ProfessorGPTProfessorGPT
Microsoft
MicrosoftMicrosoft· v1.0.0
ai-mlOfficial
Official Provider SkillView repo

Review 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,

Files2 files
SKILL.md679 lines
Loading editor…

Install

Recommended

One command — your agent picks it up automatically.

Select an AI agent above to see the install command.

or

Manual Install

More steps

Download the archive and add the files to your project manually.

Skill details

Versionv1.0.0
AuthorMicrosoft
Categoryai-ml
Skill IDmicrosoft/gh-copilot-fabric-agents/.github/skills/fabric-optimization-review
Files2 files

Related skills

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 Semantic ModelInfer 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.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.Agent Host Chat ContributionsBuild and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.Build Champ TriageFind out why a build has failed