A curated collection of Agent Skills for working with dbt, to help AI agents understand and execute dbt workflows more effectively.
65
82%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Risky
Do not use without reviewing
Never generate documentation which simply restates the entity's name. Describe why, not just what.
Inspect the data before writing documentation about it, using discovering-data.
Describe the grain of the table, its purpose and any edge cases
Bad:
models:
- name: active_customers
description: All customers who are activeGood:
models:
- name: active_customers
description: The `customers` table pre-filtered for easier analytics. One row per customer whose contract_expiry_date is null or in the futureCalculated fields should include a brief description of the transformation and its purpose.
Bad:
models:
- name: customers
columns:
- name: customer_id
description: The customer's identification numberGood:
models:
- name: customers
columns:
- name: customer_id
description: Users older than 2020-02-16 have `v1_` prefixed to their customer ID due to the platform migration.evals
skills
adding-dbt-unit-test
references
answering-natural-language-questions-with-dbt
building-dbt-semantic-layer
configuring-dbt-mcp-server
fetching-dbt-docs
scripts
migrating-dbt-core-to-fusion
running-dbt-commands
troubleshooting-dbt-job-errors
using-dbt-for-analytics-engineering