PBI Model Quality

SkillFiles & storage

Use when the user asks for a model audit, model quality review, scorecard, bad-practices or best-practices assessment, or a review of star-schema fit, relationships, DAX maintainability, VertiPaq/storage risk, metadata hygiene, governance signals, or validation gaps in a Power BI semantic model. For diagnosing one slow query, use mcp-engine-dax-performance; for Copilot or natural-language readiness, use mcp-engine-ai-readiness; to execute the remediation backlog, use mcp-engine-refactoring.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the PBI Model Quality skill

What this skill tells your AI

The instructions your AI receives, as published by maxanatsko/mcp-engine-public in skills/mcp-engine-model-quality/SKILL.md and read by ahel’s review.

Use this skill to assess a connected Power BI semantic model and return a source-backed quality scorecard with prioritized recommendations. This is an assess-only workflow; do not apply model changes.

Start Here

  1. Confirm the current model context with SemanticOps MCP tools when needed.
  2. Gather metadata before querying data.
  3. Use list_model, manage_dependencies, run_query, manage_tests, and manage_model_connection where available.
  4. Use run_query only for small aggregated validation, performance analysis, VertiPaq/storage diagnostics, or access tests.
  5. Do not dump raw rows or sensitive values.
  6. Cite bundled Microsoft Learn and SQLBI source links for material findings.

Workflow

Assessment Areas

  • Model shape and star-schema fit.
  • Relationships and filter propagation risk.
  • DAX and semantic layer maintainability.
  • Storage and performance risk, including high-cardinality and unnecessary imported data.
  • Metadata, naming, descriptions, display folders, and field exposure.
  • Governance signals, including roles, sensitive-field exposure, and perspective-vs-security separation.
  • Validation and test coverage.

Guardrails

  • Do not call write operations from authoring or governance tools during the assessment.
  • Treat unavailable Pro diagnostics, browse-only mode, policy denials, or missing tool capabilities as scope limitations, not model defects.
  • Keep source-backed guidance nuanced; do not turn "generally recommended" practices into absolute rules when the source allows exceptions.
  • Mark inferred findings with lower confidence unless tool evidence confirms them.
  • End with concrete remediation steps and validation suggestions, not broad advice.

Output Standard

Return a compact quality assessment unless the user asks for raw detail:

  1. Executive score and quality band.
  2. Top 3 risks.
  3. Category scorecard.
  4. Findings grouped by critical, high, medium, and low severity.
  5. Prioritized remediation backlog.
  6. Validation/test recommendations.
  7. Source notes.

Signals

GitHub stars
256
Forks
65
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
mcp-engine-model-quality
Source
github.com/maxanatsko/mcp-engine-public