Semantic modeling assistant for building optimized Power BI data models with DAX, relationships, and best practices.
Works with
Connects to active Power BI models (Desktop or Fabric) to analyze current structure before providing guidance on star schemas, relationships, measures, and naming conventions
Covers core modeling tasks: creating DAX measures, configuring table relationships and cardinality, implementing row-level security (RLS), and optimizing performance
Includes model quality assessm
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpowerbi-modelingExecute the skills CLI command in your project's root directory to begin installation:
Fetches powerbi-modeling from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate powerbi-modeling. Access via /powerbi-modeling in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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:
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
github/awesome-copilot
github/awesome-copilot
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Solid pick for teams standardizing on skills: powerbi-modeling is focused, and the summary matches what you get after install.
I recommend powerbi-modeling for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: powerbi-modeling is focused, and the summary matches what you get after install.
Registry listing for powerbi-modeling matched our evaluation — installs cleanly and behaves as described in the markdown.
We added powerbi-modeling from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in powerbi-modeling — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added powerbi-modeling from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
powerbi-modeling reduced setup friction for our internal harness; good balance of opinion and flexibility.
powerbi-modeling fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend powerbi-modeling for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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