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exploration

Profile datasets to assess data quality, schemas, and patterns.

Updated Aug 23, 2026
Search Tags:#analytics#data#profiling#schemas#data-quality#datasets
One-click install
npx skills add https://github.com/kortix-ai/computer --skill exploration-kortix-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploration
Source: https://github.com/kortix-ai/computer/tree/main/packages/kortix-opencode/skills/GENERAL-KNOWLEDGE-WORKER/exploration
Command: npx skills add https://github.com/kortix-ai/computer --skill exploration-kortix-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data teams often struggle to quickly understand the quality, structure, and patterns within datasets, leading to unreliable insights and slow analytics. This skill provides a structured methodology to profile data, assess quality, and discover schemas and relationships to enable faster, safer data-driven decisions.

Core Features & Use Cases

  • Column-level profiling: null rates, distinct counts, top values, data types, and basic quality checks.
  • Schema understanding: identify grain, primary keys, and potential foreign keys to guide data modeling and governance.
  • Pattern discovery: detect distributions, correlations, and anomalies to inform cleaning and feature engineering.
  • Use Case: a data analyst profiles a new dataset to surface quality issues and schema gaps before ingestion into a data lake.

Quick Start

Profile the attached dataset to generate a data quality report and a schema overview.

Frequently Asked Questions about exploration

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

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

Dataset profiling analyzes datasets to assess data quality by calculating null rates, distinct counts, and top values, helping you understand schemas and reveal data patterns before analysis.

How do I profile a new dataset to identify schemas and potential foreign keys?▼

You can profile a new dataset to identify its grain, primary keys, and potential foreign keys, generating a schema overview and column-level statistics to guide data modeling and governance.

Can I use exploratory data analysis to detect distributions and correlations in semi-structured data?▼

Yes, exploratory data analysis applies to both structured and semi-structured data, detecting distributions, correlations, and anomalies to inform data cleaning and feature engineering.

Does this data profiling approach work for product analytics and data governance contexts?▼

Data profiling supports exploratory data analysis across research, product analytics, and data governance contexts by providing phase-based profiling and lineage hints for structured datasets.

What is the best way to surface quality issues before ingesting data into a data lake?▼

The best way to surface quality issues before data lake ingestion is to run column-level profiling and data quality checks to identify schema gaps, null rates, and basic anomalies.

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