fabric-semantic-model Skill
Infer and generate Power BI semantic models (TMDL format) from Microsoft Fabric lakehouse tables. Covers star-schema detection, table classification (fact/dimension), relationship inference, DAX measure generation, data type mapping, and deployment via REST API. Use when: create semantic model, build data model, star s Published by microsoft in gh-copilot-fabric-agents.
What is fabric-semantic-model Skill?
Infer and generate Power BI semantic models (TMDL format) from Microsoft Fabric lakehouse tables. Covers star-schema detection, table classification (fact/dimension), relationship inference, DAX measure generation, data type mapping, and deployment via REST API. Use when: create semantic model, build data model, star s Published by microsoft in gh-copilot-fabric-agents. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Deployment
- Data analysis
- Deployment use cases
- Data analysis use cases
Technical details
- Install or run with Copy skill directory
When to use fabric-semantic-model Skill
- Use it for deployment.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/gh-copilot-fabric-agents
- Skill file: .github/skills/fabric-semantic-model/SKILL.md
What it does
Infer and generate Power BI semantic models (TMDL format) from Microsoft Fabric lakehouse tables. Covers star-schema detection, table classification (fact/dimension), relationship inference, DAX measure generation, data type mapping, and deployment via REST API. Use when: create semantic model, build data model, star s
Skill instructions
Fabric Semantic Model Creator — Reference Generates Power BI semantic models in TMDL format by analyzing lakehouse table schemas and sample data. The agent reasons about table names, column names, data types, cardinality, and sample values to infer the best star-schema model. Table Classification Rules Classify each lakehouse table as fact or dimension based on these signals: | Signal | Fact Table | Dimension Table | |--------|-----------|-----------------| | Name patterns | Contains fact, fct, sales, orders, transactions, events, log, movements | Contains dim, lookup, calendar, date, product, customer, employee, geography, category, status | | Column patterns | Has multiple id/key foreign keys + numeric measure columns (amount, quantity, price, total, count) | Has a single primary key + descriptive text columns (name, description, label, title, code) | | Row count | Typically high (transactions) | Typically low (reference data) | | Numeric density | Many numeric columns relative to to
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Frequently asked questions
What is fabric-semantic-model?
fabric-semantic-model is a open-source AI agent skill with Copy skill directory. Infer and generate Power BI semantic models (TMDL format) from Microsoft Fabric lakehouse tables.
Who is fabric-semantic-model best for?
fabric-semantic-model is best for reusing agent instructions, scripts, and references, deployment workflows, data analysis workflows.
How do I install fabric-semantic-model?
Install or run fabric-semantic-model using Copy skill directory. Check fabric-semantic-model for the latest setup command.
Is fabric-semantic-model actively maintained?
fabric-semantic-model may need a closer maintenance check before production use.
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