MCPInstall

dbt-transformation-patterns

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.

Installs
11K
Last 24h
+29
Trending rank
#4476
Security
Warn

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/wshobson/agents --skill dbt-transformation-patterns
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads dbt-transformation-patterns automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·This skill contains standard documentation, schemas, and configurations for data build tool (dbt) projects. It demonstrates best practices for organizing models, macros, testing, and implementation, and presents no security risks.
Socket PassNo alerts
Snyk PassLOW ·Risk: LOW · No issues
Runlayer WarnMEDIUM ·1/1 file flagged
ZeroLeaks PassNONE ·Score: 93/100 · 2 sections analyzed

SKILL.md

The instructions your agent reads when the skill is triggered.

dbt 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

LayerPrefixExample
Stagingstg_stg_stripe__payments
Intermediateint_int_payments_pivoted
Martsdim_, 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

FAQ

What does the dbt-transformation-patterns skill do?

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.

How do I install dbt-transformation-patterns?

Run npx skills add https://github.com/wshobson/agents --skill dbt-transformation-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/dbt-transformation-patterns/ (personal) or .claude/skills/dbt-transformation-patterns/ (project).

Which AI agents can use dbt-transformation-patterns?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is dbt-transformation-patterns safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains dbt-transformation-patterns?

It is published by wshobson in wshobson/agents. It has 11,049 recorded installs.

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