Catch dbt test gaps before bad data ships

Audit local dbt model SQL and schema YAML for missing tests, source freshness and test gaps, likely-key coverage, missing model descriptions, SELECT *, and raw table references. Get reviewable findings without running dbt or connecting to a warehouse.

See what the scanner can flag

Identify models with no schema entry or unique/not_null tests, sources missing freshness or tests, likely ID columns lacking unique or not_null coverage, models missing descriptions, SELECT * usage, and raw FROM or JOIN references that may need ref() or source().

Get evidence and usable starting points

SQL findings include file and line evidence, severity, a message, and remediation. Missing model-test and likely-key findings also include starter tests YAML to review and adapt; the skill never writes it into the project.

Run locally and read-only

The bundled scanner uses only the Python 3 standard library. It requires no PyYAML, dbt invocation, warehouse credentials, network access, secret reads, or file writes.

Know what still needs verification

The parser is heuristic, unusual YAML layouts can be missed, and YAML findings may use a general schema.yml location. Confirm each finding in project context before changing tests, freshness thresholds, or model references.