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Using Dbt For Analytics Engineering

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

Automates data transformations, modeling, and documentation using dbt to build robust and scalable analytics pipelines.

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 using-dbt-for-analytics-engineering -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

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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

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  • Pinned to a commit: passed

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

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Commit
0d094169f5
Manifest
skills/dbt/skills/using-dbt-for-analytics-engineering/SKILL.md
Scanned
9 files, 999 lines
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About this skill

What it does

This skill enables AI agents to leverage dbt (data build tool) for transforming raw data into reliable, reusable analytics assets. It automates the creation of complex SQL transformations and manages dependencies within a data warehouse environment.

When to use it

  • You need to transform messy source tables into clean, business-ready datasets for reporting or machine learning.
  • Your team requires version-controlled, repeatable data pipelines that ensure consistency across different environments.
  • You want to modularize SQL logic to avoid code duplication and improve maintainability as data models grow.

Key capabilities

  • Automates the generation of dbt projects with proper directory structures and configuration files.
  • Writes and executes complex SQL transformations including joins, aggregations, and window functions.
  • Manages package dependencies and ensures compatibility between different dbt versions and database targets.
  • Generates documentation and tests to validate data quality and lineage automatically.

Example prompts

  • "Create a dbt project that transforms raw sales logs into a daily aggregated revenue table for Snowflake."
  • "Write a dbt model to join customer profiles with transaction history and calculate lifetime value metrics."
  • "Set up a dbt pipeline that applies GDPR-compliant masking rules to personal identifiable information in our staging layer."

Tips & gotchas

Ensure your target database connection is configured correctly before attempting to run transformations, as dbt relies on active access to the data warehouse. Always validate generated SQL against your specific dialect (e.g., Snowflake, BigQuery) to avoid syntax errors in production environments.