Dbt Labs@Dbt Agent Skills

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()`) rebuilding or not, cross-developer cloning, lag_tolerance.

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.

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.

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.

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), enforcing contracts, setting access or groups, or doing multi-project dbt Mesh work (cross-project refs via dependencies.yml, disambiguating similarly-named models, splitting a monolith). Covers single- and multi-project, and planning or advising as well as implementing.

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.

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.

Use when a user wants to upgrade, update, or migrate a dbt-core project to the latest version — e.g. "upgrade my dbt project," "migrate this off dbt-core 1.5," "get this project running on the latest dbt," "bump the dbt-core version." Upgrades a dbt-core v1 project (on 1.3, 1.4, 1.5, 1.6, or 1.7) all the way to 1.12, applying the required breaking, behavior, and deprecated changes from a data-driven issue corpus. Inputs — starting_version (the project's current dbt-core minor, one of 1.3/1.4/1.5/1.6/1.7) and adapter_type (snowflake/redshift/bigquery/databricks/spark); both are normally supplied by the caller (e.g. the dbt VS Code extension), with fallbacks described in the skill.

Audits dbt documentation coverage and drafts missing model/column descriptions in the project's own house style, one folder at a time, for human review. Use when documenting undocumented models, backfilling missing YAML descriptions, auditing doc coverage, or keeping schema YAML in sync with model SQL — especially on multi-contributor projects where new models routinely land undocumented.

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.

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.

Use when a user needs help triaging dbt-core to dbt v2 migration errors. Runs dbt-autofix first, then classifies remaining errors into actionable categories (auto-fixable, guided fixes, needs input, blocked).

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.

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.