PBI Model Quality
SkillFiles & storageUse 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.
No other account needed.
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
- Confirm the current model context with SemanticOps MCP tools when needed.
- Gather metadata before querying data.
- Use
list_model,manage_dependencies,run_query,manage_tests, andmanage_model_connectionwhere available. - Use
run_queryonly for small aggregated validation, performance analysis, VertiPaq/storage diagnostics, or access tests. - Do not dump raw rows or sensitive values.
- Cite bundled Microsoft Learn and SQLBI source links for material findings.
Workflow
- Read model-quality-assessment-workflow for the inspection sequence, SemanticOps MCP tool usage, safety rules, and final output order.
- Read model-quality-scorecard when scoring the model, assigning severity, formatting findings, and building the remediation backlog.
- Read model-quality-rulebook for source-backed bad/questionable practice checks and recommended remediation language.
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:
- Executive score and quality band.
- Top 3 risks.
- Category scorecard.
- Findings grouped by critical, high, medium, and low severity.
- Prioritized remediation backlog.
- Validation/test recommendations.
- 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