microsoft-skill-creator by github/awesome-copilot
npx skills add https://github.com/github/awesome-copilot --skill microsoft-skill-creator为 Microsoft 技术创建混合技能,这些技能在本地存储核心知识,同时支持动态的 Learn MCP 查询以获取更深入的详细信息。
技能是模块化包,通过专业知识和工作流程扩展智能体的能力。一个技能可以将通用智能体转变为特定领域的专家。
skill-name/
├── SKILL.md (必需) # 前言(名称、描述)+ 使用说明
├── references/ # 按需加载到上下文中的文档
├── sample_codes/ # 可运行的代码示例
└── assets/ # 输出中使用的文件(模板等)
name 和 description 决定了技能何时触发——务必清晰全面| 工具 | 用途 | 使用时机 |
|---|---|---|
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
microsoft_docs_search| 搜索官方文档 |
| 初步探索,查找主题 |
microsoft_docs_fetch | 获取完整页面内容 | 深入研究重要页面 |
microsoft_code_sample_search | 查找代码示例 | 获取实现模式 |
如果 Learn MCP 服务器不可用,请改用终端或 shell(例如 Bash、PowerShell 或 cmd)中的 mslearn CLI:
# 直接运行(无需安装)
npx @microsoft/learn-cli search "semantic kernel overview"
# 或全局安装后运行
npm install -g @microsoft/learn-cli
mslearn search "semantic kernel overview"
| MCP 工具 | CLI 命令 |
|---|---|
microsoft_docs_search(query: "...") | mslearn search "..." |
microsoft_code_sample_search(query: "...", language: "...") | mslearn code-search "..." --language ... |
microsoft_docs_fetch(url: "...") | mslearn fetch "..." |
生成的技能应包含相同的 CLI 备用方案表格,以便智能体可以使用任一途径。
使用 Learn MCP 工具分三个阶段建立深入理解:
阶段 1 - 范围发现:
microsoft_docs_search(query="{technology} overview what is")
microsoft_docs_search(query="{technology} concepts architecture")
microsoft_docs_search(query="{technology} getting started tutorial")
阶段 2 - 核心内容:
microsoft_docs_fetch(url="...") # 获取阶段 1 中的页面
microsoft_code_sample_search(query="{technology}", language="{lang}")
阶段 3 - 深度挖掘:
microsoft_docs_search(query="{technology} best practices")
microsoft_docs_search(query="{technology} troubleshooting errors")
调研后,请验证:
展示发现并提出问题:
使用 skill-templates.md 中的相应模板:
| 技术类型 | 模板 |
|---|---|
| 客户端库,NuGet/npm 包 | SDK/库 |
| Azure 资源 | Azure 服务 |
| 应用开发框架 | 框架/平台 |
| REST API,协议 | API/协议 |
{skill-name}/
├── SKILL.md # 核心知识 + Learn MCP 指导
├── references/ # 详细的本地文档(如果需要)
└── sample_codes/ # 可运行的代码示例
├── getting-started/
└── common-patterns/
在以下情况本地存储:
在以下情况保持动态:
| 内容类型 | 本地 | 动态 |
|---|---|---|
| 核心概念 (3-5 个) | ✅ 完整 | |
| Hello world 代码 | ✅ 完整 | |
| 常见模式 (3-5 个) | ✅ 完整 | |
| 主要 API 方法 | 签名 + 示例 | 通过 fetch 获取完整文档 |
| 最佳实践 | 前 5 个要点 | 搜索更多 |
| 故障排除 | 搜索查询 | |
| 完整 API 参考 | 文档链接 |
"{name} overview" → 目的,架构
"{name} getting started quickstart" → 设置步骤
"{name} API reference" → 核心类/方法
"{name} samples examples" → 代码模式
"{name} best practices performance" → 优化
"{service} overview features" → 功能
"{service} quickstart {language}" → 设置代码
"{service} REST API reference" → 端点
"{service} SDK {language}" → 客户端库
"{service} pricing limits quotas" → 限制
"{framework} architecture concepts" → 心智模型
"{framework} project structure" → 约定
"{framework} tutorial walkthrough" → 端到端流程
"{framework} configuration options" → 自定义
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/")
semantic-kernel/
├── SKILL.md
└── sample_codes/
├── getting-started/
│ └── hello-kernel.cs
└── common-patterns/
├── chat-completion.cs
└── function-calling.cs
---
name: semantic-kernel
description: 使用 Microsoft Semantic Kernel 构建 AI 智能体。用于在 .NET 或 Python 中开发具有插件、规划器和记忆功能的 LLM 驱动应用。
---
# Semantic Kernel
用于通过插件、规划器和记忆将 LLM 集成到应用程序中的编排 SDK。
## 关键概念
- **Kernel** : 管理 AI 服务和插件的中央编排器
- **Plugins** : AI 可以调用的函数集合
- **Planner** : 编排插件函数以实现目标
- **Memory** : 用于 RAG 模式的向量存储集成
## 快速开始
参见 [getting-started/hello-kernel.cs](sample_codes/getting-started/hello-kernel.cs)
## 了解更多
| 主题 | 如何查找 |
|-------|-------------|
| 插件开发 | `microsoft_docs_search(query="semantic kernel plugins custom functions")` |
| 规划器 | `microsoft_docs_search(query="semantic kernel planner")` |
| 记忆 | `microsoft_docs_fetch(url="https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-memory")` |
## CLI 替代方案
如果 Learn MCP 服务器不可用,请改用 `mslearn` CLI:
| MCP 工具 | CLI 命令 |
|----------|-------------|
| `microsoft_docs_search(query: "...")` | `mslearn search "..."` |
| `microsoft_code_sample_search(query: "...", language: "...")` | `mslearn code-search "..." --language ...` |
| `microsoft_docs_fetch(url: "...")` | `mslearn fetch "..."` |
使用 `npx @microsoft/learn-cli <command>` 直接运行,或使用 `npm install -g @microsoft/learn-cli` 全局安装。
每周安装量
7.4K
代码库
GitHub 星标数
26.7K
首次出现
2026 年 2 月 11 日
安全审计
安装于
codex7.3K
gemini-cli7.3K
opencode7.3K
github-copilot7.3K
cursor7.3K
kimi-cli7.3K
Create hybrid skills for Microsoft technologies that store essential knowledge locally while enabling dynamic Learn MCP lookups for deeper details.
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-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.)
name and description determine when the skill triggers—be clear and comprehensive| 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 |
If the Learn MCP server is not available, use the mslearn CLI from a terminal or shell (for example, Bash, PowerShell, or cmd) 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.
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")
After investigating, verify:
Present findings and ask:
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 |
{skill-name}/
├── SKILL.md # Core knowledge + Learn MCP guidance
├── references/ # Detailed local documentation (if needed)
└── sample_codes/ # Working code examples
├── getting-started/
└── common-patterns/
Store locally when:
Keep dynamic when:
| 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 |
"{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
"{service} overview features" → capabilities
"{service} quickstart {language}" → setup code
"{service} REST API reference" → endpoints
"{service} SDK {language}" → client library
"{service} pricing limits quotas" → constraints
"{framework} architecture concepts" → mental model
"{framework} project structure" → conventions
"{framework} tutorial walkthrough" → end-to-end flow
"{framework} configuration options" → customization
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/")
semantic-kernel/
├── SKILL.md
└── sample_codes/
├── getting-started/
│ └── hello-kernel.cs
└── common-patterns/
├── chat-completion.cs
└── function-calling.cs
---
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`.
Weekly Installs
7.4K
Repository
GitHub Stars
26.7K
First Seen
Feb 11, 2026
Security Audits
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Installed on
codex7.3K
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opencode7.3K
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cursor7.3K
kimi-cli7.3K
97,600 周安装