Does it support automated comparisons?

Yes, it includes a `compare-model-data` command that automates row count, schema comparison, and sample data extraction into a markdown report.

What types of data differences does it help identify?

It categorizes differences into Expected (timing/rounding), Investigate (small metric shifts), and Critical (large row count gaps or missing data).

What is the primary purpose of this Claude Code skill?

It provides reusable SQL patterns and a structured strategy to validate that new or refactored dbt models produce results equivalent to the original versions.

How does it handle very large datasets?

The skill includes specific 'Handling Large Tables' patterns that break down complex queries into manageable segments to avoid database timeouts on tables with 1B+ rows.

Data Quality Validation Patterns

Name: Data Quality Validation Patterns
Author: kziaj

bykziaj

•

Database Management

Provides standardized SQL templates and strategies for validating data consistency between dbt models during migrations and refactors.

This skill empowers data engineers to maintain high data integrity by providing a structured framework for comparing dbt models. It offers reusable SQL patterns for row count verification, primary key uniqueness checks, and business-critical metric comparisons (like ARR or revenue). Designed for production workflows involving Snowflake and dbt, the skill includes specialized logic for handling billion-row tables and interpreting acceptable variances caused by timing or rounding. It ensures that 'verified' models remain functionally equivalent to 'scratch' models, reducing the risk of logic errors during complex refactoring tasks.

Key Features

013 GitHub stars

02Standardized SQL templates for row count and unique key validation

03Business-critical metric comparison for financial and operational data

04Boolean flag distribution analysis to verify filtering and status logic

05Optimized strategies for validating billion-row tables without timeouts

06Automated validation command for quick schema and data comparisons

Use Cases

01Migrating dbt models from scratch schemas to verified production environments

02Refactoring complex SQL logic while ensuring functional parity with legacy models

03Performing data integrity audits during large-scale database migrations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kziaj/claude-agents data-quality-validation-patterns

For use in Claude.ai and ChatGPT

Download Skill