ReddRadar
Agent skill
microsoft-skill-creator
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.
Install this agent skill to your Project
npx add-skill https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/microsoft-skill-creator
SKILL.md
Microsoft Skill Creator
Create hybrid skills for Microsoft technologies that store essential knowledge locally while enabling dynamic Learn MCP lookups for deeper details.
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 |
CLI Alternative
If the Learn MCP server is not available, use the mslearn CLI from the command line instead:
# Run directly (no install needed)
npx @microsoft/learn-cli search "semantic kernel overview"
# Or install globally, then run
npm install -g @microsoft/learn-cli
mslearn search "semantic kernel overview"
| MCP Tool | CLI Command |
|---|---|
microsoft_docs_search(query: "...") |
mslearn search "..." |
microsoft_code_sample_search(query: "...", language: "...") |
mslearn code-search "..." --language ... |
microsoft_docs_fetch(url: "...") |
mslearn fetch "..." |
Generated skills should include this same CLI fallback table so agents can use either path.
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")` |
## CLI Alternative
If the Learn MCP server is not available, use the `mslearn` CLI instead:
| MCP Tool | CLI Command |
|----------|-------------|
| `microsoft_docs_search(query: "...")` | `mslearn search "..."` |
| `microsoft_code_sample_search(query: "...", language: "...")` | `mslearn code-search "..." --language ...` |
| `microsoft_docs_fetch(url: "...")` | `mslearn fetch "..."` |
Run directly with `npx @microsoft/learn-cli <command>` or install globally with `npm install -g @microsoft/learn-cli`.
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