dbt-artifacts

Monitoring dbt execution captures historical run metadata including test results, model status, and runtime performance. This Skill stores per-run artifacts in structured tables (invocations, model_executions, test_executions) joined by command_invocation_id and node_id, enabling you to query patterns across environments and versions to identify flaky tests and performance trends.

dbt produces execution artifacts including test pass/fail status, model runtime duration, data quality results, and dependency metadata. This Skill exposes these artifacts through core tables and join patterns, allowing you to analyze run history, detect slow models, and audit execution context across your entire project.

Yes. This Skill tracks dbt runs across environments and versions with support for installation and configuration across platforms. It captures run context and dependencies in standardized tables, enabling cross-environment observability and auditing of your data pipeline execution.

Query the test_executions table joined with invocations by command_invocation_id to surface test status and execution history. Aggregate results over time to rank failing tests, identify patterns, and prioritize which tests need immediate attention for data quality improvement.

This Skill stores comprehensive execution metadata—status, runtime, test results, run context, and dependencies—in queryable tables with standardized join keys. This creates an auditable record of all dbt runs, enabling compliance tracking, root-cause analysis, and historical comparison across versions.

This Skill requires Snowflake and depends on the brooklyn-data/dbt_artifacts package. It captures artifacts into Snowflake tables, so you need Snowflake configured as your dbt target to store and query execution metadata.