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explore-data

Profile tables and files to assess structure, quality, and patterns.

23.4k|2.8k|Updated Jan 23, 2026
Search Tags:#data quality#data analysis#sql#statistics#data profiling#dataset exploration
One-click install
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill explore-data-anthropics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: explore-data
Source: https://github.com/anthropics/knowledge-work-plugins/tree/main/data/skills/explore-data
Command: npx skills add https://github.com/anthropics/knowledge-work-plugins --skill explore-data-anthropics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you quickly understand the structure, quality, and patterns within a new dataset, whether it's a database table or a file, before you start any in-depth analysis.

Core Features & Use Cases

  • Data Profiling: Generates comprehensive statistics for tables and files, including row/column counts, data types, null rates, and distinct value counts.
  • Quality Assessment: Identifies potential data quality issues like high null rates, suspicious values, and inconsistencies.
  • Pattern Discovery: Suggests relationships, hierarchies, and potential join keys.
  • Use Case: When presented with a new sales database table, use this Skill to get an immediate overview of its columns, identify any missing customer IDs, and understand the distribution of sales amounts.

Quick Start

Use the explore-data skill to profile the table named 'customer_orders'.

Frequently Asked Questions about explore-data

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

FAQPage Schema
How do I profile a database table to understand its data quality?▼

Data profiling generates statistics like row counts, null rates, and distinct values to assess data quality. It identifies missing values and suspicious records to give you an immediate overview of structural issues before deep analysis.

What is the best way to explore a new dataset before analysis?▼

Dataset exploration reveals structure, patterns, and quality issues before in-depth analysis. It profiles columns and identifies potential join keys so you understand the shape and characteristics of your data.

How do I check for missing data and inconsistencies in a SQL table?▼

Checking for missing data and inconsistencies involves calculating null rates and identifying suspicious values. Quality assessment flags these issues to help you clean the dataset before running analytical queries.

Can I use data profiling to find potential join keys across different tables?▼

Yes, pattern discovery suggests relationships, hierarchies, and potential join keys across tables. It analyzes your datasets to identify matching columns and structural connections for data warehousing workflows.

What statistics should I generate to understand the shape of my data?▼

Generate comprehensive statistics including row and column counts, data types, null rates, and distinct value counts. These metrics reveal the dataset's structure and highlight distributions for analytical dimensions.

Does data profiling work with both database tables and files?▼

Yes, data profiling supports both data warehousing tables and file-based datasets. It profiles structures from either source to identify quality issues and suggest follow-up analyses regardless of the storage format.

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