Claude-skill-registry-data microsoft-fabric

Expert guidance for Microsoft Fabric development using the Fabric MCP Server. Access Fabric public APIs, OpenAPI specs, item schemas, best practices, and OneLake file management. Use when working with Fabric workloads, Lakehouses, pipelines, semantic models, notebooks, or building Fabric integrations.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry-data

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/microsoft-fabric-housegarofalo-claude-code-base" ~/.claude/skills/majiayu000-claude-skill-registry-data-microsoft-fabric && rm -rf "$T"

manifest: data/microsoft-fabric-housegarofalo-claude-code-base/SKILL.md

source content

Microsoft Fabric Development Expert

Expert guidance for Microsoft Fabric using the Fabric MCP Server. Access comprehensive API specifications, item definitions, best practices, and OneLake management capabilities - all running locally without connecting to live environments.

Core Capabilities

API Discovery - Enumerate and access Fabric workload APIs
Schema Access - Get JSON schemas for item definitions
Best Practices - Access guidance and examples
OneLake Management - File and item operations
Local-First - All tools run locally for reference and development

Quick Reference - MCP Tools

API Access Tools

Tool	Purpose
`publicapis_list`	List all Fabric workload types
`publicapis_get`	Get OpenAPI spec for workload
`publicapis_platform_get`	Get platform API specs
`publicapis_bestpractices_get`	Get best practices documentation
`publicapis_bestpractices_examples_get`	Get API request/response examples
`publicapis_bestpractices_itemdefinition_get`	Get item schema definitions

OneLake Tools

Tool	Purpose
`onelake download file`	Download files from OneLake
`onelake upload file`	Upload files to OneLake
`onelake file list`	List files in OneLake
`onelake file delete`	Delete files from OneLake
`onelake directory create`	Create directories
`onelake directory delete`	Delete directories
`onelake item list`	List workspace items
`onelake item list-data`	List items via DFS endpoint
`onelake item create`	Create new Fabric items

API Discovery Tools

publicapis_list

List all Microsoft Fabric workload types that have public API specifications.

When to use:

Starting Fabric development
Exploring available workloads
Finding workload-specific APIs

Workload Types Include:

Lakehouses
Data Pipelines
Semantic Models (Power BI)
Notebooks
Spark Job Definitions
Warehouses
KQL Databases
Eventhouse
Real-Time Intelligence
ML Models
ML Experiments

publicapis_get

Retrieve complete OpenAPI/Swagger specification for a specific workload.

Parameters:

Parameter	Type	Required	Description
workload	string	Yes	Workload type name

Workload Examples:

```
DataPipeline
```
```
Lakehouse
```
```
SemanticModel
```
```
Notebook
```
```
SparkJobDefinition
```
```
Warehouse
```
```
KQLDatabase
```

publicapis_platform_get

Access OpenAPI specifications for Microsoft Fabric platform-level APIs.

Platform APIs Include:

Workspace management
Item management (generic)
Permission management
Capacity operations
Deployment pipelines
Git integration

publicapis_bestpractices_get

Get embedded best practice documentation for Fabric development.

Parameters:

Parameter	Type	Required	Description
topic	string	Yes	Best practice topic

Topics Include:

Pagination patterns
Error handling
Retry/backoff strategies
Authentication
Rate limiting
API versioning
Request/response patterns

publicapis_bestpractices_examples_get

Retrieve example API request/response files for workloads.

Parameters:

Parameter	Type	Required	Description
workload	string	Yes	Workload type
example_type	string	No	Type of example

Example Types:

```
create
```
- Creation requests
```
update
```
- Update operations
```
get
```
- Retrieval operations
```
list
```
- Listing operations
```
delete
```
- Deletion operations

publicapis_bestpractices_itemdefinition_get

Access JSON schema definitions for items within workload APIs.

Parameters:

Parameter	Type	Required	Description
workload	string	Yes	Workload type
item_type	string	Yes	Specific item type

Common Item Types:

Lakehouse:
```
lakehouse
```
Pipeline:
```
datapipeline
```
,
```
activity
```
Semantic Model:
```
semanticmodel
```
,
```
dataset
```
Notebook:
```
notebook
```
Warehouse:
```
warehouse
```
KQL Database:
```
kqldatabase
```

OneLake Management Tools

onelake download file

Download files from OneLake to local disk.

Parameters:

Parameter	Type	Required	Description
workspace	string	Yes	Workspace ID or name
item	string	Yes	Item ID or name.type format
path	string	Yes	File path in OneLake
local_path	string	Yes	Local destination path

Item Format: Can be:

GUID:
```
550e8400-e29b-41d4-a716-446655440000
```
Name.Type:
```
MyLakehouse.Lakehouse
```

onelake upload file

Upload local files to OneLake storage.

Parameters:

Parameter	Type	Required	Description
workspace	string	Yes	Workspace ID or name
item	string	Yes	Item ID or name.type format
local_path	string	Yes	Local file path
onelake_path	string	Yes	Destination path in OneLake

onelake file list

List files in OneLake via hierarchical endpoint.

Parameters:

Parameter	Type	Required	Description
workspace	string	Yes	Workspace ID or name
item	string	Yes	Item ID or name.type format
path	string	No	Path to list (default: root)

onelake item create

Create new Fabric items (Lakehouses, notebooks, pipelines, etc.).

Parameters:

Parameter	Type	Required	Description
workspace	string	Yes	Workspace ID or name
item_type	string	Yes	Type of item to create
display_name	string	Yes	Item display name
description	string	No	Item description
definition	object	No	Item-specific configuration

Item Types:

```
Lakehouse
```
```
DataPipeline
```
```
Notebook
```
```
Warehouse
```
```
KQLDatabase
```
```
SemanticModel
```
```
SparkJobDefinition
```

Development Workflows

Workflow 1: Discover and Use Fabric APIs

1. publicapis_list
   - See all available workloads

2. publicapis_get
   workload: "DataPipeline"
   - Get complete API specification

3. publicapis_bestpractices_itemdefinition_get
   workload: "DataPipeline"
   item_type: "datapipeline"
   - Get schema for pipeline definition

4. publicapis_bestpractices_examples_get
   workload: "DataPipeline"
   example_type: "create"
   - See example API calls

5. Implement based on specs and examples

Workflow 2: Create and Configure Lakehouse

1. onelake item create
   workspace: "MyWorkspace"
   item_type: "Lakehouse"
   display_name: "DataLakehouse"

2. onelake directory create
   item: "DataLakehouse.Lakehouse"
   path: "/Files/raw"

3. onelake directory create
   item: "DataLakehouse.Lakehouse"
   path: "/Files/processed"

4. onelake upload file
   item: "DataLakehouse.Lakehouse"
   local_path: "./data.csv"
   onelake_path: "/Files/raw/data.csv"

5. onelake file list
   item: "DataLakehouse.Lakehouse"
   - Verify upload

Workflow 3: Build Data Pipeline

1. publicapis_get
   workload: "DataPipeline"

2. publicapis_bestpractices_itemdefinition_get
   workload: "DataPipeline"
   item_type: "activity"

3. onelake item create
   item_type: "DataPipeline"
   display_name: "ETL Pipeline"
   definition: { /* pipeline config */ }

4. publicapis_bestpractices_get
   topic: "error_handling"

Best Practices

API Usage

Start with publicapis_list - Discover available workloads
Get full spec - Use publicapis_get for complete API documentation
Use schemas - Validate against item definitions
Follow examples - Start from provided examples
Handle errors - Implement retry logic from best practices

OneLake Management

Use friendly names - Prefer
```
name.type
```
format over GUIDs
Check before operations - List files/items first
Organize structure - Create logical directory hierarchies
Cleanup regularly - Delete unnecessary files
Verify uploads - List files after upload operations

Item Creation

Get schema first - Use itemdefinition_get before creating
Validate configuration - Check required properties
Start simple - Begin with minimal configuration
Test incrementally - Create, verify, then enhance
Use examples - Adapt from examples_get results

Fabric Workload Reference

Lakehouse

Purpose: Delta Lake storage with SQL analytics
Use publicapis_get:
```
Lakehouse
```
Item type:
```
Lakehouse
```
OneLake structure:
```
/Files
```
,
```
/Tables
```

Data Pipeline

Purpose: Data integration and ETL
Use publicapis_get:
```
DataPipeline
```
Item type:
```
DataPipeline
```
Components: Activities, datasets, linked services

Semantic Model

Purpose: Power BI datasets
Use publicapis_get:
```
SemanticModel
```
Item type:
```
SemanticModel
```
Components: Tables, measures, relationships

Notebook

Purpose: Interactive code notebooks
Use publicapis_get:
```
Notebook
```
Item type:
```
Notebook
```
Languages: Python, Scala, R, SQL

Warehouse

Purpose: SQL data warehouse
Use publicapis_get:
```
Warehouse
```
Item type:
```
Warehouse
```
Features: T-SQL, tables, views, procedures

KQL Database

Purpose: Real-time analytics with Kusto
Use publicapis_get:
```
KQLDatabase
```
Item type:
```
KQLDatabase
```
Query language: KQL (Kusto Query Language)

When to Use This Skill

Developing Microsoft Fabric integrations
Building Fabric REST API clients
Creating Lakehouses and data pipelines
Managing OneLake storage programmatically
Understanding Fabric item schemas
Implementing Fabric best practices
Automating Fabric workspace operations
Learning Fabric API capabilities

Keywords

microsoft fabric, onelake, lakehouse, data pipeline, semantic model, notebook, warehouse, kql database, fabric api, openapi, rest api, item definition, schema, best practices, workspace management, file operations, fabric workload