Fabric Semantic Model
microsoft/gh-copilot-fabric-agents/.github/skills/fabric-semantic-model
devopsResmi
Resmi Sağlayıcı Skill'iView repo
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 schema, Power BI model, TMDL, fact table, dimension table.
Dosyalar2 dosya
SKILL.md239 satır
Loading editor…
Kurulum
ÖnerilenTek komut — ajanınız otomatik olarak devreye alır.
Kurulum komutunu görmek için yukarıdan bir AI aracı seçin.
veya
Manuel Kurulum
Daha fazla adımArşivi indirin ve dosyaları projenize manuel olarak ekleyin.
Skill detayları
Versiyonv1.0.0
YazarMicrosoft
Kategoridevops
Skill IDmicrosoft/gh-copilot-fabric-agents/.github/skills/fabric-semantic-model
Dosyalar2 dosya
İlgili skill'ler
Fabric Data CleanerAlgorithm reference and self-contained PySpark notebook templates for cleaning Microsoft Fabric lakehouse tables. Covers profiling, duplicate detection, null analysis, type validation, statistics, IQR outlier detection, date format validation, Spanish DNI/NIE checksum validation, and email/phone format checks. Each notebook can be uploaded and run independently in Fabric.Fabric Metadata CreationCreate reviewable metadata proposals for Microsoft Fabric lakehouse tables and semantic models. Covers schema analysis, concise technical and business descriptions, glossary terms, classifications, Purview-like sensitivity label proposals, PII and sensitive data detection, relationship and lineage hints, data quality rules, CDE candidates, data products, JSON/PDF output, and steward review questions. Use when: metadata creation, data catalog, Purview mapping, glossary inference, data classificatFabric Optimization ReviewReview and optimize data storage, data models, and semantic models in Microsoft Fabric lakehouses. Covers Delta table optimization (V-Order, Z-Order, compaction, partitioning), data model anti-pattern detection, semantic model DAX review, relationship optimization, AI/agentic readiness (descriptions, synonyms, linguistic schema, display folders), workspace audit (Fabric best practices, dimensional modeling, Direct Lake), and actionable recommendation reports. Use when: optimization, performance,Fabric Synthetic DataGenerate and upload synthetic data to Microsoft Fabric lakehouse tables. Covers star-schema generation, realistic fake data (names, dates, IDs, transactions), configurable row counts, referential integrity between tables, Parquet export, and OneLake upload. Use when: synthetic data, test data, fake data, sample data, generate tables, populate lakehouse, seed data, mock data.Azure PipelinesUse when validating Azure DevOps pipeline changes for the VS Code build. Covers queueing builds, checking build status, viewing logs, and iterating on pipeline YAML changes without waiting for full CI runs.Publish ExtensionPublish your Command Palette extension to the Microsoft Store or WinGet. Use when asked to publish, distribute, release, deploy to store, create MSIX packages, submit to WinGet, set up CI/CD for releases, or automate builds with GitHub Actions.