🧪 dbt Test & Quality Auditor

Streamline Your Data Quality Workflows

The dbt Test Quality Auditor is a specialized development tool designed to automate the tedious process of auditing dbt projects for testing gaps and documentation debt. Instead of manually scouring YAML files, this skill performs a heuristic analysis of your models and schema definitions to ensure your data pipeline meets production-grade standards.

What it does

• Test Gap Analysis: Identifies missing unique, not_null, and relationship tests on likely primary and foreign keys.
• Source Integrity: Detects dbt sources missing freshness blocks or basic validation tests.
• Anti-Pattern Detection: Flags "SELECT *" usage and hard-coded table references that should be replaced with ref() or source() macros.
• Documentation Audit: Surfaces models and columns missing descriptions required for data catalog clarity.
• Remediation Generation: Provides copy-paste ready YAML snippets to fix identified issues instantly.

Why use this skill?

Prompting a generic AI often results in hallucinations or missed context because LLMs aren't optimized for cross-referencing model SQL with separate YAML declarations. This skill uses a dedicated Python-based scanner to provide evidence-backed findings with exact file and line references, ensuring higher precision than a zero-shot prompt.

Output Format

You receive a structured markdown report categorized by severity (Critical to Info). It includes confirmed findings, impact assessments, and a list of verification steps to ensure your dbt project is robust and compliant.