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chaserbreitenbach
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data-validation

Detect anomalies and enforce schema integrity across databases, APIs, and pipelines.

Updated Aug 27, 2026
Search Tags:#validation#schema#ETL#data-pipelines#data-quality#anomaly-detection#quality-score
One-click install
npx skills add https://github.com/chaserbreitenbach/claude-skills-and-agents --skill data-validation-chaserbreitenbach
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/chaserbreitenbach/claude-skills-and-agents/tree/main/skills/testing/data-validation
Command: npx skills add https://github.com/chaserbreitenbach/claude-skills-and-agents --skill data-validation-chaserbreitenbach

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ensures data quality and schema integrity across databases, APIs, data pipelines, and file formats by providing a unified validation framework that detects anomalies and generates actionable quality metrics.

Core Features & Use Cases

  • Schema validation and type checks for structured data sources (SQL, NoSQL, JSON, CSV)
  • Dimensional data quality scoring across completeness, accuracy, consistency, and freshness
  • Automatic generation of data quality reports and anomaly detection insights for ETL monitoring and data migrations

Quick Start

Run a validation job against your data source to generate a quality report and scorecard.

Frequently Asked Questions about data-validation

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

FAQPage Schema
How do I validate data quality and schema integrity across multiple data pipelines?▼

Data quality validation across pipelines is automated by enforcing schema definitions, checking type integrity, and scoring dimensions like completeness and freshness for databases, APIs, and files to generate actionable quality reports.

What is the best way to automate ETL validation and anomaly detection in staging environments?▼

Automating ETL validation involves running configurable validation jobs against your data sources to detect anomalies, enforce schema rules, and auto-generate quality scorecards for monitoring staging and production environments.

Can I use this data validation framework for both SQL databases and JSON API responses?▼

Yes, data validation supports structured data sources including SQL, NoSQL, JSON, and CSV formats, allowing you to perform schema checks and consistency scoring across databases and API responses.

How do I generate a data quality scorecard for a data migration project?▼

Generate a data quality scorecard by running a validation job against your target data source, which evaluates accuracy, completeness, and consistency to produce actionable quality metrics and anomaly insights.

What data quality dimensions are evaluated during schema validation?▼

Schema validation evaluates multiple data quality dimensions including completeness, accuracy, consistency, and freshness to provide a comprehensive quality score and detect anomalies across your data sources.

Does data warehouse testing require any specific dependencies or components to run validation jobs?▼

No specific dependencies or components are required to run data warehouse testing jobs, allowing you to directly validate schema integrity and generate quality scorecards across your warehouse environments.

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