Does it support Great Expectations?

Yes, it includes standardized patterns for setting up Great Expectations contexts, creating expectation suites, and running validators on data batches.

How does it help with data observability?

It provides methods to calculate and track key quality metrics such as completeness, uniqueness, and timeliness, allowing you to monitor the health of your data over time.

When should I use this skill?

Use this skill whenever you are building or maintaining data pipelines, setting up data warehouses, or implementing data governance protocols to ensure high data integrity.

What is the Data Quality Checker skill?

It is a specialized capability for Claude Code designed to help engineers implement data validation, schema checks, and quality monitoring frameworks within their codebase.

Can I implement custom validation logic?

Absolutely. The skill provides templates for defining custom Python-based validation functions that can check for nulls, duplicates, and data freshness specific to your domain.

Data Quality Checker

Name: Data Quality Checker
Author: armanzeroeight

byarmanzeroeight

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Analytics & Monitoring

Implements automated data validation, quality monitoring, and integrity checks for reliable data pipelines.

About

The Data Quality Checker skill empowers Claude to architect and implement robust validation frameworks for modern data engineering workflows. It provides standardized patterns for integrating Great Expectations, defining custom validation rules, and calculating essential quality metrics like completeness, uniqueness, and timeliness. This skill is essential for developers building production-grade data pipelines who need to ensure data governance, prevent schema drift, and maintain high-trust datasets through automated monitoring and proactive alerting systems.

Key Features

Data freshness and staleness monitoring for pipeline observability
Automated schema validation and column-level integrity checks
Quality metric tracking including completeness, uniqueness, and validity
Custom validation rule implementation for domain-specific logic
Great Expectations integration for enterprise-grade validation suites
19 GitHub stars

Use Cases

Validating incoming data at the ingestion stage of an ETL pipeline
Setting up quality-based alerting for production data monitoring
Implementing automated data governance rules and schema enforcement

About

Key Features

Data freshness and staleness monitoring for pipeline observability
Automated schema validation and column-level integrity checks
Quality metric tracking including completeness, uniqueness, and validity
Custom validation rule implementation for domain-specific logic
Great Expectations integration for enterprise-grade validation suites
19 GitHub stars

Use Cases

Validating incoming data at the ingestion stage of an ETL pipeline
Setting up quality-based alerting for production data monitoring
Implementing automated data governance rules and schema enforcement