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data-exploration

Profile datasets to assess structure, quality, and column patterns.

145|36|Updated Feb 26, 2026
Search Tags:#data quality#exploratory data analysis#pattern discovery#data profiling#dataset analysis#schema exploration
One-click install
npx skills add https://github.com/w95/awesome-claude-corporate-skills --skill data-exploration-w95
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-exploration
Source: https://github.com/w95/awesome-claude-corporate-skills/tree/main/10-data-analytics/data-exploration
Command: npx skills add https://github.com/w95/awesome-claude-corporate-skills --skill data-exploration-w95

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you systematically understand the structure, quality, and patterns within any new dataset before diving into in-depth analysis.

Core Features & Use Cases

  • Structural Understanding: Assesses table-level details like row/column counts, grain, and primary keys.
  • Column-Level Profiling: Computes null rates, distinct counts, distributions, and statistical summaries for all column types (numeric, string, date, etc.).
  • Quality Assessment: Provides a framework for scoring completeness, consistency, accuracy, and timeliness.
  • Relationship Discovery: Identifies potential foreign keys, hierarchies, and correlations between columns.
  • Use Case: When presented with a new sales dataset, use this skill to quickly determine the number of records, identify unique customer IDs, check for missing sales figures, and understand the distribution of sales amounts by region.

Quick Start

Profile the attached dataset 'customer_transactions.csv' to understand its structure and identify potential data quality issues.

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 check data quality and structure?▼

Dataset profiling assesses table-level details like row counts, grains, and primary keys to evaluate data quality. It computes null rates, distinct counts, and statistical summaries to provide a structural understanding before in-depth analysis.

What is exploratory data analysis used for when handling unfamiliar datasets?▼

Exploratory data analysis identifies potential foreign keys, hierarchies, and correlations between columns to discover patterns. It provides a framework for scoring dataset completeness, consistency, accuracy, and timeliness before analysis.

How do I identify nulls and outliers in column distributions?▼

Column-level profiling computes null rates, distinct counts, and distributions to identify nulls and outliers across all column types. It generates statistical summaries that highlight missing values and anomalies within the dataset.

What's the best way to classify columns and discover relationships in a dataset?▼

Column classification and relationship discovery evaluates numeric, string, and date columns to identify correlations and potential foreign keys. It systematically maps structural hierarchies to determine which dimensions require deeper analysis.

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

Data profiling evaluates column distributions and statistical summaries to help decide which dimensions to analyze. It assesses completeness and identifies distinct counts to prioritize dimensions that hold the most analytical value.

Does exploratory data analysis work for assessing dataset accuracy and timeliness?▼

Exploratory data analysis provides a quality assessment framework for scoring dataset accuracy and timeliness. It systematically evaluates structural details and column-level patterns to measure consistency across the entire table.

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