dbt

Tools

Skills

14 in the ChatGPT listing

• Adding dbt unit testCreates unit test YAML definitions that mock upstream model inputs and validate expected outputs.
• Answering natural language questions with dbtWrites and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions.
• Building dbt semantic layerUse when creating or modifying dbt Semantic Layer components — semantic models, metrics, dimensions, entities, measures, or time spines.
• Configuring dbt mcp serverGenerates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt.
• Creating mermaid dbt dagGenerates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks.
• Fetching dbt docsRetrieves and searches dbt documentation pages in LLM-friendly markdown format.
• Migrating dbt core to fusionUse when a user needs help triaging dbt-core to Fusion migration errors.
• Migrating dbt project across platformsUse 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.
• Running dbt commandsFormats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters.
• Troubleshooting dbt job errorsDiagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues.
• Upgrading dbt coreUse when a user wants to upgrade, update, or migrate a dbt-core project to a newer or the latest version — e.g.
• Using dbt for analytics engineeringBuilds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show.
• Using dbt stateUse when a user is enabling, configuring, optimizing, or debugging dbt State (the server-backed reuse mechanism that clones or skips nodes instead of rebuilding them).
• Working with dbt meshUse 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, or BI tools depend on — to judge whether the change is breaking and who it affects.