From dbt-toolkit
Generates schema tests, data quality tests, and freshness checks for dbt models and sources. Use when implementing data validation in dbt projects.
How this skill is triggered — by the user, by Claude, or both
Slash command
/dbt-toolkit:test-generatorThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate comprehensive tests for dbt models including schema tests, data quality tests, and freshness checks.
Generate comprehensive tests for dbt models including schema tests, data quality tests, and freshness checks.
# models/schema.yml
version: 2
models:
- name: stg_orders
description: Staging orders table
columns:
- name: order_id
description: Unique order identifier
tests:
- unique
- not_null
- name: customer_id
description: Customer reference
tests:
- not_null
- relationships:
to: ref('stg_customers')
field: customer_id
- name: order_status
description: Order status
tests:
- accepted_values:
values: ['pending', 'processing', 'shipped', 'delivered', 'cancelled']
- name: order_total
description: Total order amount
tests:
- not_null
- dbt_utils.accepted_range:
min_value: 0
-- tests/assert_positive_revenue.sql
select
order_id,
order_total
from {{ ref('fct_orders') }}
where order_total < 0
# models/sources.yml
version: 2
sources:
- name: raw
database: analytics
schema: raw_data
tables:
- name: orders
description: Raw orders data
freshness:
warn_after: {count: 12, period: hour}
error_after: {count: 24, period: hour}
loaded_at_field: created_at
# Run all tests
dbt test
# Run tests for specific model
dbt test --select stg_orders
# Run specific test type
dbt test --select test_type:unique
dbt test --select test_type:not_null
Schema tests:
dbt_utils tests:
Custom tests:
npx claudepluginhub p/armanzeroeight-dbt-toolkit-plugins-dbt-toolkitAdds schema tests and data quality validation to dbt models. Guides matching existing test patterns and YAML style before adding new tests.
Generates dbt test configurations and code with step-by-step guidance, best practices, and validation for data pipeline testing.
Implements data quality validation with Great Expectations, dbt tests, and data contracts. Use for building data quality pipelines, validation rules, or establishing data contracts.