Microsoft Skill Creator

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: name and description determine 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

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:

1. "I found these key areas: [list]. Which are most important?"
2. "What tasks will agents primarily perform with this skill?"
3. "Which programming language should code samples prioritize?"

Step 3: Generate the Skill

Use the appropriate template from skill-templates.md:

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

Step 5: Validate

1. Review: Is local content sufficient for common tasks?
2. Test: Do suggested search queries return useful results?
3. 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")` |