databricks-data-quality-analyst

Name: databricks-data-quality-analyst
Availability: InStock
Author: alexeyban

Generate QA plans, validation reports, and governance artifacts for Databricks data pipelines.

21|4|Updated Mar 9, 2026

Search Tags:#validation #qa #reporting #governance #artifacts #data-quality #databricks

Ready to run

npx skills add https://github.com/alexeyban/databricks-lab --skill databricks-data-quality-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-data-quality-analyst
Source: https://github.com/alexeyban/databricks-lab/tree/main/skills/databricks-data-quality-analyst
Command: npx skills add https://github.com/alexeyban/databricks-lab --skill databricks-data-quality-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables Codex to operate as the Databricks data-quality analyst, producing QA plans, validation results, and governance artifacts for Databricks data pipelines.

Core Features & Use Cases

Adopt the databricks-data-quality-analyst role and align actions with the agent’s mission and rules.
Generate outputs such as QA plans, validation reports, and architecture deliverables across Databricks pipelines.
Follow the agent definition before substantive work and deliver mission-aligned artifacts.

Quick Start

Adopt the databricks-data-quality-analyst role and generate QA plans and artifacts per the agent specification.

Frequently Asked Questions about databricks-data-quality-analyst

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

FAQPage Schema

How do I create a data-quality QA plan for Databricks pipelines?▼

To create a data-quality QA plan for Databricks pipelines, adopt the databricks-data-quality-analyst role and follow the agent definition to produce structured validation and reporting deliverables across your data pipelines.

What is data-quality validation in Databricks governance?▼

Data-quality validation in Databricks governance is the process of generating QA plans and reports to ensure data pipelines meet defined functional and architectural rules before producing final deliverables.

Can I use this approach to generate validation reports for Databricks data pipelines?▼

Yes, you can generate validation reports for Databricks data pipelines by applying the analyst role specification to consistently deliver reporting and architecture deliverables across your workflows.

What's the best way to automate QA reporting across Databricks workflows?▼

The best way to automate QA reporting across Databricks workflows is to activate the analyst role, which consistently delivers data-quality QA artifacts and governance reporting for pipeline validation tasks.

Do I need predefined governance artifacts to start data-quality validation in Databricks?▼

You need to read the agent document first to adopt the role before substantive work, ensuring your Databricks data pipelines align with the required governance artifacts and QA planning rules.