dbt-transformation-patterns▌
wshobson/agents · updated Apr 8, 2026
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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
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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches dbt-transformation-patterns from GitHub repository wshobson/agents and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★33 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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