Migrating Dbt Core To Fusion
Use when a user needs help triaging dbt-core to Fusion migration errors. Runs dbt-autofix first, then classifies remaining errors into actionable categories (auto-fixable, guided fixes, needs input, blocked).
Auditing Skills
Use when checking skills for security or quality issues, reviewing audit results from skills.sh or Tessl, or remediating findings across published skills.
Building Dbt Semantic Layer
Use when creating or modifying dbt Semantic Layer components — semantic models, metrics, dimensions, entities, measures, or time spines. Covers MetricFlow configuration, metric types (simple, derived, cumulative, ratio, conversion), and validation for both latest and legacy YAML specs.
Working With Dbt Mesh
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, or BI tools depend on — to judge whether the change is breaking and who it affects. Also use when versioning a model (model versions, latest_version, latest_version_pointer, deprecation_date, migration windows), enforc…
Configuring Dbt Mcp Server
Generates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt. Use when setting up, configuring, or troubleshooting the dbt MCP server for AI tools like Claude Desktop, Claude Code, Cursor, or VS Code.
Running Dbt Commands
Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles, or show queries via dbt CLI. Use when unsure which dbt executable to use or how to format command parameters.
Troubleshooting Dbt Job Errors
Diagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues. Use when a dbt Cloud/platform job fails and you need to diagnose the root cause, especially when error messages are unclear or when intermittent failures occur. Do not use for local dbt development errors.
Using Dbt For Analytics Engineering
Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.
Answering Natural Language Questions With Dbt
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 analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.
Creating Mermaid Dbt Dag
Generates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks. Use when visualizing dbt model lineage and dependencies as a Mermaid diagram in markdown format.
Fetching Dbt Docs
Retrieves and searches dbt documentation pages in LLM-friendly markdown format. Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer.
Migrating Dbt Project Across Platforms
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.
Dbt Mcp
A MCP (Model Context Protocol) server for interacting with dbt.
Using Dbt State
Use 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). Use when they conflate dbt State with the `state:modified` selector or `--state` deferral. Use when asked about models rebuilding unexpectedly, views with `select *` rebuilding, volatile SQL (`current_timestamp()`, `random()`) rebu…
Adding Dbt Unit Test
Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt.