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designing-semantic-model

Generate Fabric semantic model definitions from dbt project artifacts.

Updated Apr 18, 2026
Search Tags:#design#architecture#dbt#modeling#fabric#semantic-model#tmdl
One-click install
npx skills add https://github.com/accelerate-data/fabric-semantic-model --skill designing-semantic-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: designing-semantic-model
Source: https://github.com/accelerate-data/fabric-semantic-model/tree/main/skills/designing-semantic-model
Command: npx skills add https://github.com/accelerate-data/fabric-semantic-model --skill designing-semantic-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designs and scaffolds Fabric semantic models by translating dbt artifacts into TMDL-ready definitions, enabling consistent, production-ready reporting structures.

Core Features & Use Cases

  • Ingests dbt project artifacts (dbt_project.yml, models/, manifest.json) to derive a semantic model skeleton.
  • Generates DirectLake-ready table definitions and star-schema mappings (fct/dim -> Sales/Calendar) with relationship scaffolding.
  • Provides starter measures and validation steps to ensure mapping accuracy and deployment readiness.

Quick Start

Generate a starter Fabric semantic model outline from a basic dbt project.

Frequently Asked Questions about designing-semantic-model

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

FAQPage Schema
How do I convert a dbt project into a Fabric semantic model?▼

To convert a dbt project into a Fabric semantic model, ingest dbt artifacts like manifest.json to generate TMDL-ready definitions with star-schema mappings and DirectLake deployment requirements.

Can I generate DirectLake-ready fact and dimension tables from dbt models?▼

Yes, you can generate DirectLake-ready fact and dimension tables by translating dbt models into TMDL definitions, applying star-schema organization to scaffold final reporting structures and relationships.

What is TMDL and when do I need it for Fabric semantic modeling?▼

TMDL is the definition format used to scaffold Fabric semantic models. You need it when translating dbt artifacts into production-ready reporting structures to ensure deployment readiness and mapping accuracy.

Does dbt-to-TMDL mapping support star-schema organization for reporting?▼

Yes, dbt-to-TMDL mapping enforces star-schema organization by scaffolding fact and dimension tables from dbt models, establishing necessary relationships for consistent reporting structures.

What's the best way to scaffold relationships in a Fabric semantic model from dbt?▼

The best way to scaffold relationships is by ingesting dbt project artifacts to derive a semantic model skeleton, then applying TMDL definitions to map fact and dimension tables accurately.

Do I need a manifest.json file to generate a Fabric semantic model from dbt?▼

Yes, you need the manifest.json file along with dbt_project.yml and models to derive the semantic model skeleton, ensuring accurate TMDL-ready definitions and DirectLake configuration scaffolding.

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