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Exploratory Data Analysis

Checked
aj-geddes·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.

Installs under a different name. Its SKILL.md names it Exploratory Data Analysis, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/exploratory-data-analysis/SKILL.md
Scanned
3 files, 270 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, Exploratory Data Analysis (EDA), helps AI agents systematically examine datasets to understand their characteristics and assess data quality. It performs data profiling, distribution analysis, relationship discovery, anomaly detection, and evaluates completeness and consistency of data. The tool uses Python libraries like pandas, numpy, matplotlib, and seaborn for these analyses.

When to use it

  • Starting a new dataset analysis project.
  • Before building models to understand the underlying data.
  • When you need to identify potential data quality issues.
  • To generate hypotheses that can be tested with further investigation.
  • For communicating insights and findings about the data to stakeholders.

Key capabilities

  • Data Profiling: Calculates basic statistics and identifies data types within a dataset.
  • Distribution Analysis: Visualizes how variables are distributed using histograms and other plots.
  • Relationship Discovery: Identifies patterns and correlations between different variables.
  • Anomaly Detection: Uses boxplots to help find outliers in the data.
  • Data Quality Assessment: Evaluates missing values, duplicate entries, and overall consistency of the dataset.
  • Skewness and Kurtosis Analysis: Calculates these statistical measures for numerical columns.
  • Percentile Analysis: Determines specific percentile values within a column (e.g., 25th, 50th, 75th).

Example prompts

  • "Perform an exploratory data analysis on this customer dataset."
  • "Show me the distribution of ages in the 'customer_data.csv' file."
  • "Identify any outliers in the income column and show a boxplot."

Tips & gotchas

  • This skill requires access to Python libraries like pandas, numpy, matplotlib, and seaborn.
  • The skill operates on data loaded into a Pandas DataFrame; ensure your data is properly formatted for analysis.
  • The provided code examples use 'customer_data.csv' as an example file name - you will need to provide the correct filename when using this skill.