Power BI 语义建模最佳实践指南：星型模式、DAX 度量值与模型优化

powerbi-modeling by github/awesome-copilot

7,600 周安装量

26,700 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/github/awesome-copilot --skill powerbi-modeling

数据分析生产力

🇨🇳中文介绍

Power BI 语义建模

指导用户遵循 Microsoft 最佳实践构建优化良好、文档完善的 Power BI 语义模型。

何时使用此技能

当用户询问以下内容时使用此技能：

创建或优化 Power BI 语义模型
设计星型模式（维度/事实表）
编写 DAX 度量值或计算列
配置表关系（基数、交叉筛选器）
实现行级安全性 (RLS)
表、列、度量值的命名约定
向模型添加描述和文档
性能调优和优化
计算组和字段参数
模型验证和最佳实践检查

触发短语： "create a measure", "add relationship", "star schema", "optimize model", "DAX formula", "RLS", "naming convention", "model documentation", "cardinality", "cross-filter"

先决条件

必需工具

Power BI Modeling MCP Server : 连接和修改语义模型所必需
- 启用功能：connection_operations, table_operations, measure_operations, relationship_operations 等
- 必须配置并运行才能与模型交互

可选依赖项

Microsoft Learn MCP Server : 推荐用于研究最新最佳实践
- 启用功能：microsoft_docs_search, microsoft_docs_fetch
- 用于复杂场景、新功能和官方文档

工作流程

1. 首先连接并分析

在提供任何建模指导之前，始终先检查当前模型状态：

1. 列出连接：connection_operations(operation: "ListConnections")
2. 如果没有连接，检查本地实例：connection_operations(operation: "ListLocalInstances")
3. 连接到模型（Desktop 或 Fabric）
4. 获取模型概览：model_operations(operation: "Get")
5. 列出表：table_operations(operation: "List")
6. 列出关系：relationship_operations(operation: "List")
7. 列出度量值：measure_operations(operation: "List")

广告位招租

在这里展示您的产品或服务

触达数万 AI 开发者，精准高效

联系我们

领域	最佳实践
表	清晰的维度与事实分类
命名	人类可读：`Customer Name` 而非 `CUST_NM`
描述	所有表、列、度量值都有文档记录
度量值	业务指标使用显式 DAX 度量值
关系	从维度到事实的一对多关系
交叉筛选	单向筛选，除非特别需要
隐藏字段	在报表视图中隐藏技术键、ID
日期表	专用的标记日期表

🇺🇸English

Power BI Semantic Modeling

Guide users in building optimized, well-documented Power BI semantic models following Microsoft best practices.

When to Use This Skill

Use this skill when users ask about:

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)
Implementing row-level security (RLS)
Naming conventions for tables, columns, measures
Adding descriptions and documentation to models
Performance tuning and optimization
Calculation groups and field parameters
Model validation and best practice checks

Trigger phrases: "create a measure", "add relationship", "star schema", "optimize model", "DAX formula", "RLS", "naming convention", "model documentation", "cardinality", "cross-filter"

Prerequisites

Required Tools

Power BI Modeling MCP Server : Required for connecting to and modifying semantic models
- Enables: connection_operations, table_operations, measure_operations, relationship_operations, etc.
- Must be configured and running to interact with models

Optional Dependencies

Microsoft Learn MCP Server : Recommended for researching latest best practices
- Enables: microsoft_docs_search, microsoft_docs_fetch
- Use for complex scenarios, new features, and official documentation

Workflow

1. Connect and Analyze First

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")

2. Evaluate Model Health

After connecting, assess the model against best practices:

Star Schema : Are tables properly classified as dimension or fact?
Relationships : Correct cardinality? Minimal bidirectional filters?
Naming : Human-readable, consistent naming conventions?
Documentation : Do tables, columns, measures have descriptions?
Measures : Explicit measures for key calculations?
Hidden Fields : Are technical columns hidden from report view?

3. Provide Targeted Guidance

Based on analysis, guide improvements using references:

Star schema design: See STAR-SCHEMA.md
Relationship configuration: See RELATIONSHIPS.md
DAX measures and naming: See MEASURES-DAX.md
Performance optimization: See PERFORMANCE.md
Row-level security: See RLS.md

Quick Reference: Model Quality Checklist

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

MCP Tools Reference

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`

Common Tasks

Add Measure with Description

measure_operations(
  operation: "Create",
  definitions: [{
    name: "Total Sales",
    tableName: "Sales",
    expression: "SUM(Sales[Amount])",
    formatString: "$#,##0",
    description: "Sum of all sales amounts"
  }]
)

Update Column Description

column_operations(
  operation: "Update",
  definitions: [{
    tableName: "Customer",
    name: "CustomerKey",
    description: "Unique identifier for customer dimension",
    isHidden: true
  }]
)

Create Relationship

relationship_operations(
  operation: "Create",
  definitions: [{
    fromTable: "Sales",
    fromColumn: "CustomerKey",
    toTable: "Customer",
    toColumn: "CustomerKey",
    crossFilteringBehavior: "OneDirection"
  }]
)

When to Use Microsoft Learn MCP

Research current best practices using microsoft_docs_search for:

Latest DAX function documentation
New Power BI features and capabilities
Complex modeling scenarios (SCD Type 2, many-to-many)
Performance optimization techniques
Security implementation patterns

Weekly Installs

7.6K

Repository

github/awesome-copilot

GitHub Stars

26.7K

First Seen

Feb 4, 2026

Security Audits

Gen Agent Trust HubPass SocketPass SnykPass

Installed on

codex7.4K

gemini-cli7.4K

opencode7.4K

github-copilot7.4K

cursor7.4K

kimi-cli7.4K