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@9d91941 official
by dbt Labsdbt-labs/dbt-agent-skills729 stars
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Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt.

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Use this Skill: https://skilld.dev/gh/dbt-labs/dbt-agent-skills/adding-dbt-unit-test

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referenceswarehouse-redshift-caveats.md

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Caveats for Redshift

Unit test limitations for Redshift

  • Redshift doesn't support unit tests when the SQL in the common table expression (CTE) contains functions such as LISTAGG, MEDIAN, PERCENTILE_CONT, and so on. These functions must be executed against a user-created table. dbt combines given rows to be part of the CTE, which Redshift does not support.

    In order to support this pattern in the future, dbt would need to "materialize" the input fixtures as tables, rather than interpolating them as CTEs. Adding this functionality is proposed in GitHub issue #8499.

  • Redshift doesn't support unit tests that rely on sources in a database that differs from the models. Redshift sources need to be in the same database as the models.

Source: SKILL.md on GitHub

Third-party checks

1 warning17d5 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The skill provides instructional guidance and reference material for creating dbt unit tests. It does not contain any executable scripts, dependencies, or security risks.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • Runlayer7mo

    9/14 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at 9d91941. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 5 months ago

Capability

user-invocable
false
metadata
{
  "author": "dbt-labs"
}

Topics

  • dbt
  • unit-testing
  • sql
  • yaml
  • tdd
  • data-warehouse

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What it does

Creates YAML unit test definitions for dbt SQL models with mocked upstream inputs and expected outputs. Use this when adding unit tests to validate model transformation logic or practicing test-driven development in dbt projects.

Generated from the current SKILL.md.

Frequently asked

What SQL models can I create unit tests for?
You can create unit tests for SQL models only. Python models, snapshots, seeds, sources, analyses, and models using materialized view or recursive SQL materializations are not supported.
Do upstream models need to exist before running unit tests?
Yes. Direct parent models must exist in the warehouse before running unit tests. You can build them schema-only with `dbt run --select +my_model --exclude my_model --empty`, or use `dbt build --select my_model` which handles the full pipeline automatically.
Should I run unit tests in production?
No. dbt Labs recommends running unit tests only in development and CI environments. Use the `--exclude-resource-type` flag or `DBT_EXCLUDE_RESOURCE_TYPES` environment variable to skip them in production builds.
What data formats are supported for mock inputs and outputs?
The default format is `dict` (inline YAML). The skill also supports CSV and SQL formats for fixture data, with SQL required when testing models that depend on ephemeral models.
Can I unit test cross-project models or models from packages?
No. dbt only supports adding unit tests to models in your current project.

Generated from the current SKILL.md. These answers refresh after source changes.