

Identify the notebook kind:

experiment

or

tutorial

.

Capture the objective, audience, and what "done" looks like.

Scaffold from the template.

Use the helper script to avoid hand-authoring raw notebook JSON.

uv run

--python

3.12

python

"

$JUPYTER_NOTEBOOK_CLI

"

\

--kind

experiment

\

--title

"Compare prompt variants"

\

--out

output/jupyter-notebook/compare-prompt-variants.ipynb

uv run

--python

3.12

python

"

$JUPYTER_NOTEBOOK_CLI

"

\

--kind

tutorial

\

--title

"Intro to embeddings"

\

--out

output/jupyter-notebook/intro-to-embeddings.ipynb

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.

Apply the right pattern.

For experiments, follow

references/experiment-patterns.md

.

For tutorials, follow

references/tutorial-patterns.md

.

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.

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:

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.