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.

This repo convention: After generating skill content with this skill, use the make-skill-template skill to ensure the output follows this repo's SKILL.md frontmatter conventions and directory structure (see .github/instructions/agent-skills.instructions.md).

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

CLI Alternative

If the Learn MCP server is not available, use the mslearn CLI via Bash 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"

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:

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)

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

See references/investigation-patterns.md for SDK/Library, Azure Service, and Framework/Platform search query templates, plus a complete Semantic Kernel example.

Reference Index