fabric-tmdl-api

Name: fabric-tmdl-api
Availability: InStock
Author: wardawgmalvicious

Automate CRUD operations on the Fabric Semantic Model Definition API.

2|1|Updated May 5, 2026

Search Tags:#TMDL #Semantic Model #Fabric API #Direct Lake #API Management

Ready to run

npx skills add https://github.com/wardawgmalvicious/claude-config --skill fabric-tmdl-api

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fabric-tmdl-api
Source: https://github.com/wardawgmalvicious/claude-config/tree/main/skills/fabric-tmdl-api
Command: npx skills add https://github.com/wardawgmalvicious/claude-config --skill fabric-tmdl-api

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexity of managing the Fabric Semantic Model Definition API, providing a concise guide to create, retrieve, and update semantic models with detailed instructions and best practices.

Core Features & Use Cases

API Navigation: Covers the Fabric API for CRUD operations on semantic model definitions.
Update Strategies: Explains how to handle updates and the importance of including all parts.
Base64 Encoding: Details base64 encoding requirements for TMDL content.
Direct Lake Configuration: Offers guidance on Direct Lake partition configuration and usage.
Use Case: Streamline the process of creating and managing semantic models for Fabric, ensuring compatibility and optimal performance.

Quick Start

Use the fabric-tmdl-api skill to create a new semantic model using the createItemWithDefinition API.

Frequently Asked Questions about fabric-tmdl-api

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

FAQPage Schema

How do I create a Fabric semantic model definition using the API?▼

Create a Fabric semantic model definition by using the `createItemWithDefinition` API to submit your TMDL content, which automates the initial setup and deployment of the model structure.

Why do Fabric Semantic Model API updates fail when modifying TMDL?▼

Fabric Semantic Model API updates fail when modifying TMDL if you do not include all parts of the definition during the update operation. You must bundle the entire model structure to ensure compatibility and prevent partial updates.

Does the Fabric Semantic Model Definition API require base64 encoding for TMDL content?▼

Yes, the Fabric Semantic Model Definition API requires base64 encoding for TMDL content. You must encode your TMDL scripts into base64 format before sending them through the API to ensure successful processing and deployment.

How do I configure Direct Lake partitions in a Fabric semantic model via API?▼

Configure Direct Lake partitions in a Fabric semantic model by following specific API configuration requirements during model creation or update. This ensures your semantic model directly queries the underlying lakehouse data for optimal performance.

Can I automate CRUD operations for Fabric semantic models?▼

Yes, you can automate CRUD operations for Fabric semantic models using the Fabric API. This covers creating, reading, updating, and deleting semantic model definitions to streamline management and ensure compatibility across your Fabric environment.

What is the best way to handle TMDL updates for Direct Lake semantic models?▼

The best way to handle TMDL updates for Direct Lake semantic models is to include all definition parts in the API request and verify Direct Lake partition configuration requirements. This maintains optimal performance and prevents update failures.