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Agent skill

Jupyter Notebook Builder

by Kilo-Org in Kilo-Org/kilo-marketplace

Creates, inspects, edits and runs Jupyter notebooks, scaffolding experiment or tutorial notebooks from templates and preferring a Jupyter MCP server over raw JSON edits.

MITAuto-check passedData & Analytics

Install Jupyter Notebook Builder

skills CLI
$ npx skills add Kilo-Org/kilo-marketplace --skill jupyter-notebook -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Kilo-Org/kilo-marketplace jupyter-notebook --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/jupyter-notebook .claude/skills/jupyter-notebook && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
jupyter-notebook
GitHub stars
190
Token cost
~1.3k tokens
SKILL.md length
530 words
Files
10 (incl. scripts, references, assets)
Skills in repo
86
Repo updated
First seen
Licence
MIT

At a glance

Creates, inspects, edits and runs Jupyter notebooks, scaffolding experiment or tutorial notebooks from templates and preferring a Jupyter MCP server over raw JSON edits.

  • Creating a new notebook for an experiment or exploratory analysis
  • SKILL.md covers When to use, Decision tree, Choose the notebook tooling and Workflow, plus 5 more sections
  • Runs Python scripts from its folder; calls python3
  • Writing a tutorial notebook that other people will re-run

What it does

The skill covers working with .ipynb files in two modes: experiments and exploratory analysis, and tutorials or teaching walkthroughs. A decision tree picks the mode from the request, and edits to an existing notebook are treated as refactors that keep the original intent while improving structure. New notebooks are scaffolded with scripts/new_notebook.py and the bundled experiment and tutorial templates, so the agent does not hand-write notebook JSON.

Before inspecting, editing or running a notebook, the agent checks for Jupyter MCP tools and uses them when available. If they are missing or an operation fails, it tells you it is switching to manual editing and follows references/manual-editing.md, and it never mixes the two approaches for one change. Notebooks are built from small runnable steps with short Markdown explanations, and reference files cover notebook structure, experiment and tutorial patterns and a quality checklist.

When your agent uses it

  • Creating a new notebook for an experiment or exploratory analysis
  • Writing a tutorial notebook that other people will re-run
  • Turning rough notes or a script into a structured notebook
  • Refactoring a messy notebook to be reproducible and easier to skim

Example prompts

  • “Create an experiment notebook comparing three prompt variants, with a results table at the end.”
  • “Turn this analysis script into a tutorial notebook with short explanations between cells.”
  • “Re-run analysis.ipynb, fix the kernel error and refresh the saved outputs.”

Requirements

  • Python 3 to run the helper script
  • A Jupyter MCP server (preferred) or the ability to edit notebook JSON by hand

What it can do on your machine

Read from SKILL.md and the folder at commit ff51758. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Jupyter Notebook Builder loads about 1.3k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 52 tokens; SKILL.md has 530 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Kilo-Org/kilo-marketplace at commit ff51758, republished under its MIT licence (© Kilo-Org). 530 words, ~1,259 tokens.

Download SKILL.mdSave it as .claude/skills/jupyter-notebook/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
jupyter-notebook
description
Use whenever the user works with Jupyter notebooks (`.ipynb`), including creating, inspecting, editing, executing, or visualizing notebook content for experiments, explorations, or tutorials.
license
MIT
metadata.category
development
metadata.author
Kilo

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.
  • Inspect notebook cells, outputs, metadata, dependencies, or execution state.
  • Add, remove, reorder, or edit code and Markdown cells.
  • Execute notebooks, refresh saved outputs, or troubleshoot kernel failures.
  • Create or improve notebook tables, charts, and other visualizations.
  • 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.

Choose the notebook tooling

Before inspecting, editing, or executing a notebook, check whether suitable Jupyter MCP tools are available.

  • Prefer a connected Jupyter MCP server for notebook operations.
  • If Jupyter MCP is unavailable or an MCP operation fails, explicitly tell the user that you are switching to manual notebook editing.
  • Follow references/manual-editing.md before editing raw notebook JSON.
  • Do not use MCP and manual JSON editing simultaneously for the same change.

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. Resolve scripts/new_notebook.py against the base directory supplied when this skill is loaded; do not assume the skill is installed under the project's .kilo directory.

bash
python3 "<SKILL_BASE_DIR>/scripts/new_notebook.py" \
  --kind experiment \
  --title "Compare prompt variants" \
  --out "compare-prompt-variants.ipynb"
bash
python3 "<SKILL_BASE_DIR>/scripts/new_notebook.py" \
  --kind tutorial \
  --title "Intro to embeddings" \
  --out "intro-to-embeddings.ipynb"

  1. 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.

  2. Apply the right pattern. For experiments, follow references/experiment-patterns.md. For tutorials, follow references/tutorial-patterns.md.

  3. 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, follow references/manual-editing.md.

  4. 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.

Show full SKILL.md (132 more words)Show less

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.

Temp and output conventions

  • Use the system temporary directory for intermediate files and delete them when done.
  • Save the final notebook in the user-requested location, or in the current project directory when no location is specified.
  • Use stable, descriptive filenames (for example, ablation-temperature.ipynb).

Dependencies (install only when needed)

Optional Python packages for local notebook execution:

bash
python3 -m 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.
  • references/manual-editing.md: manual editing and execution fallback when Jupyter MCP is unavailable.
  • references/quality-checklist.md: final validation checklist.

© Kilo-Org, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (scripts, references, assets) in skills/jupyter-notebook of Kilo-Org/kilo-marketplace.

  • SKILL.md
  • LICENSE
  • assets/experiment-template.ipynb
  • assets/tutorial-template.ipynb
  • references/experiment-patterns.md
  • references/manual-editing.md
  • references/notebook-structure.md
  • references/quality-checklist.md
  • references/tutorial-patterns.md
  • scripts/new_notebook.py

Open the folder on GitHubat commit ff51758

Compare with similar skills

Jupyter Notebook Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Jupyter Notebook Builder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jupyter Notebook Builder this skillKilo-Org/kilo-marketplace190—~1.3kAutomated safety check: PassMIT
Save Research Notebooknapjon/krisk117—~702Automated safety check: PassBSD-3-Clause
Replnteract/nteract179—~1.4kAutomated safety check: PassBSD-3-Clause
Analytics Data AnalysisMindrally/skills271—~1.6kAutomated safety check: PassApache-2.0
Export ML Notebookprobabl-ai/skills138—~1.7kAutomated safety check: PassBSD-3-Clause
Jupyter Live KernelAlexAI-MCP/hermes-CCC135—~977Automated safety check: PassMIT

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Works with

Categories

Questions about Jupyter Notebook Builder

What does Jupyter Notebook Builder do?

Creates, inspects, edits and runs Jupyter notebooks, scaffolding experiment or tutorial notebooks from templates and preferring a Jupyter MCP server over raw JSON edits. ipynb files in two modes: experiments and exploratory analysis, and tutorials or teaching walkthroughs. A decision tree picks the mode from the request, and edits to an existing notebook are treated as refactors that keep the original intent while improving structure.

When should I use Jupyter Notebook Builder?

Jupyter Notebook Builder fits situations like: creating a new notebook for an experiment or exploratory analysis; writing a tutorial notebook that other people will re-run; turning rough notes or a script into a structured notebook; refactoring a messy notebook to be reproducible and easier to skim.

How do I install Jupyter Notebook Builder in Claude Code?

Run `npx skills add Kilo-Org/kilo-marketplace --skill jupyter-notebook -a claude-code`. Or copy the skill folder (skills/jupyter-notebook in Kilo-Org/kilo-marketplace) into .claude/skills/jupyter-notebook in your project. Claude Code loads it when a task matches its description.

How do I install Jupyter Notebook Builder in Codex?

Run `npx skills add Kilo-Org/kilo-marketplace --skill jupyter-notebook -a codex`. Or copy the skill folder (skills/jupyter-notebook in Kilo-Org/kilo-marketplace) into .agents/skills/jupyter-notebook in your project. Codex loads it when a task matches its description.

Can I use Jupyter Notebook Builder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Kilo-Org/kilo-marketplace --skill jupyter-notebook -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jupyter-notebook, .gemini/skills/jupyter-notebook, .github/skills/jupyter-notebook and .opencode/skills/jupyter-notebook in your project.

What does Jupyter Notebook Builder need to run?

Going by SKILL.md and its folder, Jupyter Notebook Builder needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3 to run the helper script; A Jupyter MCP server (preferred) or the ability to edit notebook JSON by hand.

Does Jupyter Notebook Builder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Jupyter Notebook Builder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Jupyter Notebook Builder use?

Jupyter Notebook Builder is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Jupyter Notebook Builder use?

About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Jupyter Notebook Builder?

Skills that share tags, products or a category with Jupyter Notebook Builder: Save Research Notebook (napjon/krisk, 117 stars), Repl (nteract/nteract, 179 stars), Analytics Data Analysis (Mindrally/skills, 271 stars) and Export ML Notebook (probabl-ai/skills, 138 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jupyter Notebook Builder?

Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 190 GitHub stars. The repository holds 86 skills in this directory. The repository was last updated on September 28, 2026.

Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.