Production-ready patterns for dbt model organization, testing, documentation, and incremental processing.
Works with
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
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondbt-transformation-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches dbt-transformation-patterns from wshobson/agents and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate dbt-transformation-patterns. Access via /dbt-transformation-patterns in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
sources/ Raw data definitions
↓
staging/ 1:1 with source, light cleaning
↓
intermediate/ Business logic, joins, aggregations
↓
marts/ Final analytics tables
| Layer | Prefix | Example |
|---|---|---|
| Staging | stg_ |
stg_stripe__payments |
| Intermediate | int_ |
int_payments_pivoted |
| Marts | dim_, fct_ |
dim_customers, fct_orders |
# 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
# 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
-- 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
-- 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
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: dbt-transformation-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: dbt-transformation-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
dbt-transformation-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
dbt-transformation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
dbt-transformation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend dbt-transformation-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend dbt-transformation-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: dbt-transformation-patterns is focused, and the summary matches what you get after install.
dbt-transformation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
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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