smithery.ai

dbt

dbt (data build tool) patterns for data transformation and analytics engineering. Use when building data models, implementing data quality tests, or managing data transformation pipelines.

First seen Apr 1, 2026

Installation

$ npx skills add https://smithery.ai

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai

Browse all from smithery.ai

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,707 B
  • docs SUMMARY.md 199 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

dbt Skill

This skill provides dbt patterns for analytics engineering.

Project Structure

dbt_project/
├── dbt_project.yml
├── models/
│   ├── staging/
│   │   └── stg_customers.sql
│   ├── intermediate/
│   │   └── int_customer_orders.sql
│   └── marts/
│       └── fct_orders.sql
├── seeds/
├── macros/
├── tests/
└── snapshots/

Model Patterns

Staging Models

-- models/staging/stg_customers.sql
with source as (
    select * from {{ source('raw', 'customers') }}
),

renamed as (
    select
        id as customer_id,
        lower(email) as email,
        created_at
    from source
)

select * from renamed

Incremental Models

-- models/marts/fct_orders.sql
{{
    config(
        materialized='incremental',
        unique_key='order_id'
    )
}}

select *
from {{ ref('stg_orders') }}
{% if is_incremental() %}
where updated_at > (select max(updated_at) from {{ this }})
{% endif %}

Testing

# models/schema.yml
models:
  - name: stg_customers
    columns:
      - name: customer_id
        tests:
          - unique
          - not_null
      - name: email
        tests:
          - unique

Best Practices

  • Use staging → intermediate → marts pattern
  • Source all raw data with source()
  • Reference models with ref()
  • Add documentation and tests
  • Use incremental models for large datasets