@dbt-labs

using-dbt-for-analytics-engineering covers model building, sql transformations, testing, and validation through dbt's abstraction layer. applies software engineering discipline (dry, modularity, testing) to data pipeline work with emphasis on iterative validation and cost awareness.

troubleshooting-dbt-job-errors guides systematic diagnosis of dbt cloud job failures through log analysis, api querying, and data investigation. covers when to use and the principle of investigating root causes before modifying tests.

adding-dbt-unit-test provides reference links and warehouse-specific guidance but lacks actionable steps, decision logic, or concrete procedures for writing dbt unit tests.

building-dbt-semantic-layer documents the conceptual taxonomy of semantic layer components (models, entities, dimensions, metrics) but lacks actionable procedure for constructing them. mostly reference pointers without guidance.

Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about…

Generates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt. Use when setting up, configuring, or…

Retrieves and searches dbt documentation pages in LLM-friendly markdown format. Use when fetching dbt documentation, looking up dbt features, or answering…

Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles,…