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Data Analysis Jupyter

Checked
mindrally·vlatest·data·Apache-2.0

Helps with data, analysis as part of data analysis and analytics workflows 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-analysis-jupyter -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-analysis-jupyter/SKILL.md
Scanned
1 file, 134 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

The data-analysis-jupyter skill enables AI agents to execute Python code within a Jupyter Notebook environment, facilitating interactive data exploration and visualization directly from natural language instructions. It bridges the gap between conversational queries and complex computational tasks by providing immediate access to standard data science libraries.

When to use it

  • Analyzing time-series datasets to identify trends or anomalies without writing manual scripts.
  • Generating dynamic charts and graphs to visualize correlations in customer behavior data.
  • Performing statistical summaries on large CSV files uploaded for processing.
  • Iteratively refining data cleaning pipelines through conversational feedback loops.

Key capabilities

  • Executes Python code cells within a Jupyter Notebook context.
  • Imports and utilizes standard data science libraries (e.g., pandas, numpy, matplotlib).
  • Renders interactive visualizations directly in the agent's output.
  • Processes structured data formats like CSVs and JSON files.

Example prompts

  • "Load this sales dataset and calculate the average revenue per month for the last year."
  • "Create a bar chart showing the distribution of product categories based on the uploaded file."
  • "Run a linear regression to predict future inventory needs using the historical data provided."

Tips & gotchas

Ensure your AI agent has permission to access external libraries or that necessary packages are pre-installed in the environment, as execution failures often stem from missing dependencies. Keep datasets reasonably sized for local processing to avoid timeout errors during code execution.