dbt-model-builder

Name: dbt-model-builder
Availability: InStock
Author: zazu-22

Build dbt models with Kimball dimensional modeling and 2×2 stat patterns.

Updated Aug 27, 2026

Search Tags:#dbt #data-modeling #external-parquet #kimball #2x2 model

Ready to run

npx skills add https://github.com/zazu-22/ff_data_analytics --skill dbt-model-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dbt-model-builder
Source: https://github.com/zazu-22/ff_data_analytics/tree/main/.claude/_archive_skills/dbt-model-builder
Command: npx skills add https://github.com/zazu-22/ff_data_analytics --skill dbt-model-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

This Skill guides users to create complete dbt models following Kimball dimensional modeling and the 2x2 stat model (actuals vs projections × real-world vs fantasy). It emphasizes staging, facts, dimensions, external Parquet usage, and per-model YAML documentation.

Core Features & Use Cases

Workflow for creating staging models, core facts, dimensions, and analytical marts
Per-model YAML documentation with tests (dbt 1.10+ syntax)
External Parquet integration and partitioning patterns
Phase-driven approach: staging, facts, and marts with lineage and testing

Quick Start

Begin by creating a staging model using the provided templates and validate with dbt tests. Then build a fact and a mart following the consolidated 2x2 model design.

Frequently Asked Questions about dbt-model-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build dbt models following Kimball dimensional modeling?▼

Kimball dimensional modeling structures data into staging, fact, and dimension tables. This Skill provides templates and workflows to build these models with explicit grain declarations, surrogate keys, and comprehensive tests using dbt 1.10+ syntax, delivering analytics-ready structures for dimensional schemas.

What is the 2×2 stat model pattern and when should I use it?▼

The 2×2 stat model separates actuals from projections and real-world from fantasy scenarios in your data warehouse. This Skill applies the pattern to FF Analytics fact and mart models, enabling side-by-side comparison of different data states within a single consolidated structure.

How do I integrate external Parquet files into my dbt models?▼

External Parquet integration in dbt loads Parquet files as sources without materializing them in the warehouse. This Skill configures external Parquet sources with partitioning by season and week, stage them in dbt, and join them with fact and dimension models for downstream analytics.

Can I apply SCD Type 2 slowly changing dimensions with dbt?▼

SCD Type 2 tracks historical changes by maintaining multiple rows per entity with effective date ranges. This Skill includes explicit patterns for SCD Type 2 implementation in dimension models, with surrogate keys and comprehensive tests ensuring referential integrity across fact tables.

How do I set up per-model YAML documentation and testing in dbt?▼

Per-model YAML documentation in dbt defines columns, descriptions, and tests at the model level. This Skill provides templates for not_null, unique combination, and foreign key integrity tests using dbt 1.10+ syntax, ensuring data quality and lineage visibility throughout your models.

What's the best approach to partition fact and dimension tables by time?▼

Time-based partitioning optimizes query performance and data organization. This Skill applies season and week-level partitioning to fact models and analytical marts, balancing query efficiency with grain explicitness required by the 2×2 stat model.