jupyter-notebook
by openaiThe jupyter-notebook skill helps you create, refactor, and structure .ipynb notebooks for experiments, tutorials, and data analysis. It uses bundled templates and the new_notebook.py helper to produce clean, reproducible notebooks with clear sections, runnable cells, and fewer JSON mistakes.
This skill scores 78/100, which means it is a solid directory listing for users who want an agent to create, scaffold, or refactor Jupyter notebooks with less guesswork than a generic prompt. The repository gives enough workflow detail, templates, and helper-script guidance to make installation worthwhile, though it is narrower in scope than a broader notebook automation toolkit.
- Clear triggerability for creating, converting, and refactoring .ipynb notebooks, including a stated choice between experiment and tutorial modes.
- Good operational structure: decision tree, workflow steps, notebook structure rules, and quality checklist reduce ambiguity for agents.
- Useful support files and templates, including a helper script plus experiment and tutorial references/templates for reproducible notebook creation.
- No install command in SKILL.md, so users must infer setup from the path/export instructions.
- Focused on notebooks for experiments and tutorials; it is not a general data science or code-generation skill.
Overview of jupyter-notebook skill
The jupyter-notebook skill helps you create, refactor, and structure .ipynb files for experiments, exploratory analysis, and tutorials. It is most useful when you want a notebook that is clean enough to rerun, skim, and share—not just something that “works on my machine.”
Best fit readers are people building notebooks for data analysis, demos, or teaching material, especially when the output needs a clear narrative and fewer JSON mistakes. The skill is less about generic prompt help and more about producing a usable notebook artifact with the right scaffold, sectioning, and execution habits.
What this skill is good for
Use the jupyter-notebook skill when you need a fresh notebook, want to convert rough notes into a structured notebook, or need to improve an existing notebook that has become hard to follow. It is especially relevant for jupyter-notebook for Data Analysis workflows where reproducibility and readability matter together.
What makes it different
The skill leans on bundled templates and a helper script rather than hand-editing raw notebook JSON. That matters because notebooks are stateful JSON documents: structure, cell order, and execution metadata can easily become messy. The skill also separates two common modes—experiment and tutorial—so the notebook shape matches the job.
When it is the right choice
Choose jupyter-notebook if your goal is to ship a notebook that someone else can run or review. If you only need a quick answer, a plain prompt may be enough. If you need a reproducible notebook with sections, reusable structure, and clearer intent, this skill is the better starting point.
How to Use jupyter-notebook skill
Install and locate the skill files
Install with:
npx skills add openai/skills --skill jupyter-notebook
After install, the skill path is typically set under your skills directory, and the repo’s helper script is available at scripts/new_notebook.py. Start by reading SKILL.md, then review references/notebook-structure.md, references/experiment-patterns.md, references/tutorial-patterns.md, and references/quality-checklist.md to understand the workflow before you prompt.
Give the skill a notebook-shaped brief
Strong inputs describe the notebook type, audience, goal, and expected output. For example, instead of “make a notebook about pandas,” say: “Create an experiment notebook for analyzing monthly revenue trends from a CSV, with one baseline chart, a short metric table, and a final notes section.” For tutorials, specify the learner level and outcome: “Create a tutorial notebook for beginners that explains joins with a tiny dataset and one exercise.”
Follow the template-first workflow
The practical jupyter-notebook usage flow is: identify whether the task is experiment or tutorial, scaffold from the bundled template, then edit sections to match the task. Use the helper script new_notebook.py to generate a clean starter notebook instead of building JSON by hand. Preserve notebook structure, keep early cells self-contained, and avoid hidden dependencies on earlier runs.
Read the right repository files first
For install decisions, the most useful files are not just the main skill doc. Check assets/experiment-template.ipynb and assets/tutorial-template.ipynb to see the expected notebook shape, then use the references to understand how cells, narrative, and quality checks are organized. If you want the skill to produce consistent output, mirror those patterns in your prompt rather than asking for a vague “well-structured notebook.”
jupyter-notebook skill FAQ
Is jupyter-notebook only for data analysis?
No. jupyter-notebook is strong for jupyter-notebook for Data Analysis, but it also fits tutorials, explorations, and notebook-based documentation. If the deliverable should be a runnable .ipynb with clear sections, it is a good match.
Do I need to be an expert in Jupyter to use it?
No. The skill is beginner-friendly if you can describe your goal clearly. What matters most is telling it whether you need an experiment or tutorial, what data or topic you have, and how detailed the notebook should be.
Why not just use a normal prompt?
A normal prompt can draft content, but the jupyter-notebook skill adds structure discipline: notebook type selection, template-based scaffolding, and guidance for reproducible cells. That reduces the chance of malformed notebooks, missing setup cells, or unclear narrative flow.
When should I not use it?
Do not use jupyter-notebook if you need a one-off explanation, a plain script, or a notebook-free report. It is also not ideal if you cannot run or validate notebook cells at all, because the quality checklist assumes top-to-bottom execution when possible.
How to Improve jupyter-notebook skill
Give better constraints, not more noise
The best jupyter-notebook install results come from precise inputs: notebook kind, dataset shape, audience, and success criteria. Good: “Tutorial for analysts who know SQL but not Jupyter; explain groupby with one dataset and one checkpoint exercise.” Weak: “Make it educational.” The more specific your target reader and output format, the better the notebook structure will be.
Specify the first-run shape of the notebook
Tell the skill what the notebook must contain in order: imports, setup, baseline analysis, interpretation, and wrap-up. For experiments, include the question and key metric. For tutorials, include the lesson goal, prerequisites, and one exercise. This improves jupyter-notebook usage because the notebook is easier to run and review from the first pass.
Watch for common failure modes
The most common issues are overlong outputs, hidden state from prior runs, and notebooks that explain too much without enough runnable structure. If the first output is weak, ask for a tighter template, smaller code cells, and a clearer distinction between markdown explanation and executable cells. If you are refactoring an existing notebook, explicitly say what to preserve and what to clean up.
Iterate using reviewable checkpoints
After the first version, improve one thing at a time: notebook order, cell clarity, reproducibility, or audience fit. Ask for a shorter baseline, a cleaner narrative, or a stronger ending summary depending on what blocked adoption. This is the fastest way to turn a workable jupyter-notebook guide into a notebook people actually reuse.