dataset-profiling-quality-audit

Name: dataset-profiling-quality-audit
Availability: InStock
Author: Anthril

Profile datasets and generate data quality reports with cleaning plans.

3|Updated Apr 1, 2026

Search Tags:#python #validation #profiling #sql #data-analytics #data-quality #data-cleaning

Ready to run

npx skills add https://github.com/anthril/official-claude-plugins --skill dataset-profiling-quality-audit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dataset-profiling-quality-audit
Source: https://github.com/anthril/official-claude-plugins/tree/main/plugins/data-analysis/skills/dataset-profiling-quality-audit
Command: npx skills add https://github.com/anthril/official-claude-plugins --skill dataset-profiling-quality-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Profile datasets and audit data quality across six dimensions, producing prioritised cleaning recommendations to help teams trust their analytics.

Core Features & Use Cases

Comprehensive quality profiling across completeness, validity, consistency, uniqueness, timeliness, and accuracy.
Generates prioritised, actionable cleaning plans with concrete techniques, code snippets (Python/SQL), and risk assessments.
Suitable for CRM exports, marketing data, and operational datasets, enabling faster remediation and safer downstream analyses.

Quick Start

Describe your dataset or provide a sample, and Claude will generate the full quality audit and cleaning plan.

Frequently Asked Questions about dataset-profiling-quality-audit

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

FAQPage Schema

How do I profile data quality and generate a cleaning plan for CRM exports?▼

To audit data quality, provide your CRM dataset or sample to generate a structured report across six dimensions, yielding a prioritized cleaning plan with concrete Python pandas and PostgreSQL SQL code snippets for remediation.

What is data quality profiling and how does cross-field validation work?▼

Data quality profiling audits datasets across six dimensions, using cross-field validation and domain-specific checks to surface inconsistencies, producing a structured report with risk assessments to ensure safer downstream analytics.

Can I use Python pandas and SQL PostgreSQL for automated data cleaning?▼

Yes, you can use Python pandas and SQL PostgreSQL for data cleaning; the generated audit outputs concrete code snippets in both languages, providing prioritized, actionable techniques to remediate marketing and operational datasets.

What's the best way to audit completeness and uniqueness in marketing data?▼

The best way to audit completeness and uniqueness in marketing data is to run a comprehensive profiling audit evaluating these dimensions alongside validity and accuracy, outputting a prioritized cleaning plan for faster remediation.

Does data profiling work with operational datasets for cross-field validation?▼

Yes, data profiling works with operational datasets by applying cross-field validation and domain-specific checks to measure consistency and accuracy, generating a structured quality report with actionable cleaning recommendations.