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Adding Dbt Unit Test

Official
dbt-labs·vlatest·data·Apache-2.0

Automatically generates and adds dbt unit tests to models based on schema and data analysis.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add dbt-labs/dbt-agent-skills --skill adding-dbt-unit-test -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

dbt-labs

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned dbt-labs/dbt-agent-skills at 0d094169f5.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
0d094169f5
Manifest
skills/dbt/skills/adding-dbt-unit-test/SKILL.md
Scanned
14 files, 923 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to automatically generate and execute unit tests for dbt (data build tool) models. It ensures data transformation logic remains reliable by validating individual model files against expected outputs and schema constraints within the dbt project structure.

When to use it

  • Pre-deployment validation: Run before merging changes to catch regressions in data transformations early.
  • CI/CD integration: Automate testing pipelines to enforce quality gates for data engineering workflows.
  • Model maintenance: Verify logic integrity when refactoring complex SQL queries or macros.
  • Team collaboration: Standardize testing practices across different team members working on the same dataset.

Key capabilities

  • Generates unit tests directly from dbt model definitions without manual coding.
  • Executes tests against the local dbt project environment.
  • Validates data types, column constraints, and business logic rules.
  • Provides detailed error reports for failed test cases.

Example prompts

  • "Generate a unit test for my customers dbt model to validate that all email addresses follow standard format."
  • "Run all unit tests in the staging package and summarize any failures with line numbers."
  • "Create a new unit test ensuring the order_total column equals the sum of quantity and price in the orders model."

Tips & gotchas

Ensure your dbt project has proper dependencies installed before running generated tests, as missing packages can cause false negatives. Unit tests only validate individual models; they do not replace end-to-end integration testing for complex data pipelines.