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Exploratory Data Analysis

Checked
jackspace·vlatest·data·MIT

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 jackspace/claudeskillz --skill exploratory-data-analysis -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

jackspace

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 jackspace/claudeskillz at aef4e299d1.

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

Commit
aef4e299d1
Manifest
skills/scientific-thinking-exploratory-data-analysis/SKILL.md
Scanned
2 files, 215 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 perform exploratory data analysis (EDA) on datasets, helping users understand underlying patterns and distributions. It automates the initial investigation phase of data science workflows to reveal insights before deeper modeling occurs.

When to use it

  • You need to quickly summarize the structure and content of a raw dataset.
  • You want to identify missing values, outliers, or unusual data distributions automatically.
  • You are preparing data for machine learning models and require statistical summaries.
  • You need to visualize correlations between variables without writing complex code.

Key capabilities

  • Automatically generates descriptive statistics (mean, median, mode, standard deviation).
  • Detects and reports missing data points within columns.
  • Identifies potential outliers based on statistical thresholds.
  • Creates visualizations to show data distributions and relationships.
  • Provides a high-level overview of dataset dimensions and types.

Example prompts

  • "Run an exploratory data analysis on this CSV file containing customer sales records."
  • "What are the key statistics and any missing values in this dataset?"
  • "Generate a summary report including outliers and correlations for the provided JSON data."

Tips & gotchas

Ensure your input data is clean or pre-formatted, as EDA tools may struggle with unstructured text files. This skill focuses on discovery; you will still need to manually decide which variables to include in final predictive models based on the generated insights.