Skip to content
TrustedSkillsbeta
Back to all skills

Jupyter Notebook

Official
openai·vlatest·data

This skill generates interactive Jupyter Notebook code for data analysis, exploration, and visualization – a powerful tool for experimentation & learning.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add openai/skills --skill jupyter-notebook -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

openai

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned openai/skills at 49f948faa9.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
49f948faa9
Manifest
skills/.curated/jupyter-notebook/SKILL.md
Scanned
8 files, 289 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The Jupyter Notebook skill enables AI agents to interact with and execute code in a Jupyter Notebook environment. This allows users to run Python scripts, analyze data, visualize results, and document their findings within an interactive notebook interface.

When to use it

  • When you need to perform data analysis or visualization using Python.
  • To test and debug code snippets in an isolated, interactive environment.
  • For educational purposes, such as teaching programming or data science concepts.
  • When sharing reproducible research or computational workflows with others.

Key capabilities

  • Execute Python code within a Jupyter Notebook interface.
  • Support for data visualization libraries like Matplotlib and Seaborn.
  • Integration with popular data science tools and packages.
  • Ability to document and present findings in an interactive format.

Example prompts

  • "Run this Python script to plot a histogram of the dataset."
  • "Create a Jupyter Notebook that calculates the mean and standard deviation of the given numbers."
  • "Execute the provided code snippet and return the output."

Tips & gotchas

  • Ensure your environment supports Jupyter Notebook execution, as some platforms may require additional setup.
  • Be mindful of resource limits when running computationally intensive tasks in a notebook.