Generates dbt staging, intermediate, and model code with tests and documentation.
npx skills add https://github.com/jpoutrin/product-forge --skill dbt-jpoutrin
Please help me install this Agent Skill. Skill: dbt Source: https://github.com/jpoutrin/product-forge/tree/main/plugins/devops-data/skills/dbt Command: npx skills add https://github.com/jpoutrin/product-forge --skill dbt-jpoutrin
Building and managing data transformation logic for analytics often lacks structure, version control, and testing capabilities, leading to inconsistent data models and unreliable insights. This Skill provides dbt (data build tool) patterns to streamline analytics engineering.
unique, not_null) directly within your dbt project.source(), referencing models with ref(), and adding documentation.Use the dbt skill to generate a dbt staging model for a raw.customers table, renaming id to customer_id.
High-intent search queries and answers about installing and using this skill.
Data transformation with dbt organizes SQL logic into staging, intermediate, and marts models using source() and ref() functions. You define models as SQL queries, configure materialization (table, view, incremental), and dbt handles dependencies and execution order automatically.
dbt projects use a layered structure: staging models clean raw data, intermediate models combine and reshape data, and marts models serve analytics use cases. This separation enforces data quality checks and makes models reusable across your analytics workflow.
Incremental models process only new or changed data by appending rows since the last run. You specify a unique key and dbt-managed timestamp to identify updates, reducing compute time and costs when processing large fact tables or event streams.
Yes. dbt includes built-in tests like unique and not_null, plus custom tests you define. Tests run after model builds to verify schema constraints, referential integrity, and business logic, catching data issues before downstream analytics.
Yes. dbt models are written in SQL (or Jinja templates). You write SELECT statements to define transformations, then dbt compiles and deploys them. Strong SQL skills accelerate development, though dbt's macros and documentation support learning.
dbt uses ref() to reference upstream models and source() for raw tables, creating a directed acyclic graph (DAG). dbt automatically resolves execution order, ensures models run after dependencies complete, and manages schema creation.
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