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data-exploration

Profile datasets to assess structure, quality, and patterns before analysis.

37|3|Updated Mar 1, 2026
Search Tags:#data quality#statistical analysis#pattern discovery#data profiling#dataset exploration#schema understanding
One-click install
npx skills add https://github.com/eren726290/opencode-cowork-plugins --skill data-exploration-eren726290
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-exploration
Source: https://github.com/eren726290/opencode-cowork-plugins/tree/main/data/agents/data/skills/data-exploration
Command: npx skills add https://github.com/eren726290/opencode-cowork-plugins --skill data-exploration-eren726290

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you systematically profile datasets to understand their structure, identify data quality issues, and discover underlying patterns before diving into analysis.

Core Features & Use Cases

  • Structural Understanding: Assess table and column counts, identify primary keys, and classify data types.
  • Column-Level Profiling: Compute null rates, distinct counts, value distributions, and statistical summaries for numeric, string, and temporal columns.
  • Quality Assessment: Evaluate completeness, consistency, accuracy, and timeliness of data.
  • Pattern Discovery: Analyze distributions, temporal patterns, segmentation, and correlations.
  • Use Case: When you receive a new dataset for a marketing campaign analysis, use this skill to quickly understand its contents, identify missing values, and determine which columns are most relevant for segmentation.

Quick Start

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

Frequently Asked Questions about data-exploration

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

FAQPage Schema
What is data profiling and how does it help with dataset exploration?▼

Data quality assessment evaluates dataset completeness, consistency, accuracy, and timeliness. It identifies null rates, distinct counts, and outliers across numeric, string, and temporal columns to quantify overall dataset reliability.

How do I identify null rates and data type issues in a new CSV dataset?▼

To identify null rates and data type issues, profile the dataset to compute column-level distributions, detect missing values, and classify structural schemas. This process reveals completeness gaps and highlights columns requiring type conversion or cleaning.

Can I use this approach to find primary keys and classify column data types?▼

Yes, structural dataset profiling identifies primary keys by analyzing distinct counts and classifies column data types by evaluating value patterns. This schema understanding ensures correct foundational mapping for subsequent statistical analysis.

What is the best way to discover column distributions and outliers before analysis?▼

The best way to discover column distributions and outliers is through systematic pattern discovery, which analyzes statistical summaries and segmentation. This approach identifies temporal patterns and correlations to flag anomalies affecting data accuracy.

Does dataset exploration work for assessing marketing campaign data readiness?▼

Yes, dataset exploration profiles marketing campaign data to identify missing values, assess completeness, and determine relevant segmentation columns. It applies quality scoring to ensure the dataset meets readiness standards for accurate analysis.

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