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Data Jupyter Python

Checked
mindrally·vlatest·data·Apache-2.0

Helps with data, Python as part of developing backend services and APIs workflows.

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 mindrally/skills --skill data-jupyter-python -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

mindrally

Taken from the source listing. We haven't verified who controls this account.

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 mindrally/skills at 7682ca7771.

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

Commit
7682ca7771
Manifest
data-jupyter-python/SKILL.md
Scanned
1 file, 66 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

This skill enables AI agents to execute Python code within a Jupyter Notebook environment, allowing for interactive data analysis and visualization. It provides a sandboxed execution context where agents can run scripts, manipulate datasets, and generate dynamic outputs directly from natural language requests.

When to use it

  • Analyzing complex datasets to derive insights or identify trends.
  • Generating custom visualizations like charts and graphs to present findings.
  • Running statistical calculations or machine learning models on the fly.
  • Prototyping data processing logic before integrating it into production code.

Key capabilities

  • Interactive Python execution within a Jupyter interface.
  • Support for standard data science libraries (e.g., pandas, matplotlib).
  • Dynamic rendering of output cells including tables and plots.
  • Stateful session management for multi-step analysis workflows.

Example prompts

  • "Load the sales dataset from the attached file and calculate the total revenue by region."
  • "Create a line chart showing monthly temperature trends over the last five years."
  • "Run a correlation matrix on the customer survey data and display the results as a heatmap."

Tips & gotchas

Ensure your AI agent has access to necessary external libraries if they are not pre-installed in the environment. For large datasets, consider chunking the data or using efficient memory management techniques to prevent execution timeouts.