By dbt-labs
Build and test dbt models using SQL transformations, ref/source, and YAML unit tests; configure semantic layers for metrics, dimensions, and KPI queries; troubleshoot Cloud jobs with logs, API, and git; implement Mesh governance for contracts and cross-project refs; access docs; format CLI commands; generate MCP configs for VS Code integration.
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.
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.
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.
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.
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.
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A curated collection of Agent Skills for working with dbt. These skills help AI agents understand and execute dbt workflows more effectively.
Agent Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently.
These skills are not slash commands or user-invoked actions. Once installed, the agent automatically loads the relevant skill when your prompt matches its use case. Just describe what you need in natural language and the agent handles the rest. See skill invocation control for more details.
Add the dbt skills marketplace and install the plugins:
# Add the marketplace
/plugin marketplace add dbt-labs/dbt-agent-skills
# Install the dbt skills (analytics engineering, semantic layer, testing, etc.)
/plugin install dbt@dbt-agent-marketplace
# Install the migration skills (typically a one-off — not needed for every session)
/plugin install dbt-migration@dbt-agent-marketplace
Use the Vercel Skills CLI to install skills from this repository. Supports 30+ AI agents including Cursor, Cline, GitHub Copilot, and others.
# Preview available skills
npx skills add dbt-labs/dbt-agent-skills --list
# Install all skills
npx skills add dbt-labs/dbt-agent-skills
# Install only the dbt skills (analytics engineering, semantic layer, etc.)
npx skills add dbt-labs/dbt-agent-skills/skills/dbt
# Install only the migration skills
npx skills add dbt-labs/dbt-agent-skills/skills/dbt-migration
# Install a specific skill
npx skills add dbt-labs/dbt-agent-skills --skill using-dbt-for-analytics-engineering
# Install globally (available in all projects, stored in ~/.<agent>/skills/)
npx skills add dbt-labs/dbt-agent-skills --global
# Check for updates
npx skills check
# Update installed skills
npx skills update
Install skills using Tessl, a package manager for agent skills:
# Install all skills
tessl install dbt-labs/dbt-agent-skills
# Install a specific skill
tessl install dbt-labs/dbt-agent-skills --skill using-dbt-for-analytics-engineering
# Install from GitHub directly
tessl install github:dbt-labs/dbt-agent-skills
Browse the tile on the Tessl registry.
These skills work with AI agents that support the Agent Skills format.
| Skill | Description |
|---|---|
using-dbt-for-analytics-engineering | Build and modify dbt models, debug errors, explore data sources, write tests |
adding-dbt-unit-test | Add unit tests for dbt models, practice test-driven development |
building-dbt-semantic-layer | Create semantic models, metrics, and dimensions with MetricFlow |
answering-natural-language-questions-with-dbt | Answer business questions by querying the semantic layer |
working-with-dbt-mesh | Implement dbt Mesh governance (contracts, access, groups, versions) and cross-project collaboration |
troubleshooting-dbt-job-errors | Diagnose and resolve dbt platform job failures |
configuring-dbt-mcp-server | Set up the dbt MCP server for Claude, Cursor, or VS Code |
fetching-dbt-docs | Look up dbt documentation efficiently |
running-dbt-commands | Run dbt CLI commands with correct flags, selectors, and parameter formats |
These skills are typically used once during a migration project rather than in every agent session.
| Skill | Description |
|---|---|
migrating-dbt-core-to-fusion | Migrate dbt projects from dbt Core to the Fusion engine |
migrating-dbt-project-across-platforms | Migrate dbt projects across data platforms |
Most skills assume:
dbt_project.yml existsSome skills like fetching-dbt-docs and configuring-dbt-mcp-server can be used without an existing project.
Skills for migrating dbt projects — moving from dbt Core to the Fusion engine or across data platforms.
Miscellaneous skills for dbt.
npx claudepluginhub dbt-labs/dbt-agent-skills --plugin dbtSkills for migrating dbt projects — moving from dbt Core to the Fusion engine or across data platforms.
Skills and tools powered by the Honeydew MCP that help coding agents query data and build semantic models
Semantic SQL compiler — compile .view.yml schema definitions into dialect-specific SQL. Unix-philosophy CLI designed as a tool-use interface for LLMs.
Explore, query, model, embed, and manage Omni Analytics through the REST API and embed SDK. Includes 9 skills, 3 specialized agents, and 3 context rules for model exploration, querying, model building, content browsing, content building, embedding, AI optimization, AI eval, and administration.
Spec-Driven Development framework for Data Engineering — 58 agents, 24 KB domains, 5-phase SDD workflow, 31 commands
Editorial "Data Engineering" bundle for Claude Code from Antigravity Awesome Skills.