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github/mcp-deploy-manage-agents

MCP 에이전트 배포 및 관리를 위한 스킬로, GitHub 기반의 에이전트 배포 파이프라인을 설정하고 관리합니다.

mcp-deploy-manage-agents란 무엇인가요?

이 스킬은 MCP(Model Context Protocol) 기반 에이전트를 GitHub을 활용하여 배포하고 관리하는 방법을 제공합니다. 개발자는 이 스킬을 통해 클라우드 환경(예: AWS, GCP, Azure)이나 자체 호스팅 서버에 MCP 에이전트를 배포하는 CI/CD 파이프라인을 구성할 수 있습니다. GitHub Actions와 같은 자동화 도구와 통합되어 버전 관리, 테스트, 배포 과정을 간소화하며, 에이전트의 설정 파일, 환경 변수, 의존성 관리를 체계적으로 수행할 수 있습니다. 이 스킬은 Claude Code, Cursor, Codex 등 다양한 AI 에이전트 플랫폼에서 사용 가능하며, 특히 MCP 서버를 운영하는 개발자에게 유용합니다. 배포된 에이전트의 상태를 모니터링하고, 롤백 전략을 수립하며, 보안 설정을 관리하는 방법도 포함되어 있어 프로덕션 환경에서 안정적인 에이전트 운영을 지원합니다.

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문서

mcp-deploy-manage-agents은(는) 무엇을 하나요?

MCP 에이전트 배포 및 관리를 위한 스킬입니다.

이 스킬은 MCP(Model Context Protocol) 기반 에이전트를 GitHub을 사용하여 배포하고 관리하는 방법을 안내합니다. 주요 기능은 다음과 같습니다:

  • CI/CD 파이프라인 설정: GitHub Actions를 사용하여 자동화된 배포 파이프라인 구축
  • 환경 관리: 개발, 스테이징, 프로덕션 환경별 설정 관리
  • 버전 관리: Git 태그와 릴리스를 통한 에이전트 버전 관리
  • 모니터링 및 로깅: 배포된 에이전트의 상태를 모니터링하고 로그 수집
  • 롤백 전략: 문제 발생 시 빠른 롤백을 위한 절차 수립
  • 보안 설정: API 키, 환경 변수 등 민감 정보를 안전하게 관리

사전 요구 사항

  • GitHub 계정 및 저장소
  • MCP 에이전트 코드 (Node.js, Python 등)
  • 클라우드 서비스 계정 (예: AWS, GCP, Azure) 또는 자체 호스팅 서버
  • Docker (선택 사항, 컨테이너화된 배포를 위해)

사용 방법

  1. 저장소를 클론합니다:

    git clone https://github.com/your-username/your-agent-repo.git
    cd your-agent-repo
    
  2. 배포 설정 파일을 구성합니다:

    # .github/workflows/deploy.yml
    name: Deploy MCP Agent
    
    on:
      push:
        branches: [main]
    
    jobs:
      deploy:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v3
          - name: Deploy to Cloud
            run: |
              echo "Deploying MCP agent..."
              # 배포 명령어 추가
    
  3. 환경 변수를 설정합니다:

    # .env.example
    MCP_SERVER_PORT=3000
    DATABASE_URL=postgresql://user:password@localhost:5432/mcp_agent
    API_KEY=your-api-key
    
  4. 배포 스크립트를 실행합니다:

    npm run deploy
    # 또는
    python deploy.py
    

설정 세부 사항

GitHub Actions 워크플로우

워크플로우 파일은 .github/workflows/ 디렉토리에 위치합니다. 주요 단계:

  1. 코드 체크아웃: 저장소에서 최신 코드 가져오기
  2. 의존성 설치: npm install 또는 pip install -r requirements.txt
  3. 테스트 실행: 단위 테스트 및 통합 테스트 수행
  4. 빌드: Docker 이미지 빌드 또는 애플리케이션 번들링
  5. 배포: 대상 환경에 배포 (SSH, Kubernetes, Serverless 등)
  6. 상태 확인: 배포 후 헬스 체크 엔드포인트 확인

환경 변수 관리

GitHub Secrets를 사용하여 민감한 환경 변수를 관리합니다:

gh secret set DEPLOY_KEY < your-private-key.pem
gh secret set DATABASE_URL

문제 해결

  • 배포 실패: 워크플로우 로그를 확인하고, 필요한 권한이 있는지 검토합니다.
  • 에이전트 연결 오류: 환경 변수와 설정 파일을 확인합니다.
  • 롤백 필요 시: git revert 또는 이전 릴리스 태그로 되돌립니다.

참고 자료


이 스킬은 mcp-deploy-manage-agents.prompt.md에서 변환되었습니다.

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.

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