jupyter-notebook

Files (12)

• LICENSE.txt
• SKILL.md
• openai.yaml · agents/
• experiment-template.ipynb · assets/
• jupyter-small.svg · assets/
• jupyter.png · assets/
• tutorial-template.ipynb · assets/
• experiment-patterns.md · references/
• notebook-structure.md · references/
• quality-checklist.md · references/
• tutorial-patterns.md · references/
• new_notebook.py · scripts/

SKILL.md (first lines)

# 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 JUPYTER_NOTEBOOK_CLI="$PWD/skills/jupyter-notebook/scripts/new_notebook.py"
```

The skill lives in this workspace at `skills/jupyter-notebook/` and reaches
Freebuff through `_scripts/sync_skills.py --apply`; there is no per-user skills
install to maintain.

## 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 notebooks/compare-prompt-variants.ipynb
```

```bash
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
  --kind tutorial \
  --title "Intro to embeddings" \
  --out notebooks/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
- The notebook belongs to its project: `projects/<name>/notebooks/`.
- Anything the notebook produces — figures, csv, exports — goes to
  `projects/<name>/output/`, next to the work and visible in git.
- Intermediate scratch (`tmp/`) stays uncommitted and is deleted when done.
- 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.

From youlianvr/oper-share, CC-BY-4.0.

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