From devops-data
Provides SQLMesh patterns for data transformation with column-level lineage, virtual environments, incremental models, audits, and automatic DAG inference for efficient data pipelines.
npx claudepluginhub jpoutrin/product-forge --plugin devops-dataThis skill uses the workspace's default tool permissions.
This skill provides SQLMesh patterns for data transformation.
Provides dbt patterns for model organization in staging, intermediate, and marts layers, including project structure, sources, configs, SQL examples, and testing setups. Useful for data transformation projects.
Provides production-ready dbt patterns for model organization into layers, testing strategies, documentation, incremental processing, and project structure.
Provides production-ready dbt patterns for analytics engineering including model layers (staging, intermediate, marts), naming conventions, testing, documentation, incremental models, and project structure. Use for data transformations and models.
Share bugs, ideas, or general feedback.
This skill provides SQLMesh patterns for data transformation.
sqlmesh_project/
├── config.yaml
├── models/
│ ├── staging/
│ │ └── stg_customers.sql
│ └── marts/
│ └── dim_customers.sql
├── macros/
├── seeds/
├── audits/
└── tests/
-- models/staging/stg_customers.sql
MODEL (
name staging.stg_customers,
kind INCREMENTAL_BY_TIME_RANGE (
time_column created_at
),
cron '@daily'
);
SELECT
id AS customer_id,
LOWER(email) AS email,
created_at
FROM raw.customers
WHERE created_at BETWEEN @start_ds AND @end_ds
| Kind | Use Case |
|---|---|
FULL | Complete refresh each run |
INCREMENTAL_BY_TIME_RANGE | Time-based incremental |
INCREMENTAL_BY_UNIQUE_KEY | Key-based merge |
VIEW | Virtual table |
SEED | Static CSV data |
# Create a virtual environment for testing
sqlmesh plan dev
# Apply to production
sqlmesh plan prod
-- audits/no_nulls.sql
AUDIT (
name assert_no_null_customer_id,
model staging.stg_customers
);
SELECT * FROM staging.stg_customers
WHERE customer_id IS NULL