A tool for creating Jupyter notebooks, which are documents that combine runnable code, notes, and results. It asks about the notebook's purpose, author, hardware, libraries, and long-running work before setting it up.

Use it to start a new data-science or machine-learning notebook with the required structure, configuration, visual style, and optional progress bars.

It removes the need to remember the project's required notebook layout, styling, imports, and progress indicators. It also helps keep notebook numbering consistent with existing files.

notebook — Skill for Claude Code by stellarshenson/claude-code-plugins

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add stellarshenson/claude-code-plugins --skill notebook

Clone the repo

git clone --depth 1 https://github.com/stellarshenson/claude-code-plugins

Made for: Claude Code.

Or install datascience, the plugin that ships this one along with the rest of its 20 skills, 15 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/notebook/github.svg)](https://agentmods.dev/skills/stellarshenson/claude-code-plugins/notebook)

Your own site

<a href="https://agentmods.dev/skills/stellarshenson/claude-code-plugins/notebook"><img src="https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/notebook/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/stellarshenson/claude-code-plugins/notebook"><img src="https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/notebook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 930 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00018	$0.00930
Opus 5.5	$0.00007	$0.00372
Sonnet 5.5	$0.00004	$0.00186
Haiku 4.5	$0.00002	$0.00093

Measured 1mo ago against content hash f7cc50745c24, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

notebook scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/datascience/skills/notebook/SKILL.md · 50 lines

How it starts

The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Create New Notebook

Scaffold a properly structured notebook. Uses skills for standards - do NOT duplicate their rules here.

Skills to apply

datascience:notebook-standards - section order, GPU selection, imports, config cell; its references/rich-output.md (colour palette, print patterns) and references/equations.md (unicode inline + display math)
datascience:progressbars - progress bar style and patterns (if long-running ops)

Read these skills before generating the notebook. They are the source of truth for structure, colors, and patterns.

Steps

ASK the user:
- Purpose (what the notebook does - used for header narrative)
- Author initials (default: kj)
- GPU needed? (yes/no) - if yes, GPU selection: pin a specific GPU by UUID (default, reproducible) or auto-pick the freest GPU at runtime? See notebook-standards/references/gpu-setup.md
- Libraries needed (torch, polars, sklearn, transformers, etc.)
- Long-running operations? (yes/no - if yes, ask classic tqdm or modern rich per progressbars skill)
Determine next notebook number by scanning existing NN-*.ipynb or NN-*.py files.
Check pyproject.toml - ensure rich in dependencies. If progress bars needed, ensure tqdm or rich per chosen style. Add if missing.
Create <NN>-<initials>-<description>.ipynb (default) or .py if user prefers.
Header: brief narrative paragraph (purpose, method, rationale - 2-4 sentences of flowing prose, not bullets). Approach section with numbered phases explaining "what AND why". Output section listing specific artifacts.
Structure: follow notebook-standards skill exactly - GPU first, imports grouped with autoreload, reproducibility seeds, config cell with sectioned Rich render. Section overview MANDATORY - every ## Section Name header gets a 1-2 sentence overview (or 3-5 bullets when listy) directly below, BEFORE the first code cell.
Configuration cell: hyperparameters with inline comments + sectioned Rich render at the END of the same cell. When GPU is enabled, the render MUST include [bold]Device[/bold] sub-section showing torch.cuda.get_device_name(0) so the resolved GPU is visible in the output. See notebook-standards/SKILL.md Configuration template + notebook-standards/references/gpu-setup.md section 5.

Read the full file on GitHub · 50 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 50 lines · 18 tokens per session scan A f7cc50745c24

Subscribe to this mod's changes

notebook is a skill published in the GitHub repository stellarshenson/claude-code-plugins (4 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 930 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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notebook