Microsoft Skill Creator
Create hybrid agent skills for Microsoft technologies, combining essential local knowledge with dynamic Microsoft Learn MCP lookups for deeper exploration.
Description
name: microsoft-skill-creator description: Create agent skills for Microsoft technologies using Learn MCP tools. Use when users want to create a skill that teaches agents about any Microsoft technology, library, framework, or service (Azure, .NET, M365, VS Code, Bicep, etc.). Investigates topics deeply, then generates a hybrid skill storing essential knowledge locally while enabling dynamic deeper investigation. context: fork compatibility: Requires Microsoft Learn MCP Server (https://learn.microsoft.com/api/mcp)
Microsoft Skill Creator
Create hybrid skills for Microsoft technologies that store essential knowledge locally while enabling dynamic Learn MCP lookups for deeper details.
Prerequisites: Connect to the Microsoft Learn MCP Server
IMPORTANT: This skill requires the Microsoft Learn MCP Server (type: http, url: https://learn.microsoft.com/api/mcp). If it's not already connected, you MUST install and configure it yourself. Ask the user for permission if needed.
It's a free, official Microsoft-owned MCP, no API key or authentication required.
About Skills
Skills are modular packages that extend agent capabilities with specialized knowledge and workflows. A skill transforms a general-purpose agent into a specialized one for a specific domain.
Skill Structure
skill-name/
├── SKILL.md (required) # Frontmatter (name, description) + instructions
├── references/ # Documentation loaded into context as needed
├── sample_codes/ # Working code examples
└── assets/ # Files used in output (templates, etc.)
Key Principles
- Frontmatter is critical:
nameanddescriptiondetermine when the skill triggers—be clear and comprehensive - Concise is key: Only include what agents don't already know; context window is shared
- No duplication: Information lives in SKILL.md OR reference files, not both
Learn MCP Tools
| Tool | Purpose | When to Use |
|---|---|---|
microsoft_docs_search |
Search official docs | First pass discovery, finding topics |
microsoft_docs_fetch |
Get full page content | Deep dive into important pages |
microsoft_code_sample_search |
Find code examples | Get implementation patterns |
Creation Process
Step 1: Investigate the Topic
Build deep understanding using Learn MCP tools in three phases:
Phase 1 - Scope Discovery:
microsoft_docs_search(query="{technology} overview what is")
microsoft_docs_search(query="{technology} concepts architecture")
microsoft_docs_search(query="{technology} getting started tutorial")
Phase 2 - Core Content:
microsoft_docs_fetch(url="...") # Fetch pages from Phase 1
microsoft_code_sample_search(query="{technology}", language="{lang}")
Phase 3 - Depth:
microsoft_docs_search(query="{technology} best practices")
microsoft_docs_search(query="{technology} troubleshooting errors")
Investigation Checklist
After investigating, verify:
- Can explain what the technology does in one paragraph
- Identified 3-5 key concepts
- Have working code for basic usage
- Know the most common API patterns
- Have search queries for deeper topics
Step 2: Clarify with User
Present findings and ask:
- "I found these key areas: [list]. Which are most important?"
- "What tasks will agents primarily perform with this skill?"
- "Which programming language should code samples prioritize?"
Step 3: Generate the Skill
Use the appropriate template from skill-templates.md:
| Technology Type | Template |
|---|---|
| Client library, NuGet/npm package | SDK/Library |
| Azure resource | Azure Service |
| App development framework | Framework/Platform |
| REST API, protocol | API/Protocol |
Generated Skill Structure
{skill-name}/
├── SKILL.md # Core knowledge + Learn MCP guidance
├── references/ # Detailed local documentation (if needed)
└── sample_codes/ # Working code examples
├── getting-started/
└── common-patterns/
Step 4: Balance Local vs Dynamic Content
Store locally when:
- Foundational (needed for any task)
- Frequently accessed
- Stable (won't change)
- Hard to find via search
Keep dynamic when:
- Exhaustive reference (too large)
- Version-specific
- Situational (specific tasks only)
- Well-indexed (easy to search)
Content Guidelines
| Content Type | Local | Dynamic |
|---|---|---|
| Core concepts (3-5) | ✅ Full | |
| Hello world code | ✅ Full | |
| Common patterns (3-5) | ✅ Full | |
| Top API methods | Signature + example | Full docs via fetch |
| Best practices | Top 5 bullets | Search for more |
| Troubleshooting | Search queries | |
| Full API reference | Doc links |
Step 5: Validate
- Review: Is local content sufficient for common tasks?
- Test: Do suggested search queries return useful results?
- Verify: Do code samples run without errors?
Common Investigation Patterns
For SDKs/Libraries
"{name} overview" → purpose, architecture
"{name} getting started quickstart" → setup steps
"{name} API reference" → core classes/methods
"{name} samples examples" → code patterns
"{name} best practices performance" → optimization
For Azure Services
"{service} overview features" → capabilities
"{service} quickstart {language}" → setup code
"{service} REST API reference" → endpoints
"{service} SDK {language}" → client library
"{service} pricing limits quotas" → constraints
For Frameworks/Platforms
"{framework} architecture concepts" → mental model
"{framework} project structure" → conventions
"{framework} tutorial walkthrough" → end-to-end flow
"{framework} configuration options" → customization
Example: Creating a "Semantic Kernel" Skill
Investigation
microsoft_docs_search(query="semantic kernel overview")
microsoft_docs_search(query="semantic kernel plugins functions")
microsoft_code_sample_search(query="semantic kernel", language="csharp")
microsoft_docs_fetch(url="https://learn.microsoft.com/semantic-kernel/overview/")
Generated Skill
semantic-kernel/
├── SKILL.md
└── sample_codes/
├── getting-started/
│ └── hello-kernel.cs
└── common-patterns/
├── chat-completion.cs
└── function-calling.cs
Generated SKILL.md
---
name: semantic-kernel
description: Build AI agents with Microsoft Semantic Kernel. Use for LLM-powered apps with plugins, planners, and memory in .NET or Python.
---
# Semantic Kernel
Orchestration SDK for integrating LLMs into applications with plugins, planners, and memory.
## Key Concepts
- **Kernel**: Central orchestrator managing AI services and plugins
- **Plugins**: Collections of functions the AI can call
- **Planner**: Sequences plugin functions to achieve goals
- **Memory**: Vector store integration for RAG patterns
## Quick Start
See [getting-started/hello-kernel.cs](sample_codes/getting-started/hello-kernel.cs)
## Learn More
| Topic | How to Find |
|-------|-------------|
| Plugin development | `microsoft_docs_search(query="semantic kernel plugins custom functions")` |
| Planners | `microsoft_docs_search(query="semantic kernel planner")` |
| Memory | `microsoft_docs_fetch(url="https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-memory")` |
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