Dbt Transformation Patterns Agent Skill
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/wshobson/agents --skill dbt-transformation-patternsThe instruction itself
10 sections, as written by the authordbt Transformation Patterns
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
When to Use This Skill
- Building data transformation pipelines with dbt
- Organizing models into staging, intermediate, and marts layers
- Implementing data quality tests
- Creating incremental models for large datasets
- Documenting data models and lineage
- Setting up dbt project structure
Core Concepts
1. Model Layers (Medallion Architecture)
sources/ Raw data definitions
↓
staging/ 1:1 with source, light cleaning
↓
intermediate/ Business logic, joins, aggregations
↓
marts/ Final analytics tables
2. Naming Conventions
| Layer | Prefix | Example |
| ------------ | -------------- | ----------------------------- |
| Staging | stg_ | stg_stripe__payments |
| Intermediate | int_ | int_payments_pivoted |
| Marts | dim_, fct_ | dim_customers, fct_orders |
Quick Start
# dbt_project.yml
name: "analytics"
version: "1.0.0"
profile: "analytics"
model-paths: ["models"]
analysis-paths: ["analyses"]
test-paths: ["tests"]
seed-paths: ["seeds"]
macro-paths: ["macros"]
vars:
start_date: "2020-01-01"
models:
analytics:
staging:
+materialized: view
+schema: staging
intermediate:
+materialized: ephemeral
marts:
+materialized: table
+schema: analytics
# Project structure
models/
├── staging/
│ ├── stripe/
│ │ ├── _stripe__sources.yml
│ │ ├── _stripe__models.yml
│ │ ├── stg_stripe__customers.sql
│ │ └── stg_stripe__payments.sql
│ └── shopify/
│ ├── _shopify__sources.yml
│ └── stg_shopify__orders.sql
├── intermediate/
│ └── finance/
│ └── int_payments_pivoted.sql
└── marts/
├── core/
│ ├── _core__models.yml
│ ├── dim_customers.sql
│ └── fct_orders.sql
└── finance/
└── fct_revenue.sql
Detailed patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Best Practices
Do's
- Use staging layer - Clean data once, use everywhere
- Test aggressively - Not null, unique, relationships
- Document everything - Column descriptions, model descriptions
- Use incremental - For tables > 1M rows
- Version control - dbt project in Git
Don'ts
- Don't skip staging - Raw → mart is tech debt
- Don't hardcode dates - Use
{{ var('start_date') }} - Don't repeat logic - Extract to macros
- Don't test in prod - Use dev target
- Don't ignore freshness - Monitor source data
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Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
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How to use it
Take wshobson/dbt-transformation-patterns from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.