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universal-eda

Profile datasets and detect quality issues, distributions, and correlations.

Updated Aug 23, 2026
Search Tags:#data quality#visualization#pattern detection#data exploration#eda#data profiling
One-click install
npx skills add https://github.com/nimrodfisher/ai-workshop-cursor --skill universal-eda
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: universal-eda
Source: https://github.com/nimrodfisher/ai-workshop-cursor/tree/main/.cursor/skills/universal-eda
Command: npx skills add https://github.com/nimrodfisher/ai-workshop-cursor --skill universal-eda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of understanding new datasets, identifying patterns, checking data quality, and generating initial insights, saving significant time in the data exploration phase.

Core Features & Use Cases

  • Comprehensive Data Profiling: Understand dataset structure, data types, and memory usage.
  • Automated Quality Checks: Detect duplicates, missing values, and potential data type issues.
  • Distribution & Outlier Analysis: Visualize and quantify the spread of numeric and categorical data.
  • Relationship Discovery: Identify correlations between variables using statistical methods and visualizations.
  • Pattern Detection: Surface insights specific to time-series, segmentation, or behavioral data.
  • Use Case: When presented with a new customer transaction dataset, use this Skill to quickly understand customer demographics, purchase patterns, identify high-value segments, and detect anomalies in spending behavior.

Quick Start

Run a full exploratory data analysis on the attached dataset 'customer_data.csv'.

Frequently Asked Questions about universal-eda

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform exploratory data analysis on a new dataset?▼

To perform exploratory data analysis, you can automate the process of understanding new datasets by running systematic profiling to identify data structure, quality issues, distributions, and initial insights. The analysis adapts its depth based on dataset size and user focus.

What is the best way to automate data profiling and quality checks for transactional data?▼

The best way to automate data profiling for transactional data is using systematic statistical analysis to detect duplicates, missing values, and data type issues while surfacing high-value customer segments and anomalies in spending behavior.

How do I uncover correlations and distributions in a time-series dataset?▼

To uncover correlations and distributions in a time-series dataset, apply automated statistical methods and visualizations that quantify the spread of numeric variables and identify relationship patterns specific to time-series and behavioral data.

Can I use pandas and seaborn for automated pattern detection in large datasets?▼

Yes, you can use pandas and seaborn for automated pattern detection in large datasets. The analysis adapts its depth based on dataset size, utilizing these libraries to visualize and quantify distributions, correlations, and actionable patterns across various dataset types.

Does exploratory data analysis work with behavioral and segmentation data?▼

Yes, exploratory data analysis works with behavioral and segmentation data by identifying specific patterns through automated profiling and statistical analysis, helping you quickly understand customer demographics and detect anomalies in behavior.

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