Verified:GitHub

data-exploration

Profile datasets to summarize structure, quality, and patterns.

8|3|Updated Mar 9, 2026

Search Tags:#data-quality #anomaly-detection #data-cleaning #data-discovery #schema-analysis #dataset-profiling #statistical-summary

Ready to run

npx skills add https://github.com/vythanhtra/perplexity-skills --skill data-exploration-vythanhtra

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-exploration
Source: https://github.com/vythanhtra/perplexity-skills/tree/main/data-exploration
Command: npx skills add https://github.com/vythanhtra/perplexity-skills --skill data-exploration-vythanhtra

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.

Core Features & Use Cases

Data profiling: generate table- and column-level summaries of structure, quality, and distributions.
Quality assessment: detect missing values, inconsistent types, and potential data quality issues.
Pattern discovery: identify distributions, correlations, and key characteristics to guide analysis and feature engineering.
Use Case: Quickly assess a newly acquired dataset to decide which analyses to run and which columns to prioritize.

Quick Start

Profile the provided dataset to summarize structure, quality, and patterns and propose next steps for analysis.

Frequently Asked Questions about data-exploration

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

FAQPage Schema

How do I profile a new dataset to understand its structure and quality?▼

Data profiling generates table- and column-level summaries to assess dataset structure, detect missing values, and identify distributions, helping you understand overall data quality and patterns before deeper analysis.

What is the best way to detect nulls and outliers during data exploration?▼

The best way to detect nulls and outliers during data exploration is by applying column profiling and statistical summaries to identify missing values and flag potential data quality issues.

Can I use data profiling to decide which dimensions to analyze?▼

Yes, you can use data profiling to decide which dimensions to analyze by discovering column distributions, correlations, and key characteristics that guide feature engineering and prioritize relevant columns for analysis.

Does data discovery work for assessing newly acquired datasets across different domains?▼

Data discovery works for newly acquired datasets across domains by applying schema analysis and dataset profiling to assess structure, identify inconsistent types, and propose next steps for analysis.

How do I identify data quality issues like inconsistent types in my dataset?▼

To identify data quality issues like inconsistent types, run column profiling and dataset profiling to detect missing values, summarize distributions, and generate warnings for potential data quality problems.