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.
Best use case
dbt is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using dbt should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/dbt/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dbt Compares
| Feature / Agent | dbt | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```sql
-- 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
```sql
-- 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
```yaml
# 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 datasetsRelated Skills
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