Skip to content
TrustedSkillsbeta
Back to all skills

Fetching Dbt Docs

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

This skill automatically fetches and updates dbt documentation, ensuring you always have the latest information for your projects.

Repository

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 fetching-dbt-docs -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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: docs.getdbt.com.

    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.

    Hosts contacted

    docs.getdbt.comundeclared

    What it found

    • skills/dbt/skills/fetching-dbt-docs/scripts/search-dbt-docs.sh:11contacts docs.getdbt.com
      FULL_URL="https://docs.getdbt.com/llms-full.txt"

  • 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 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.

Commit
0d094169f5
Manifest
skills/dbt/skills/fetching-dbt-docs/SKILL.md
Scanned
2 files, 271 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 retrieve and parse documentation from dbt projects, allowing them to understand model definitions, column descriptions, and relationship metadata without manual inspection. It bridges the gap between raw data pipelines and semantic understanding for automated analysis.

When to use it

  • An agent needs to explain the lineage of a specific metric to a stakeholder.
  • You want an AI to audit data quality rules defined in your dbt project files.
  • An agent must generate SQL queries that respect existing table constraints and foreign keys.
  • You need to quickly summarize the purpose of a complex transformation model before running it.

Key capabilities

  • Fetches documentation from local or remote dbt projects.
  • Parses metadata including models, sources, macros, and exposures.
  • Extracts column-level descriptions and data types.
  • Identifies relationships between tables based on defined references.

Example prompts

  • "Explain the logic behind the customer_lifetime_value model using the project documentation."
  • "List all columns in the orders table that have a description mentioning 'revenue'."
  • "What are the upstream dependencies for the monthly_sales_summary model?"

Tips & gotchas

Ensure your dbt project has valid docs.yml files or descriptions enabled in dbt_project.yml, as the skill relies on this metadata. The agent cannot access database runtime data; it only reads static documentation artifacts generated by dbt.