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pbi-tmdl-analysis

Parse TMDL model files into structured schemas and compact LLM context.

Updated Aug 27, 2026
Search Tags:#parsing#context-building#semantic-models#offline-analysis#schema-extraction#power-bi#tmdl
One-click install
npx skills add https://github.com/fabioc-aloha/PBI-Visual-Assistant --skill pbi-tmdl-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pbi-tmdl-analysis
Source: https://github.com/fabioc-aloha/PBI-Visual-Assistant/tree/main/.github/skills/pbi-tmdl-analysis
Command: npx skills add https://github.com/fabioc-aloha/PBI-Visual-Assistant --skill pbi-tmdl-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Parses TMDL semantic model definitions to produce structured schemas and compact, LLm-ready contexts for Power BI design, Q&A, and model health checks.

Core Features & Use Cases

  • TMDL parsing: extract model, tables, columns, measures, relationships, and hierarchies into a coherent schema.
  • Context construction: build compact markdown representations for efficient LLM loading and downstream tasks like Q&A and visual recommendations.
  • Offline analysis: enables model health checks and validation without cloud connections.

Quick Start

Provide a TMDL model file to parse, extract the schema, and prepare a compact LLM-friendly context for downstream tasks.

Frequently Asked Questions about pbi-tmdl-analysis

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

FAQPage Schema
How do I parse TMDL files to extract a Power BI semantic model schema?▼

To parse TMDL files, you provide the model.tmdl and related definition files to extract tables, columns, measures, relationships, and hierarchies into a structured schema for downstream AI-assisted Power BI design.

Can I extract Power BI measures and relationships from TMDL for offline analysis?▼

Yes, you can extract measures and relationships from TMDL for offline analysis. The parsing process enforces faithful extraction of semantic model components without requiring a live cloud connection.

What is the best way to convert a TMDL semantic model into an LLM-ready context?▼

The best way to convert a TMDL semantic model into an LLM-ready context is by parsing the definition files to build a compact markdown representation, enabling efficient loading for downstream Q&A and visual recommendations.

Does parsing TMDL require a live Power BI cloud connection?▼

No, parsing TMDL does not require a live Power BI cloud connection. It enables offline analysis and model health checks by directly extracting structured schemas from local TMDL definition files.

How do TMDL parsed schemas support AI-assisted Power BI design?▼

TMDL parsed schemas support AI-assisted Power BI design by producing a reliable, compact context that large language models use for downstream tasks like measure creation, Q&A, and visual recommendations.

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