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explore-data

Profile datasets to reveal shape, quality, and column-level statistics.

1|Updated Apr 2, 2026
Search Tags:#profiling#exploration#data-analysis#quality-check#dataset#data-quality#profile
One-click install
npx skills add https://github.com/kongaharsha/claude-skills --skill explore-data-kongaharsha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: explore-data
Source: https://github.com/kongaharsha/claude-skills/tree/main/explore-data
Command: npx skills add https://github.com/kongaharsha/claude-skills --skill explore-data-kongaharsha

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profile datasets quickly to reveal shape and quality.

Core Features & Use Cases

  • Data profiling: generate column-level stats, data types, nulls, and cardinality.
  • Quality assessment: identify anomalies, placeholders, and obvious data quality issues.
  • Use Case: onboarding a new dataset by generating a compact data profile and recommended next steps.

Quick Start

Run /explore-data <table_name or file> to generate a data profile.

Frequently Asked Questions about explore-data

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

FAQPage Schema
How do I profile a dataset to check data quality and structure?▼

To profile a dataset, run a quick analysis command on your table or file. This generates column-level statistics, data-type inferences, null rates, and quality flags to reveal overall shape and identify anomalies.

What is data profiling and when do I need it for exploratory analysis?▼

Data profiling is the process of examining a dataset to reveal its shape, quality, and patterns. You need it during new data onboarding or exploratory analysis to understand column-level statistics and identify obvious data quality issues.

How do I generate column-level statistics and null rates for a new table?▼

You can generate column-level statistics and null rates by running a data profiling command on the new table. This infers data types and calculates cardinality to assess the dataset's quality and structural composition.

Can I use this data exploration method on uploaded files for onboarding?▼

Yes, data profiling is applicable to both new tables and uploaded files for onboarding. It delivers a compact data profile including null rates and quality flags to guide your subsequent analytics steps.

What's the best way to identify anomalies and placeholders during a quality check?▼

The best way to identify anomalies and placeholders during a quality check is to run a data profile. This process evaluates the dataset to flag obvious data quality issues and reveal underlying patterns.

What should I do after generating a data profile for my dataset?▼

After generating a data profile, you should review the recommended next steps provided by the analysis. Use the revealed column-level statistics, data types, and quality flags to guide your subsequent analytics tasks.

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