verify-data-quality

Assesses datasets for completeness, correctness, consistency, and timeliness.

Updated Mar 20, 2026

Search Tags:#validation #report #data-quality #notebook #crisp-dm #data-assessment #data-quality-check

Ready to run

npx skills add https://github.com/thbraet/claude-template --skill verify-data-quality

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: verify-data-quality
Source: https://github.com/thbraet/claude-template/tree/main/skills/verify-data-quality
Command: npx skills add https://github.com/thbraet/claude-template --skill verify-data-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill performs a systematic data quality assessment across completeness, correctness, consistency, and timeliness, producing a structured quality report and artifacts for CRISP-DM documentation.

Core Features & Use Cases

Comprehensive four-dimensional data quality checks (completeness, correctness, consistency, timeliness) across datasets.
Generates two artifacts: a Jupyter notebook with validation code and results, and a human-readable summary document for CRISP-DM.

Quick Start

Provide a dataset path to run the quality checks and generate both the notebook and the summary report.

Frequently Asked Questions about verify-data-quality

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

FAQPage Schema

How do I perform a data quality assessment for a CRISP-DM project?▼

To perform a data quality assessment for CRISP-DM, this Skill evaluates dataset completeness, correctness, consistency, and timeliness, then generates a Jupyter notebook with validation code and a markdown summary for the Data Understanding phase.

What does a data quality check include for completeness and consistency?▼

A data quality check for completeness and consistency systematically evaluates your datasets to identify missing values and logical rule violations, producing a machine-readable quality report and a notebook with validation results.

Can I run data quality checks on multiple datasets at the same time?▼

Yes, you can run data quality checks on multiple datasets simultaneously by providing their paths, optionally including reference documents to guide the validation process across completeness, correctness, consistency, and timeliness dimensions.

What artifacts are generated when validating dataset quality?▼

Validating dataset quality generates two artifacts: an executable Jupyter notebook at notebooks/2.4-data-quality.ipynb containing validation code, and a human-readable markdown summary at docs/crisp-dm/2-data-understanding/2.4-data-quality.md.

When do I need to run data quality validation in a data analytics workflow?▼

You need to run data quality validation during the Data Understanding phase of the CRISP-DM workflow to systematically assess dataset completeness, correctness, consistency, and timeliness before proceeding to data preparation.

Do I need any specific dependencies to generate a data quality report?▼

No specific dependencies are required to generate a data quality report, as the Skill operates independently to produce the validation notebook and CRISP-DM summary document directly from your dataset inputs.