powerbi-review-semantic-modellisted

Warning: This skill is incomplete and still in progress, but may provide value already as-is -- Kurt # Reviewing Semantic Models Structured evaluation of Power BI semantic models against quality, performance, and best practice standards. Produces actionable findings with prioritized recommendations. ## Review Workflow ### Step 0: Gather Context Before analyzing TMDL, collect metadata and understand the business context. **Run the model info script:** ```bash python3 scripts/get_model_info.py -w <workspace-id> -m <model-id> ``` This returns: storage mode, model size, connected reports, deployment pipeline, endorsement status, sensitivity label, data sources, refresh schedule, last refresh, and capacity SKU. **Ask the user:** - What business process does this model represent? - Who are the primary consumers? (report developers, analysts, executives, AI/Copilot users?) - Are they the developer of both the model and its reports, or only one? - Is the model in development, testing, or production? - Where should findings be documented? (scratchpad, agent-docs, wiki, etc.) Understanding the business context is critical. A model for 3 analysts has different requirements than one consumed by Copilot across the organization. The audit categories and their severity shift based on this context. ### Step 1: Analyze Model Structure Inspect the model definition to evaluate its structure. The approach depends on available tooling -- use whatever is available to read the model's