Community라이팅 & 에디팅github.com

github/microsoft-docs

공식 Microsoft 문서에서 Azure, .NET, Agent Framework, Aspire, VS Code, GitHub 등에 대한 개념, 자습서 및 코드 예제를 검색합니다. Microsoft Learn MCP를 기본으로 사용하며, learn.microsoft.com 외부의 콘텐츠는 Context7 및 Aspire MCP를 통해 제공됩니다.

microsoft-docs란 무엇인가요?

이 스킬은 개발자와 AI 에이전트가 다양한 Microsoft 제품 및 서비스에 대한 공식 문서를 손쉽게 탐색하고 이해할 수 있도록 설계되었습니다. Azure, .NET, Agent Framework, Aspire, VS Code, GitHub 등 주요 플랫폼의 개념, 자습서(튜토리얼), 코드 예제를 포함한 방대한 문서를 질의할 수 있습니다. 기본적으로 Microsoft Learn MCP를 통해 learn.microsoft.com의 공식 문서에 접근하며, 해당 도메인에 없는 콘텐츠(예: GitHub의 오픈소스 프로젝트, Aspire 관련 외부 문서)는 Context7 및 Aspire MCP를 사용하여 검색합니다. 이를 통해 사용자는 단일 인터페이스에서 일관된 방식으로 Microsoft 생태계 전반의 지식을 얻고, 개발 워크플로우에 직접 통합할 수 있습니다. 이 스킬은 Claude Code, Cursor, Codex 등 MCP(Multi-Platform Connector)를 지원하는 도구에서 사용할 수 있으며, 문서 검색 결과를 코드 생성, 문제 해결, 학습에 즉시 활용할 수 있습니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/microsoft-docs

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

microsoft-docs은(는) 무엇을 하나요?

공식 Microsoft 문서에서 Azure, .NET, Agent Framework, Aspire, VS Code, GitHub 등에 대한 개념, 자습서 및 코드 예제를 검색합니다. 기본적으로 Microsoft Learn MCP를 사용하며, learn.microsoft.com 외부의 콘텐츠는 Context7 및 Aspire MCP를 통해 제공됩니다.

개요

이 기술은 Microsoft 제품 및 서비스에 대한 최신 문서에 접근할 수 있는 통합 인터페이스를 제공합니다. 다음을 포함한 다양한 주제를 지원합니다:

  • Azure: 클라우드 서비스, 인프라, DevOps
  • .NET: 프레임워크, 라이브러리, 도구
  • Agent Framework: AI 에이전트 빌드 및 통합
  • Aspire: 클라우드 네이티브 애플리케이션 개발
  • VS Code: 편집기 확장 및 사용자 정의
  • GitHub: 리포지토리 관리, CI/CD, 협업

사용 방법

# Azure에 대한 개념 검색 예시
query "Azure Functions 트리거란 무엇인가요?"

# .NET 자습서 검색
query "ASP.NET Core MVC 자습서"

# 코드 예제 검색
query "C#를 사용한 REST API 예제"

지원되는 MCP

MCP설명주요 콘텐츠 소스
Microsoft Learn MCP기본 문서 검색learn.microsoft.com
Context7 MCPGitHub 오픈소스 프로젝트 문서외부 저장소
Aspire MCPAspire 프레임워크 관련 콘텐츠learn.microsoft.com 외부

구성

이 기술은 자동으로 적절한 MCP를 선택하거나 수동으로 구성할 수 있습니다.

{
  "mcp": {
    "default": "microsoft-learn",
    "fallback": ["context7", "aspire"]
  }
}

예제 프롬프트

  • "Azure Blob Storage를 사용하는 방법을 알려주세요."
  • ".NET 8의 새로운 기능은 무엇인가요?"
  • "Agent Framework를 사용한 봇 예제를 찾아주세요."
  • "Aspire로 클라우드 네이티브 앱을 배포하는 방법"
  • "VS Code에서 GitHub Copilot을 설정하는 방법"

참고 사항

  • 이 기술은 최신 Microsoft 문서를 기반으로 합니다.
  • 검색 결과는 개념, 자습서, 코드 예제로 분류됩니다.
  • learn.microsoft.com 외부의 콘텐츠는 해당 MCP 제공자의 정책에 따라 제공됩니다.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

관련 스킬