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data-exploration

Profile datasets to assess structure, quality, and patterns.

1|Updated Jan 26, 2026
Search Tags:#data quality#data exploration#eda#data profiling#dataset analysis#schema understanding
One-click install
npx skills add https://github.com/olavocarvalho/data-agents --skill data-exploration-olavocarvalho
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-exploration
Source: https://github.com/olavocarvalho/data-agents/tree/main/skills/data-exploration
Command: npx skills add https://github.com/olavocarvalho/data-agents --skill data-exploration-olavocarvalho

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you systematically understand new datasets by profiling their structure, identifying data quality issues, and discovering patterns before diving into in-depth analysis.

Core Features & Use Cases

  • Structural Understanding: Assesses table-level details like row/column counts and identifies primary keys.
  • Column-Level Profiling: Computes null rates, distinct counts, value distributions, and identifies anomalies for all column types (numeric, string, temporal, boolean).
  • Quality Assessment: Provides frameworks for evaluating completeness, consistency, accuracy, and timeliness of data.
  • Relationship Discovery: Helps identify potential foreign keys, hierarchies, correlations, and redundant columns.
  • Use Case: When presented with a new customer dataset, use this skill to quickly understand the distribution of customer demographics, identify missing contact information, and flag any unusual entries before building a segmentation model.

Quick Start

Profile the attached dataset 'customer_data.csv' to understand its structure and identify any 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 understand its structure and quality?▼

Dataset profiling involves systematically assessing table-level details like row and column counts, identifying primary keys, and computing null rates to understand data quality. This Skill automatically evaluates distributions and flags anomalies for all column types.

What is the best way to identify data quality issues before building a segmentation model?▼

Identifying data quality issues requires evaluating completeness, consistency, accuracy, and timeliness. This Skill profiles datasets to flag missing values, detect outliers, and highlight unusual entries, ensuring your data is clean before deeper analysis.

How do I check for nulls, outliers, and column distributions in my CSV file?▼

Checking for nulls and outliers requires column-level profiling across numeric, string, temporal, and boolean types. This Skill computes null rates, distinct counts, and value distributions to systematically reveal your dataset's underlying patterns.

Can I use data exploration to find potential foreign keys and redundant columns?▼

Data exploration can discover potential relationships by identifying foreign keys, hierarchies, and correlations. This Skill analyzes your dataset's schema to pinpoint redundant columns and map structural connections before you proceed with modeling.

Does this data profiling approach work for both numeric and string column types?▼

Data profiling works for numeric, string, temporal, and boolean column types. The Skill systematically calculates distinct counts and value distributions for each format, providing a comprehensive structural understanding of your entire dataset.

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