data-quality-auditor

Audit CSV and Excel datasets for missing values, duplicates, and inconsistencies.

86|18|Updated Dec 14, 2025

Search Tags:#data quality #auditing #data validation #pandas #etl #data cleaning

Ready to run

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill data-quality-auditor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-auditor
Source: https://github.com/dkyazzentwatwa/chatgpt-skills/tree/main/data-quality-auditor
Command: npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill data-quality-auditor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need for reliable data by providing automated checks for common data quality issues, preventing errors in analysis and operations.

Core Features & Use Cases

Comprehensive Auditing: Detects missing values, duplicates, type inconsistencies, and pattern violations.
Quality Scoring: Generates an overall score and component scores for completeness, uniqueness, validity, and consistency.
Use Case: Before importing a new dataset into a data warehouse, use this Skill to automatically validate its quality, identify problematic columns, and receive actionable recommendations for cleaning.

Quick Start

Use the data-quality-auditor skill to audit the attached file 'customer_data.csv'.

Frequently Asked Questions about data-quality-auditor

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

FAQPage Schema

How do I check data quality and find missing values in a CSV file?▼

To check data quality in a CSV file, automated assessments detect missing values, duplicates, and type inconsistencies. This process generates a detailed quality scoring report highlighting problematic columns for targeted cleaning.

What is data quality scoring and how does it validate datasets?▼

Data quality scoring validates datasets by generating an overall score alongside component scores for completeness, uniqueness, validity, and consistency. It identifies pattern violations and type mismatches to ensure ETL pipeline integrity.

Can I use pandas to audit an Excel dataset for duplicates and type mismatches?▼

Yes, you can use pandas to audit Excel datasets for duplicates and type mismatches. Automated data quality assessments utilize pandas for data manipulation to identify inconsistencies and generate actionable cleaning recommendations.

How do I validate ETL pipeline integrity before importing data into a warehouse?▼

To validate ETL pipeline integrity before warehouse import, run automated data quality assessments on your datasets. This identifies missing values, duplicates, and inconsistencies, providing a detailed report and quality score.

Do I need numpy installed to perform data validation on my datasets?▼

Yes, numpy is required alongside pandas to perform data validation. These dependencies support the data manipulation and analysis required to detect missing values, duplicates, and type mismatches in CSV and Excel files.

What is the best way to identify pattern violations and type inconsistencies in tabular data?▼

The best way to identify pattern violations and type inconsistencies in tabular data is through comprehensive auditing scripts. These scripts assess data quality by checking for missing values and generating component scores for validity.