armor-quality

Name: armor-quality
Availability: InStock
Author: anomalyarmor

Create and manage data quality metrics and validity rules via the AnomalyArmor API.

1|Updated Jan 31, 2026

Search Tags:#validation #data integrity #data quality #metrics #sql #anomalyarmor

Ready to run

npx skills add https://github.com/anomalyarmor/agents --skill armor-quality

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: armor-quality
Source: https://github.com/anomalyarmor/agents/tree/main/skills/quality
Command: npx skills add https://github.com/anomalyarmor/agents --skill armor-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anomalyarmor, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of defining, implementing, and monitoring data quality checks and metrics, ensuring the reliability and accuracy of your data assets.

Core Features & Use Cases

Metric Creation: Define quantitative measurements like row counts, null rates, and distinct counts for tables.
Validity Rule Enforcement: Implement rules such as NOT_NULL, UNIQUE, REGEX, and custom SQL expressions to enforce data integrity.
Use Case: Automatically set up a check to ensure the 'email' column in your 'customers' table never contains null values and create a daily metric for the total row count in your 'orders' table.

Quick Start

Use the armor quality skill to add a null check to the email column for the customers table.

Frequently Asked Questions about armor-quality

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

FAQPage Schema

How do I set up data quality checks for null values and row counts in SQL?▼

Data quality checks for null values and row counts are configured by defining metrics and validity rules like NOT_NULL against specific table columns. This approach uses declarative validation to enforce constraints and quantitative tracking to monitor data characteristics.

What are data integrity validity rules and when do I need them?▼

Data integrity validity rules are declarative constraints such as UNIQUE, REGEX, NOT_NULL, and custom SQL expressions applied to data assets. You need them to ensure the reliability and accuracy of your tables by preventing invalid records from passing unnoticed.

Can I use custom SQL expressions to enforce data validation constraints?▼

Custom SQL expressions can be used to enforce data validation constraints alongside standard rules like NOT_NULL, UNIQUE, and REGEX. This allows you to implement complex data integrity checks tailored to specific asset requirements programmatically.

What is the best way to automate data quality monitoring for database tables?▼

Automating data quality monitoring is achieved by creating quantitative metrics like null rates and distinct counts, then applying validity rules to your data assets. This automated enforcement ensures ongoing reliability and tracks data characteristics over time.

Do I need a Python SDK to manage data quality metrics and validity rules?▼

A Python SDK is required for programmatic interaction with the API to manage data quality metrics and validity rules. This dependency allows you to define, implement, and monitor data characteristics and integrity constraints within your data assets.