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Running Dbt Commands

Official
dbt-labs·vlatest·data·Apache-2.0

This skill executes dbt commands directly from your workflow, streamlining transformations and automating your data build process for faster insights.

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add dbt-labs/dbt-agent-skills --skill running-dbt-commands -a claude-code
  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

dbt-labs

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned dbt-labs/dbt-agent-skills at 0d094169f5.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Scanned
1 file, 154 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to execute core Data Build Tool (dbt) commands directly within a project environment. It facilitates the automation of data transformation workflows by allowing agents to run specific operations like compiling, testing, and running models without manual intervention.

When to use it

  • Automating nightly data pipeline execution for scheduled reporting.
  • Triggering model compilation and validation before deploying changes to production.
  • Running specific tests to verify data quality after a schema update.
  • Executing dbt docs generate to automatically refresh project documentation.

Key capabilities

  • Executes standard dbt CLI commands (e.g., run, test, compile, seed).
  • Integrates with existing dbt project configurations and profiles.
  • Supports running transformations against configured target environments.
  • Enables programmatic control over data modeling lifecycle stages.

Example prompts

  • "Run the full test suite for the staging schema to check for recent regressions."
  • "Compile all models in the project and report any parsing errors found."
  • "Execute the dbt docs generate command and save the output to the documentation folder."

Tips & gotchas

Ensure your AI agent has the correct environment variables set up, particularly those defining database connections within the dbt profile. The skill relies on a pre-configured dbt project structure; it will fail if the necessary dbt_project.yml or connection profiles are missing or misconfigured in the execution context.