Agent skill · data analytics · github
fabric-lakehouse
Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.
Why this skill is useful
Provides domain-specific knowledge about Fabric Lakehouse features and best practices for data management that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
When to Use This Skill Use this skill when you need to: Generate a document or explanation that includes definition and context about Fabric Lakehouse and its capabilities. Design, build, and optimize Lakehouse solutions using best practices. Understand the core concepts and components of a Lakehouse in Microsoft Fabric. Learn how to manage tabular and non-tabular data within a Lakehouse. Fabric Lakehouse Core Concepts What is a Lakehouse? Lakehouse in Microsoft Fabric is an item that gives users a place to store their tabular data (like tables) and non-tabular data (like files). It combines the flexibility of a data lake with the management capabilities of a data warehouse. It provides: Unified storage in OneLake for structured and unstructured data Delta Lake format for ACID transactions, versioning, and time travel SQL analytics endpoint for T-SQL queries Semantic model for Power BI integration Support for other table formats like CSV, Parquet Support for any file formats Tools for table optimization and data management Key Components Delta Tables: Managed tables with ACID compliance and schema enforcement Files: Unstructured/semi-structured data in the Files section SQL Endpoint: Auto-generated read-only SQL interface for querying Shortcuts: Virtual links to external/internal data without copying Fabric Materialized Views: Pre-computed tables for fast query performance Tabular data in a Lakehouse Tabular data in a form of tables are stored under "Tables" folder. Main format for tables in Lakehouse is Delta. Lakehouse can store tabular data in other formats like CSV or Parquet, these formats are only available for Spark querying. Tables can be internal, when data is stored under "Tables" folder, or external, when only reference to a table is stored under "Tables" folder but the data itself is stored in a referenced location. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/fabric-lakehouse