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jupyter-notebook

Scaffold and edit Jupyter notebooks for experiments and tutorials using bundled templates.

Updated Jul 3, 2026
Search Tags:#python#scaffold#tutorial#jupyter#experiment#notebook#ipynb
One-click install
npx skills add https://github.com/CHENHUI-X/toolbox --skill jupyter-notebook-chenhui-x
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-notebook
Source: https://github.com/CHENHUI-X/toolbox/tree/main/codex-skill/codex/jupyter-notebook
Command: npx skills add https://github.com/CHENHUI-X/toolbox --skill jupyter-notebook-chenhui-x

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Hand-authoring .ipynb files means writing fragile raw JSON, and ad-hoc notebooks often lack reproducible structure. This Skill generates clean, well-structured notebooks from templates so experiments and tutorials stay consistent and runnable. ## Core Features & Use Cases - Template-Based Scaffolding: Generate experiment or tutorial notebooks via the new_notebook.py helper script without touching raw notebook JSON. - Structured Patterns: Apply proven layouts for exploratory analysis (hypotheses, baselines, metrics) and teaching walkthroughs (prerequisites, exercises, pitfalls). - Safe Editing Guidance: Follow references on notebook JSON structure and a quality checklist when refactoring existing notebooks. - Use Case: A data scientist needs to compare prompt variants; run the helper script with --kind experiment to get a reproducible notebook skeleton, then fill in small runnable cells. ## Quick Start Ask the assistant to create a new experiment notebook titled "Compare prompt variants" using the jupyter-notebook scaffolding script.

Frequently Asked Questions about jupyter-notebook

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a new Jupyter notebook from a template?▼

Run the new_notebook.py script with --kind experiment or tutorial, a --title, and an optional --out path. It loads the bundled .ipynb template, updates the title cell, and writes a clean notebook without hand-editing JSON.

How to structure a Jupyter notebook for data experiments?▼

Start with the objective and success criteria, set seeds and configuration early, state hypotheses and metrics before coding, run a minimal baseline first, then record results and next steps in markdown near the relevant code cells.

Does the notebook scaffolding script require extra dependencies?▼

No, new_notebook.py uses only the Python standard library. Installing jupyterlab and ipykernel is optional and only needed if you want to execute the notebook locally.

Can I safely edit existing .ipynb files programmatically?▼

Yes, but preserve nbformat fields, keep cells ordered, set execution_count to null and outputs to empty lists when scaffolding. Prefer targeted edits over full rewrites and review the notebook-structure reference first.

What is the difference between experiment and tutorial notebook templates?▼

Experiment templates focus on hypotheses, baselines, and metrics for exploratory work. Tutorial templates include audience, prerequisites, step-by-step flow, exercises, and common pitfalls for teaching-oriented content.

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