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Merge pull request #657 from AlahmadiQ8/power-bi-modeling-skill
Add Power BI modeling skill
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‎docs/README.skills.md‎

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| [nuget-manager](../skills/nuget-manager/SKILL.md) | Manage NuGet packages in .NET projects/solutions. Use this skill when adding, removing, or updating NuGet package versions. It enforces using `dotnet` CLI for package management and provides strict procedures for direct file edits only when updating versions. | None |
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| [penpot-uiux-design](../skills/penpot-uiux-design/SKILL.md) | Comprehensive guide for creating professional UI/UX designs in Penpot using MCP tools. Use this skill when: (1) Creating new UI/UX designs for web, mobile, or desktop applications, (2) Building design systems with components and tokens, (3) Designing dashboards, forms, navigation, or landing pages, (4) Applying accessibility standards and best practices, (5) Following platform guidelines (iOS, Android, Material Design), (6) Reviewing or improving existing Penpot designs for usability. Triggers: "design a UI", "create interface", "build layout", "design dashboard", "create form", "design landing page", "make it accessible", "design system", "component library". | `references/accessibility.md`<br />`references/component-patterns.md`<br />`references/platform-guidelines.md`<br />`references/setup-troubleshooting.md` |
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| [plantuml-ascii](../skills/plantuml-ascii/SKILL.md) | Generate ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag | None |
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| [powerbi-modeling](../skills/powerbi-modeling/SKILL.md) | Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance. | `references/MEASURES-DAX.md`<br />`references/PERFORMANCE.md`<br />`references/RELATIONSHIPS.md`<br />`references/RLS.md`<br />`references/STAR-SCHEMA.md` |
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| [prd](../skills/prd/SKILL.md) | Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis. | None |
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| [refactor](../skills/refactor/SKILL.md) | Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements. | None |
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| [scoutqa-test](../skills/scoutqa-test/SKILL.md) | This skill should be used when the user asks to "test this website", "run exploratory testing", "check for accessibility issues", "verify the login flow works", "find bugs on this page", or requests automated QA testing. Triggers on web application testing scenarios including smoke tests, accessibility audits, e-commerce flows, and user flow validation using ScoutQA CLI. IMPORTANT: Use this skill proactively after implementing web application features to verify they work correctly - don't wait for the user to ask for testing. | None |

‎skills/powerbi-modeling/SKILL.md‎

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---
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name: powerbi-modeling
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description: 'Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.'
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---
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# Power BI Semantic Modeling
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Guide users in building optimized, well-documented Power BI semantic models following Microsoft best practices.
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## When to Use This Skill
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Use this skill when users ask about:
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- Creating or optimizing Power BI semantic models
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- Designing star schemas (dimension/fact tables)
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- Writing DAX measures or calculated columns
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- Configuring table relationships (cardinality, cross-filter)
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- Implementing row-level security (RLS)
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- Naming conventions for tables, columns, measures
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- Adding descriptions and documentation to models
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- Performance tuning and optimization
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- Calculation groups and field parameters
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- Model validation and best practice checks
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**Trigger phrases:** "create a measure", "add relationship", "star schema", "optimize model", "DAX formula", "RLS", "naming convention", "model documentation", "cardinality", "cross-filter"
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## Prerequisites
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### Required Tools
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- **Power BI Modeling MCP Server**: Required for connecting to and modifying semantic models
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- Enables: connection_operations, table_operations, measure_operations, relationship_operations, etc.
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- Must be configured and running to interact with models
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### Optional Dependencies
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- **Microsoft Learn MCP Server**: Recommended for researching latest best practices
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- Enables: microsoft_docs_search, microsoft_docs_fetch
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- Use for complex scenarios, new features, and official documentation
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## Workflow
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### 1. Connect and Analyze First
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Before providing any modeling guidance, always examine the current model state:
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```
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1. List connections: connection_operations(operation: "ListConnections")
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2. If no connection, check for local instances: connection_operations(operation: "ListLocalInstances")
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3. Connect to the model (Desktop or Fabric)
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4. Get model overview: model_operations(operation: "Get")
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5. List tables: table_operations(operation: "List")
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6. List relationships: relationship_operations(operation: "List")
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7. List measures: measure_operations(operation: "List")
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```
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### 2. Evaluate Model Health
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After connecting, assess the model against best practices:
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- **Star Schema**: Are tables properly classified as dimension or fact?
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- **Relationships**: Correct cardinality? Minimal bidirectional filters?
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- **Naming**: Human-readable, consistent naming conventions?
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- **Documentation**: Do tables, columns, measures have descriptions?
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- **Measures**: Explicit measures for key calculations?
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- **Hidden Fields**: Are technical columns hidden from report view?
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### 3. Provide Targeted Guidance
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Based on analysis, guide improvements using references:
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- Star schema design: See [STAR-SCHEMA.md](references/STAR-SCHEMA.md)
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- Relationship configuration: See [RELATIONSHIPS.md](references/RELATIONSHIPS.md)
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- DAX measures and naming: See [MEASURES-DAX.md](references/MEASURES-DAX.md)
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- Performance optimization: See [PERFORMANCE.md](references/PERFORMANCE.md)
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- Row-level security: See [RLS.md](references/RLS.md)
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## Quick Reference: Model Quality Checklist
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| Area | Best Practice |
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|------|--------------|
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| Tables | Clear dimension vs fact classification |
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| Naming | Human-readable: `Customer Name` not `CUST_NM` |
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| Descriptions | All tables, columns, measures documented |
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| Measures | Explicit DAX measures for business metrics |
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| Relationships | One-to-many from dimension to fact |
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| Cross-filter | Single direction unless specifically needed |
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| Hidden fields | Hide technical keys, IDs from report view |
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| Date table | Dedicated marked date table |
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## MCP Tools Reference
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Use these Power BI Modeling MCP operations:
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| Operation Category | Key Operations |
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|-------------------|----------------|
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| `connection_operations` | Connect, ListConnections, ListLocalInstances, ConnectFabric |
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| `model_operations` | Get, GetStats, ExportTMDL |
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| `table_operations` | List, Get, Create, Update, GetSchema |
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| `column_operations` | List, Get, Create, Update (descriptions, hidden, format) |
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| `measure_operations` | List, Get, Create, Update, Move |
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| `relationship_operations` | List, Get, Create, Update, Activate, Deactivate |
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| `dax_query_operations` | Execute, Validate |
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| `calculation_group_operations` | List, Create, Update |
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| `security_role_operations` | List, Create, Update, GetEffectivePermissions |
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## Common Tasks
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### Add Measure with Description
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```
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measure_operations(
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operation: "Create",
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definitions: [{
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name: "Total Sales",
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tableName: "Sales",
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expression: "SUM(Sales[Amount])",
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formatString: "$#,##0",
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description: "Sum of all sales amounts"
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}]
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)
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```
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### Update Column Description
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```
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column_operations(
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operation: "Update",
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definitions: [{
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tableName: "Customer",
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name: "CustomerKey",
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description: "Unique identifier for customer dimension",
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isHidden: true
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}]
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)
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```
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### Create Relationship
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```
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relationship_operations(
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operation: "Create",
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definitions: [{
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fromTable: "Sales",
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fromColumn: "CustomerKey",
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toTable: "Customer",
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toColumn: "CustomerKey",
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crossFilteringBehavior: "OneDirection"
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}]
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)
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```
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## When to Use Microsoft Learn MCP
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Research current best practices using `microsoft_docs_search` for:
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- Latest DAX function documentation
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- New Power BI features and capabilities
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- Complex modeling scenarios (SCD Type 2, many-to-many)
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- Performance optimization techniques
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- Security implementation patterns

‎skills/powerbi-modeling/references/MEASURES-DAX.md‎

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# DAX Measures and Naming Conventions
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## Naming Conventions
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### General Rules
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- Use human-readable names (spaces allowed)
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- Be descriptive: `Total Sales Amount` not `TSA`
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- Avoid abbreviations unless universally understood
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- Use consistent capitalization (Title Case recommended)
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- Avoid special characters except spaces
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### Table Naming
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| Type | Convention | Example |
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|------|------------|---------|
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| Dimension | Singular noun | Customer, Product, Date |
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| Fact | Business process | Sales, Orders, Inventory |
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| Bridge | Combined names | CustomerAccount, ProductCategory |
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| Measure Table | Underscore prefix | _Measures, _KPIs |
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### Column Naming
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| Type | Convention | Example |
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|------|------------|---------|
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| Keys | Suffix with "Key" or "ID" | CustomerKey, ProductID |
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| Dates | Suffix with "Date" | OrderDate, ShipDate |
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| Amounts | Descriptive with unit hint | SalesAmount, QuantitySold |
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| Flags | Prefix with "Is" or "Has" | IsActive, HasDiscount |
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### Measure Naming
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| Type | Convention | Example |
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|------|------------|---------|
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| Aggregations | Verb + Noun | Total Sales, Count of Orders |
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| Ratios | X per Y or X Rate | Sales per Customer, Conversion Rate |
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| Time Intelligence | Period + Metric | YTD Sales, PY Total Sales |
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| Comparisons | Metric + vs + Baseline | Sales vs Budget, Growth vs PY |
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## Explicit vs Implicit Measures
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### Always Create Explicit Measures For:
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1. Key business metrics users will query
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2. Complex calculations with filter manipulation
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3. Measures used in MDX (Excel PivotTables)
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4. Controlled aggregation (prevent sum of averages)
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### Implicit Measures (Column Aggregations)
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- Acceptable for simple exploration
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- Set correct SummarizeBy property:
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- Amounts: Sum
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- Keys/IDs: None (Do Not Summarize)
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- Rates/Prices: None or Average
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## Measure Patterns
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### Basic Aggregations
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```dax
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Total Sales = SUM(Sales[SalesAmount])
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Order Count = COUNTROWS(Sales)
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Average Order Value = DIVIDE([Total Sales], [Order Count])
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Distinct Customers = DISTINCTCOUNT(Sales[CustomerKey])
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```
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### Time Intelligence (Requires Date Table)
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```dax
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YTD Sales = TOTALYTD([Total Sales], 'Date'[Date])
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MTD Sales = TOTALMTD([Total Sales], 'Date'[Date])
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PY Sales = CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
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YoY Growth = DIVIDE([Total Sales] - [PY Sales], [PY Sales])
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```
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### Percentage Calculations
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```dax
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Sales % of Total =
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DIVIDE(
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[Total Sales],
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CALCULATE([Total Sales], REMOVEFILTERS(Product))
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)
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Margin % = DIVIDE([Gross Profit], [Total Sales])
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```
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### Running Totals
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```dax
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Running Total =
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CALCULATE(
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[Total Sales],
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FILTER(
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ALL('Date'),
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'Date'[Date] <= MAX('Date'[Date])
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)
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)
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```
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## Column References
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### Best Practice: Always Qualify Column Names
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```dax
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// GOOD - Fully qualified
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Sales Amount = SUM(Sales[SalesAmount])
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// BAD - Unqualified (can cause ambiguity)
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Sales Amount = SUM([SalesAmount])
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```
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### Measure References: Never Qualify
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```dax
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// GOOD - Unqualified measure
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YTD Sales = TOTALYTD([Total Sales], 'Date'[Date])
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// BAD - Qualified measure (breaks if home table changes)
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YTD Sales = TOTALYTD(Sales[Total Sales], 'Date'[Date])
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```
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## Documentation
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### Measure Descriptions
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Always add descriptions explaining:
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- What the measure calculates
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- Business context/usage
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- Any important assumptions
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```
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measure_operations(
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operation: "Update",
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definitions: [{
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name: "Total Sales",
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tableName: "Sales",
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description: "Sum of all completed sales transactions. Excludes returns and cancelled orders."
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}]
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)
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```
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### Format Strings
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| Data Type | Format String | Example Output |
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|-----------|---------------|----------------|
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| Currency | $#,##0.00 | $1,234.56 |
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| Percentage | 0.0% | 12.3% |
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| Whole Number | #,##0 | 1,234 |
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| Decimal | #,##0.00 | 1,234.56 |
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## Display Folders
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Organize measures into logical groups:
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```
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measure_operations(
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operation: "Update",
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definitions: [{
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name: "YTD Sales",
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tableName: "_Measures",
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displayFolder: "Time Intelligence\\Year"
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}]
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)
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```
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Common folder structure:
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```
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_Measures
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├── Sales
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│ ├── Total Sales
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│ └── Average Sale
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├── Time Intelligence
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│ ├── Year
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│ │ ├── YTD Sales
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│ │ └── PY Sales
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│ └── Month
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│ └── MTD Sales
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└── Ratios
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├── Margin %
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└── Conversion Rate
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```
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## Variables for Performance
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Use variables to:
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- Avoid recalculating the same expression
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- Improve readability
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- Enable debugging
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```dax
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Gross Margin % =
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VAR TotalSales = [Total Sales]
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VAR TotalCost = [Total Cost]
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VAR GrossProfit = TotalSales - TotalCost
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RETURN
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DIVIDE(GrossProfit, TotalSales)
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```
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## Validation Checklist
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- [ ] All key business metrics have explicit measures
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- [ ] Measures have clear, descriptive names
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- [ ] Measures have descriptions
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- [ ] Appropriate format strings applied
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- [ ] Display folders organize related measures
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- [ ] Column references are fully qualified
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- [ ] Measure references are not qualified
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- [ ] Variables used for complex calculations

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