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kwp-data-explore-data

Profile and audit datasets for structural patterns and data quality issues.

7|5|Updated May 7, 2026
Search Tags:#data quality#data warehouse#data profiling#exploratory analysis#dataset audit
One-click install
npx skills add https://github.com/14790897/MiQi --skill kwp-data-explore-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kwp-data-explore-data
Source: https://github.com/14790897/MiQi/tree/main/miqi/skills/kwp/data/explore-data
Command: npx skills add https://github.com/14790897/MiQi --skill kwp-data-explore-data

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the guesswork when working with new or unfamiliar datasets by automatically generating comprehensive quality reports and structural insights.

Core Features & Use Cases

  • Automated Profiling: Instantly calculate row counts, null rates, cardinality, and statistical distributions for any table or file.
  • Data Quality Auditing: Automatically flag anomalies, duplicates, and suspicious values that could compromise your analysis.
  • Use Case: Before building a dashboard, use this skill to verify that your user_id column is unique and that your revenue metrics do not contain unexpected negative values.

Quick Start

Use the explore-data skill to profile the customer_transactions table in the connected data warehouse.

Frequently Asked Questions about kwp-data-explore-data

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

FAQPage Schema
How do I profile a CSV file to check data quality and null rates?▼

To profile a CSV file for data quality, you can analyze structural patterns, calculate null rates, and identify potential anomalies to ensure analytical integrity before building reports.

Can I audit data quality issues directly in my data warehouse tables?▼

Yes, you can audit data warehouse tables directly to flag duplicates, calculate cardinality, and identify suspicious values like unexpected negative metrics without exporting the data first.

What is exploratory analysis for dataset auditing and when do I need it?▼

Exploratory analysis for dataset auditing is the process of automatically calculating statistical distributions and structural patterns to verify analytical integrity, needed when working with new or unfamiliar datasets.

Does data profiling work with Parquet files and live database tables?▼

Data profiling works with both uploaded files like CSV or Parquet and live database tables, instantly generating comprehensive quality reports including row counts and column types for either source.

How do I identify data anomalies and duplicates before building a dashboard?▼

To identify anomalies and duplicates before building a dashboard, run an automated dataset audit that flags suspicious values and verifies column uniqueness like checking for unique user_id entries.

What is the best way to check statistical distributions across an unfamiliar dataset?▼

The best way to check statistical distributions across an unfamiliar dataset is through automated profiling that instantly calculates row counts, cardinality, and column types for analytical integrity.

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