dbt-transformation-patterns

wshobson/agents · updated Apr 8, 2026

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

Production-ready patterns for dbt model organization, testing, documentation, and incremental processing.

  • Implements medallion architecture with staging, intermediate, and marts layers using consistent naming conventions (stg_, int_, dim_, fct_) and materialization strategies
  • Covers source definitions with freshness checks, data quality tests (unique, not_null, relationships), and comprehensive YAML documentation for lineage tracking
  • Provides incremental model patterns including dele
skill.md

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

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

Patterns

Pattern 1: Source Definitions

# models/staging/stripe/_stripe__sources.yml
version: 2

sources:
  - name: stripe
    description: Raw Stripe data loaded via Fivetran
    database: raw
    schema: stripe
    loader: fivetran
    loaded_at_field: _fivetran_synced
    freshness:
      warn_after: { count: 12, period: hour }
      error_after: { count: 24, period: hour }
    tables:
      - name: customers
        description: Stripe customer records
        columns:
          - name: id
            description: Primary key
            tests:
              - unique
              - not_null
          - name: email
            description: Customer email
          - name: created
            description: Account creation timestamp

      - name: payments
        description: Stripe payment transactions
        columns:
          - name: id
            tests:
              - unique
              - not_null
          - name: customer_id
            tests:
              - not_null
              - relationships:
                  to: source('stripe', 'customers')
                  field: id

Pattern 2: Staging Models

-- models/staging/stripe/stg_stripe__customers.sql
with source as (
    select * from {{ source('stripe', 'customers') }}
),

renamed as (
    select
        -- ids
        id as customer_id,

        -- strings
        lower(email) as email,
        name as customer_name,

        -- timestamps
        created as created_at,

        -- metadata
        _fivetran_synced as _loaded_at

    from source
)

select * from renamed
-- models/staging/stripe/stg_stripe__payments.sql
{{
    config(
        materialized='incremental',
        unique_key='payment_id',
        on_schema_change='append_new_columns'
    )
}}

with source as (
    select * from {{ source('stripe', 'payments') }}

    {% if is_incremental() %}
    where _fivetran_synced > (select max(_loaded_at) from {{ this }})
    {% endif %}
),

renamed as (
    select
        -- ids
        id as payment_id,
        customer_id,
        invoice_id,

        -- amounts (convert cents to dollars)
        amount / 100.0 as amount,
        amount_refunded / 100.0 as amount_refunded,

        -- status
        status as payment_status,

        -- timestamps
        created as created_at,

        -- metadata
        _fivetran_synced as _loaded_at

    from source
)

select * from renamed

Pattern 3: Intermediate Models

-- models/intermediate/finance/int_payments_pivoted_to_customer.sql
with payments as (
    select * from {{ ref('stg_stripe__payments') }}
),

customers as (
    select * from {{ ref('stg_stripe__customers') }}
),

payment_summary as (
    select
        customer_id,
        count(*) as total_payments,
        count(case when payment_status = 'succeeded' then 1 end) as successful_payments,
        sum(case when payment_status = 'succeeded' then amount else 0 end) as total_amount_paid,
        min(created_at) as first_payment_at,
        max(created_at) as last_payment_at
    from payments
    group by customer_id
)

select
how to use dbt-transformation-patterns

How to use dbt-transformation-patterns on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add dbt-transformation-patterns
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches dbt-transformation-patterns from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
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│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/dbt-transformation-patterns

Reload or restart Cursor to activate dbt-transformation-patterns. Access the skill through slash commands (e.g., /dbt-transformation-patterns) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.533 reviews
  • Ganesh Mohane· Dec 24, 2024

    Keeps context tight: dbt-transformation-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Nikhil Anderson· Dec 24, 2024

    Keeps context tight: dbt-transformation-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Dev Dixit· Dec 12, 2024

    dbt-transformation-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakshi Patil· Nov 15, 2024

    dbt-transformation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Camila Sharma· Nov 15, 2024

    dbt-transformation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 11, 2024

    I recommend dbt-transformation-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Alexander Yang· Nov 11, 2024

    I recommend dbt-transformation-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Alexander Mehta· Nov 3, 2024

    Solid pick for teams standardizing on skills: dbt-transformation-patterns is focused, and the summary matches what you get after install.

  • Emma Abebe· Oct 22, 2024

    dbt-transformation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 6, 2024

    Solid pick for teams standardizing on skills: dbt-transformation-patterns is focused, and the summary matches what you get after install.

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