data-exploration

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

1|Updated Mar 9, 2026

Search Tags:#profiling #distributions #dataset #data-quality #profile #data-schema #nulls

Ready to run

npx skills add https://github.com/kiryteo/opencode-setup --skill data-exploration-kiryteo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-exploration
Source: https://github.com/kiryteo/opencode-setup/tree/main/skills/data-exploration
Command: npx skills add https://github.com/kiryteo/opencode-setup --skill data-exploration-kiryteo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profile and explore datasets to understand their shape, quality, and patterns before analysis.

Core Features & Use Cases

Structural understanding: deduce table shapes, grain, keys, and update timelines.
Column-level profiling: assess nulls, distinct values, distributions, and patterns.
Relationship discovery: identify potential foreign keys and cross-column correlations.

Quick Start

Provide a quick profiling pass on a new dataset to surface quality issues and schema hints.

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 dataset to check quality before analysis?▼

Dataset profiling involves assessing structural shape, identifying nulls, evaluating data types, and reviewing distinct value distributions. This skill automates a phased workflow to surface schema hints and quality issues quickly.

What is the best way to discover potential primary and foreign keys in a new dataset?▼

Discovering potential keys requires structural understanding and relationship discovery. This skill deduces table grain, identifies primary keys, and detects cross-column correlations to suggest foreign keys within your dataset.

Can I use this to assess null values and distinct distributions across columns?▼

Yes, you can assess null values and distinct distributions. It performs column-level profiling to evaluate null frequencies, distinct counts, and underlying data patterns to reveal overall dataset quality.

Does dataset profiling work for deducing table shapes and update timelines?▼

Yes, dataset profiling works for deducing table shapes and update timelines. The structural understanding phase analyzes your dataset to determine its grain, structural shape, and temporal update patterns.

When do I need to run data schema understanding and quality checks?▼

You need to run data schema understanding and quality checks whenever applying profiling to any new dataset. It is required before analysis to understand schema structure, identify potential keys, and evaluate standard quality classifications.