Openai Jupyter 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.

Scripts and references are located under {baseDir}/.

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.

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

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: {baseDir}/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.

When NOT to Use

<!-- TODO: review -->

Experiment Patterns

Use this structure for exploratory and experimental work:

• Title and objective: state the question and the success criteria.
• Setup and reproducibility: import only what you need, set a seed early, and keep configuration in one short cell.
• Plan: list hypotheses, sweeps, and metrics before running code.
• Minimal baseline: start with the smallest runnable example and confirm it runs end-to-end before adding complexity.
• Results and notes: summarize findings in markdown near the relevant code and record key metrics in a small dictionary or table-like structure.
• Next steps: decide whether to continue, pivot, or stop, and capture follow-up ideas as short bullets.

Notebook Structure

Jupyter notebooks are JSON documents with this high-level shape:

• nbformat and nbformat_minor
• metadata
• cells (a list of markdown and code cells)

When editing .ipynb files programmatically:

• Preserve nbformat and nbformat_minor from the template.
• Keep cells as an ordered list; do not reorder unless intentional.
• For code cells, set execution_count to null when unknown.
• For code cells, set outputs to an empty list when scaffolding.
• For markdown cells, keep cell_type="markdown" and metadata={}.

Prefer scaffolding from the bundled templates or new_notebook.py (for example, {baseDir}/scripts/new_notebook.py) instead of hand-authoring raw notebook JSON.

Quality Checklist

Before delivering a notebook:

• Run it top-to-bottom at least once (or as much as the environment allows).
• Ensure early cells set all required state; avoid hidden state from prior runs.
• Keep outputs tidy. Avoid giant outputs when a short summary works.
• Prefer small tables, key metrics, or short printouts.
• Keep the narrative skimmable. Use headings and short bullets, and avoid long paragraphs.
• Leave helpful TODOs only when necessary, and label them clearly.
• If execution is not possible, call out the risk and how to validate locally.

Tutorial Patterns

Use this structure for teaching and walkthroughs:

• Audience, prerequisites, and learning goals: say who it is for, list what they should already know, and state what they will be able to do by the end.
• Outline: provide a short numbered outline so readers can skim.
• Step-by-step flow: pair a short markdown explanation with a small code cell that runs on its own and a brief interpretation of the result.
• Exercises: include at least one exercise that reinforces the key concept and provide an answer scaffold in the next cell.
• Pitfalls and extensions: call out one common mistake and how to fix it, and suggest one optional extension for curious readers.