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data-exploration

Profile datasets to generate quality metrics and profiling insights.

Updated Jan 11, 2026
Search Tags:#exploration#data-profiling#dataset-quality#schema-understanding#column-profiling#data-quality-assessment
One-click install
npx skills add https://github.com/chelleboyer/reachy_mini_retail_assistant --skill data-exploration-chelleboyer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-exploration
Source: https://github.com/chelleboyer/reachy_mini_retail_assistant/tree/main/skills/data/skills/data-exploration
Command: npx skills add https://github.com/chelleboyer/reachy_mini_retail_assistant --skill data-exploration-chelleboyer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiles datasets to understand structure, quality, and patterns before analysis.

Core Features & Use Cases

  • Data Profiling Framework: Phase 1 structural understanding, Phase 2 column-level profiling, Phase 3 relationship discovery, and a quality assessment framework to guide data work.
  • Data-quality Metrics: Null counts, distinct counts, pattern checks, and distribution analyses to identify anomalies and data quality gaps.
  • Use Case: Before modeling, profile a new dataset to decide which columns to analyze and which quality checks to apply, enabling targeted cleaning and schema documentation.

Quick Start

Profile a new dataset to generate an initial data quality report and a profiling plan for subsequent analysis.

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 assess dataset quality?▼

Data profiling evaluates dataset structure, column-level metrics, and relationships to identify quality gaps. It reports null rates, distinct counts, and distributions, enabling targeted cleaning and schema documentation before deeper analysis begins.

How do I profile a new dataset to understand its schema and structure?▼

To profile a new dataset, apply a phased approach: first understand the structural schema, then perform column-level profiling for distinct counts and patterns, and finally discover cross-column relationships to generate a comprehensive quality assessment.

What's the best way to identify data quality gaps and null rates before modeling?▼

The best way to identify data quality gaps is running pattern checks and distribution analyses on your columns. This generates a data quality report highlighting null counts and anomalies, guiding which columns need targeted cleaning before modeling.

Can I use column profiling to decide which columns to analyze and clean?▼

Yes, column profiling reports null rates, distinct counts, and cross-column consistency to help you decide which columns to analyze. It provides the necessary metrics to apply targeted data cleaning and generate accurate schema documentation.

Does data profiling work for discovering cross-column consistency and relationships?▼

Data profiling includes a relationship discovery phase that evaluates cross-column consistency. By analyzing distributions and patterns across columns, it identifies structural relationships and anomalies to guide subsequent data cleaning workflows.

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