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    dbt Test & Quality Auditor

    dbt Test & Quality Auditor

    by JustHandled Labs

    Audit your dbt project for the test and documentation gaps that let bad data ship. Flags models with no unique or not_null tests, sources missing freshness config or tests, likely keys without a not_null test, models missing descriptions, SELECT * in models, and raw table references that should use ref() or source(). Each finding comes with a suggested tests: YAML snippet to drop into schema.yml.

    Updated Jun 2026
    Security scanned
    Works with Claude Code

    $15

    · or 75 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Identify missing unique and not_null tests on primary keys
    • Detect hard-coded table references and SELECT star anti-patterns
    • terminal, file_read automation included
    • Ready for Works with Claude Code
    • Instant install

    Sample input

    Audit the dbt models in my analytic_models/ warehouse folder for test coverage and quality issues.

    Sample output

    dbt Audit Report: analytic_models/

    • HIGH: fct_orders.sql (Line 12) - Uses SELECT * which impacts performance and schema stability.
    • MEDIUM: stg_users.yml - Column user_id is missing unique and not_null tests.
    • INFO: 4 models missing descriptions in schema.yml.

    About This Skill

    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.

    Use Cases

    • Identify missing unique and not_null tests on primary keys
    • Detect hard-coded table references and SELECT star anti-patterns
    • Generate YAML snippets to fix missing dbt model documentation
    • Audit dbt source freshness and test configurations

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Read Files

    File Scopes

    dbt-test-quality-auditor/**
    **/models/**/*.sql
    **/models/**/*.yml
    **/models/**/*.yaml
    **/schema.yml
    **/dbt_project.yml

    Read-only inspection first. The bundled auditor reads matching dbt files and prints markdown or JSON findings plus suggested tests: YAML. It installs nothing, transmits nothing, modifies nothing, and never runs dbt or touches your warehouse. Any write, install, deploy, or live-account action requires explicit user confirmation.

    Tags

    dbt
    data-engineering
    sql
    data-quality
    analytics-engineering

    Works with Claude Code, Codex CLI, Cursor, OpenCode/OpenClaw, Gemini CLI, and other agents that load SKILL.md folders. The bundled auditor uses the Python 3 standard library only (it parses dbt SQL and YAML without third-party libraries) and degrades to manual checklist mode when Python or matching files are unavailable.

    Creator

    JustHandled Labs

    JustHandled Labs creates focused agent skills and workflow packs for Claude, Codex, Cursor, and AI-assisted builders. Each tool is designed around a real repeatable task: cleaner commits, better PRs, stronger handoffs, safer repo hygiene, clearer documentation, and less copy-paste chaos. The goal is not generic AI productivity. The goal is specific workflows that are easier to run, review, and repeat. Maintained by H.J. Westerfield, with a background in communications, editing, project coordination, customer support, and practical AI systems. JustHandled Labs builds tools for people who want useful automation without theatrical complexity.

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