Analytics-engineering and data-modeling skills from dbt Labs.
Official dbt skills for analytics engineering, semantic layer, Mesh, job troubleshooting, MCP, and migration.
Arranged in the author's recommended order. Walk through them in sequence, or open any one on its own.
Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show.
Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs.
Use when creating or modifying dbt Semantic Layer components – semantic models, metrics, dimensions, entities, measures, or time spines.
Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions.
Use when changing a dbt model in a way that could break its consumers — renaming, removing, or retyping a column, or changing a model that downstream models, exposures, dashboards.
Diagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues.
Generates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt.
Retrieves and searches dbt documentation pages in LLM-friendly markdown format.
Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters.
Use when a user needs help triaging dbt-core to Fusion migration errors.
Use when migrating a dbt project from one data platform or data warehouse to another (e.g., Snowflake to Databricks, Databricks to Snowflake) using dbt Fusion's real-time compilation to identify and fix SQL dialect differences.