dbt Labs
Official@dbt-labs · Philadelphia, PA
dbt helps data teams work like software engineers—to ship trusted data, faster.
Agent Skills by dbt Labs
Showing 24 vetted skills indexed across 1 GitHub repositories.
dbt-labsauditing-skills
Audit published skills against security scanners and quality reviewers, then remediate findings.
dbt-labscreating-mermaid-dbt-dag
Generate Mermaid flowchart diagrams of dbt model lineage from MCP tools, manifest.json, or code parsing.
dbt-labsupgrading-dbt-core
Migrates dbt-core projects from versions 1.3-1.7 to dbt-core 1.12.
dbt-labsmigrating-dbt-core-to-fusion
Triages dbt-core to Fusion migration errors into actionable fix categories.
dbt-labsmigrating-dbt-project-across-platforms
Migrates dbt projects between data platforms using dbt Fusion compilation and unit tests.
dbt-labsrunning-dbt-commands
Formats and executes dbt CLI commands with correct selectors, flags, and executable selection.
dbt-labstroubleshooting-dbt-job-errors
Diagnose dbt Cloud job failures using run logs, Admin API, git history, and data investigation.
dbt-labsmaintaining-dbt-documentation
Audits dbt documentation coverage and drafts missing model and column descriptions in the project's existing style.
dbt-labsconfiguring-dbt-mcp-server
Generates MCP server configuration JSON and validates connectivity for dbt AI integrations.
dbt-labsadding-dbt-unit-test
Creates dbt unit test YAML definitions that mock model inputs and validate expected outputs.
dbt-labsworking-with-dbt-mesh
Assess breaking dbt model changes and implement versioning, contracts, access, and cross-project refs.
dbt-labsbuilding-dbt-semantic-layer
Create and modify dbt Semantic Layer models, metrics, dimensions, and entities in YAML.
dbt-labsusing-dbt-for-analytics-engineering
Builds, tests, and validates dbt models using ref(), source(), and dbt show.
dbt-labsusing-dbt-state
Configure and troubleshoot dbt State server-backed node reuse across dbt Core and Fusion.
dbt-labsanswering-natural-language-questions-with-dbt
Answers business questions by querying dbt Semantic Layer metrics or writing SQL against warehouse models.
dbt-labsrunning-dbt-commands
Execute dbt CLI commands with correct flavors, selectors, and flags.
dbt-labstroubleshooting-dbt-job-errors
Diagnose root causes of failed dbt Cloud jobs using MCP Admin API and logs.
dbt-labsmigrating-dbt-core-to-fusion
Migrate dbt Core projects to Fusion using dbtf debug, parse, autofix, and compile.
dbt-labsfetching-dbt-docs
Fetch dbt documentation pages via .md URLs and script-driven cached searches.
dbt-labsconfiguring-dbt-mcp-server
Configure the dbt MCP server for AI tools across local and remote environments.
dbt-labsadding-dbt-unit-test
Define and run dbt model unit tests with YAML fixtures.
dbt-labsbuilding-dbt-semantic-layer
Create and modify dbt Semantic Layer components using MetricFlow.
dbt-labsusing-dbt-for-analytics-engineering
Plan and execute dbt analytics engineering tasks with software engineering discipline.
dbt-labsanswering-natural-language-questions-with-dbt
Answer business questions by querying the semantic layer and dbt models.
Frequently Asked Questions About dbt Labs
FAQPage SchemaWhat specific data tasks can be performed using dbt Labs capabilities?▼
These capabilities enable data engineers to execute SQL transformations, define unit tests for models, build semantic layers for business metrics, and manage documentation. Users can troubleshoot job failures, migrate projects between environments, and query warehouse data to answer complex business questions through structured semantic interfaces.
Which technical personas benefit most from these dbt Labs skills?▼
Analytics engineers, data engineers, and business intelligence developers are the primary users. These professionals utilize these skills to apply software engineering rigor—such as version control, testing, and modularity—to their data warehouse environments, ensuring reliable and reproducible data pipelines for downstream reporting and analysis.
What are the prerequisites for implementing these dbt Labs skills?▼
Implementation requires an existing cloud data warehouse, such as Snowflake, BigQuery, or Redshift, and a configured project environment. Users must have familiarity with SQL, YAML configuration, and the ability to manage project dependencies through standard command-line interfaces or integrated cloud environments.