github/awesome-copilot
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
npx skills add https://github.com/github/awesome-copilot --skill "skills/powerbi-modeling"Quick start
Install it or open the source, trigger it with a clear task, then follow the source workflow.
npx skills add https://github.com/github/awesome-copilot --skill "skills/powerbi-modeling"Use powerbi-modeling to help me with: [describe your task]. Before you begin, tell me what input you need, the steps you will follow, and the expected output.
5 key workflow steps, examples, and cautions are distilled below.
Continue to the workflowDirect answers
Power BI semantic modeling assistant for building optimized data models. 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.
It is relevant to workflows involving Documentation, Design, Operations, Research.
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This page combines upstream documentation with deterministic repository, quality, and static-risk signals. It is not described as a manual test or security review.
SkillSignal brief
Power BI semantic modeling assistant for building optimized data models. 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.
Useful in these contexts
Core capabilities
Distilled from the source
About 3 min · 8 sections
Creating or optimizing Power BI semantic models
Designing star schemas (dimension/fact tables)
Writing DAX measures or calculated columns
Configuring table relationships (cardinality, cross-filter)
1. Connect and Analyze First
2. Evaluate Model Health
3. Provide Targeted Guidance
Star Schema: Are tables properly classified as dimension or fact?
Relationships: Correct cardinality? Minimal bidirectional filters?
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
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Guide users in building optimized, well-documented Power BI semantic models following Microsoft best practices.
Use this skill when users ask about:
Trigger phrases: "create a measure", "add relationship", "star schema", "optimize model", "DAX formula", "RLS", "naming convention", "model documentation", "cardinality", "cross-filter"
Before providing any modeling guidance, always examine the current model state:
1. List connections: connection_operations(operation: "ListConnections")
2. If no connection, check for local instances: connection_operations(operation: "ListLocalInstances")
3. Connect to the model (Desktop or Fabric)
4. Get model overview: model_operations(operation: "Get")
5. List tables: table_operations(operation: "List")
6. List relationships: relationship_operations(operation: "List")
7. List measures: measure_operations(operation: "List")
After connecting, assess the model against best practices:
Based on analysis, guide improvements using references:
| Area | Best Practice |
|---|---|
| Tables | Clear dimension vs fact classification |
| Naming | Human-readable: Customer Name not CUST_NM |
| Descriptions | All tables, columns, measures documented |
| Measures | Explicit DAX measures for business metrics |
| Relationships | One-to-many from dimension to fact |
| Cross-filter | Single direction unless specifically needed |
| Hidden fields | Hide technical keys, IDs from report view |
| Date table | Dedicated marked date table |
Use these Power BI Modeling MCP operations:
| Operation Category | Key Operations |
|---|---|
connection_operations | Connect, ListConnections, ListLocalInstances, ConnectFabric |
model_operations | Get, GetStats, ExportTMDL |
table_operations | List, Get, Create, Update, GetSchema |
column_operations | List, Get, Create, Update (descriptions, hidden, format) |
measure_operations | List, Get, Create, Update, Move |
relationship_operations | List, Get, Create, Update, Activate, Deactivate |
dax_query_operations | Execute, Validate |
calculation_group_operations | List, Create, Update |
security_role_operations | List, Create, Update, GetEffectivePermissions |
measure_operations(
operation: "Create",
definitions: [{
name: "Total Sales",
tableName: "Sales",
expression: "SUM(Sales[Amount])",
formatString: "$#,##0",
description: "Sum of all sales amounts"
}]
)
column_operations(
operation: "Update",
definitions: [{
tableName: "Customer",
name: "CustomerKey",
description: "Unique identifier for customer dimension",
isHidden: true
}]
)
relationship_operations(
operation: "Create",
definitions: [{
fromTable: "Sales",
fromColumn: "CustomerKey",
toTable: "Customer",
toColumn: "CustomerKey",
crossFilteringBehavior: "OneDirection"
}]
)
Research current best practices using microsoft_docs_search for: