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Exploratory Data Analysis

Listed
davila7·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 davila7/claude-code-templates --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

davila7

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.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 457 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as davila7/claude-code-templates.
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About this skill

What it does

The exploratory data analysis skill enables AI agents to analyze datasets, identify patterns, and generate insights through statistical summaries and visualizations. It supports tasks like calculating descriptive statistics, detecting outliers, and creating plots such as histograms or scatterplots.

When to use it

  • To understand the structure and distribution of a new dataset before modeling.
  • When identifying potential correlations between variables for further analysis.
  • During data preprocessing steps to clean or transform data based on observed patterns.
  • For generating quick visual summaries to present findings to stakeholders.

Key capabilities

  • Descriptive statistics generation (mean, median, standard deviation).
  • Outlier detection and handling suggestions.
  • Creation of basic plots like histograms, scatterplots, and boxplots.
  • Data summary reports with key observations.

Example prompts

  • "Generate a histogram for the 'age' column in this dataset."
  • "Calculate descriptive statistics for all numerical columns."
  • "Identify any outliers in the 'sales' variable and suggest how to handle them."

Tips & gotchas

  • Ensure your data is properly formatted (e.g., numeric values where needed) before running analysis.
  • Some visualization features may require additional libraries or tools, which might not be available in all environments.

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