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Agent Skill · 开发

devops-infrastructure(开发运营基础设施)

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

devops-infrastructure 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 10,800。

总安装 2,072周安装 83GitHub Stars 1,362下载 671核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:devops-infrastructure(开发运营基础设施)
来源仓库:https://github.com/cloudai-x/claude-workflow-v2
仓库路径:skills/devops-infrastructure
安装命令:
npx skills add https://github.com/cloudai-x/claude-workflow-v2 --skill devops-infrastructure
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/cloudai-x/claude-workflow-v2 --skill devops-infrastructure

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于辅助云资源、部署、容器和基础设施管理,适合检查配置、整理部署步骤或生成排障思路。适用于需要自动化运维、CI/CD 流水线搭建或多环境部署的场景。通过 GitHub 安装,提供容器化、监控和部署策略模板。使用时需明确目标环境、账号权限和资源组,区分测试与生产操作。

涉及删除或修改网络配置时应先评估影响范围,避免误操作导致服务中断。

来源仓库内路径:skills/devops-infrastructure

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/cloudai-x/claude-workflow-v2 --skill devops-in…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

devops-infrastructure 怎么安装?

在终端执行 npx skills add https://github.com/cloudai-x/claude-workflow-v2 --skill devops-infrastructu…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

devops-infrastructure 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

devops-infrastructure 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

devops-infrastructure 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

devops-infrastructure 用的人多吗?

公开数据是累计安装 2,072 次,近一周安装 83 次,下载 671 次,GitHub 1,362 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

devops-infrastructure 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 1,362 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
devops-infrastructure

开发运营基础设施 · 本页

2,0721,362需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

DevOps & Infrastructure

When to Load

  • Trigger: Docker, CI/CD pipelines, deployment configuration, monitoring, infrastructure as code
  • Skip: Application logic only with no infrastructure or deployment concerns

DevOps Workflow

Copy this checklist and track progress:

DevOps Setup Progress:
- [ ] Step 1: Containerize application (Dockerfile)
- [ ] Step 2: Set up CI/CD pipeline
- [ ] Step 3: Define deployment strategy
- [ ] Step 4: Configure monitoring & alerting
- [ ] Step 5: Set up environment management
- [ ] Step 6: Document runbooks
- [ ] Step 7: Validate against anti-patterns checklist

Docker Best Practices

Multi-Stage Build

# WRONG: Single stage, bloated image
FROM node:20
WORKDIR /app
COPY . .
RUN npm install
RUN npm run build
CMD ["node", "dist/index.js"]
# Result: 1.2GB image with devDependencies and source code

# CORRECT: Multi-stage build
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
ENV NODE_ENV=production
RUN addgroup -g 1001 appgroup && adduser -u 1001 -G appgroup -s /bin/sh -D appuser
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./
USER appuser
EXPOSE 3000
CMD ["node", "dist/index.js"]
# Result: ~150MB image, no devDependencies, non-root user

Python Multi-Stage

FROM python:3.12-slim AS builder
WORKDIR /app
RUN pip install uv
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev
COPY . .

FROM python:3.12-slim AS runner
WORKDIR /app
RUN useradd -r -s /bin/false appuser
COPY --from=builder /app/.venv /app/.venv
COPY --from=builder /app/src ./src
ENV PATH="/app/.venv/bin:$PATH"
USER appuser
CMD ["python", "-m", "src.main"]

Layer Caching

# WRONG: Cache busted on every code change
COPY . .
RUN npm ci

# CORRECT: Dependencies cached separately
COPY package*.json ./
RUN npm ci                  # cached unless package.json changes
COPY . .                    # only source code changes bust this layer

.dockerignore

node_modules
.git
.env
*.md
.vscode
coverage
dist
__pycache__
.pytest_cache
*.pyc

Security

# Always pin versions
FROM node:20.11.0-alpine   # NOT node:latest

# Don't run as root
USER appuser

# Read-only filesystem where possible
# docker run --read-only --tmpfs /tmp myapp

# Scan images
# docker scout cves myimage:latest
# trivy image myimage:latest

CI/CD Pipeline Design

GitHub Actions Structure

name: CI/CD
on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: "npm"
      - run: npm ci
      - run: npm run lint

  test:
    runs-on: ubuntu-latest
    needs: lint
    services:
      postgres:
        image: postgres:16
        env:
          POSTGRES_DB: testdb
        ports: ["5432:5432"]
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: "npm"
      - run: npm ci
      - run: npm test

  build:
    runs-on: ubuntu-latest
    needs: test
    steps:
      - uses: actions/checkout@v4
      - uses: docker/setup-buildx-action@v3
      - uses: docker/build-push-action@v5
        with:
          push: ${{ github.event_name == 'push' }}
          tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
          cache-from: type=gha
          cache-to: type=gha,mode=max

  deploy:
    runs-on: ubuntu-latest
    needs: build
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - run: echo "Deploy to production"

Caching Strategies

# Node modules
- uses: actions/setup-node@v4
  with:
    cache: "npm"

# Python with uv
- name: Cache uv
  uses: actions/cache@v4
  with:
    path: ~/.cache/uv
    key: uv-${{ runner.os }}-${{ hashFiles('uv.lock') }}

# Docker layer caching
- uses: docker/build-push-action@v5
  with:
    cache-from: type=gha
    cache-to: type=gha,mode=max

Deployment Strategies

Blue-Green Deployment

1. Run two identical environments: Blue (live) and Green (idle)
2. Deploy new version to Green
3. Run smoke tests on Green
4. Switch load balancer to Green
5. Green is now live, Blue is idle
6. Rollback: switch back to Blue

Pros: Instant rollback, zero downtime
Cons: 2x infrastructure cost during deploy

Canary Deployment

1. Deploy new version to small subset (5% of traffic)
2. Monitor error rates and latency
3. Gradually increase: 5% -> 25% -> 50% -> 100%
4. Rollback: route all traffic back to old version

Pros: Limited blast radius, real-world testing
Cons: More complex routing, longer rollout

Rolling Deployment

1. Replace instances one at a time
2. Each new instance passes health checks before next starts
3. Continue until all instances updated

Pros: No extra infrastructure, gradual rollout
Cons: Mixed versions during deploy, slower rollback

Feature Flags

// Simple feature flag implementation
const features = {
  NEW_CHECKOUT: process.env.FF_NEW_CHECKOUT === "true",
  DARK_MODE: process.env.FF_DARK_MODE === "true",
};

function getCheckoutFlow(user: User) {
  if (features.NEW_CHECKOUT && user.betaGroup) {
    return newCheckoutFlow(user);
  }
  return legacyCheckoutFlow(user);
}

// Use a proper service for production: LaunchDarkly, Unleash, Flagsmith

Infrastructure as Code

Terraform Basics

# main.tf
terraform {
  required_version = ">= 1.5"
  backend "s3" {
    bucket = "myapp-terraform-state"
    key    = "prod/terraform.tfstate"
    region = "us-east-1"
  }
}

resource "aws_instance" "web" {
  ami           = var.ami_id
  instance_type = var.instance_type
  tags = {
    Name        = "web-${var.environment}"
    Environment = var.environment
    ManagedBy   = "terraform"
  }
}

# variables.tf
variable "environment" {
  type    = string
  default = "dev"
}

variable "instance_type" {
  type    = string
  default = "t3.micro"
}

Terraform Rules

1. Always use remote state (S3, GCS, Terraform Cloud)
2. Lock state files to prevent concurrent modifications
3. Use variables and modules for reusability
4. Tag all resources with environment and ManagedBy
5. Run `terraform plan` before `terraform apply`
6. Never edit infrastructure manually (all changes via code)
7. Use workspaces or separate state files per environment

Monitoring & Observability

The Three Pillars

METRICS: Numeric measurements over time
  - Request rate, error rate, latency (RED method)
  - CPU, memory, disk, network (USE method)
  - Business metrics (signups, purchases)
  Tools: Prometheus, Datadog, CloudWatch

LOGS: Discrete events with context
  - Structured JSON format
  - Correlation IDs across services
  - Log levels: DEBUG, INFO, WARN, ERROR
  Tools: ELK Stack, Loki, CloudWatch Logs

TRACES: Request flow across services
  - Distributed tracing with span context
  - Latency breakdown per service
  - Dependency mapping
  Tools: Jaeger, Zipkin, Datadog APM

Health Check Endpoint

// Express health check
app.get("/health", async (req, res) => {
  const checks = {
    uptime: process.uptime(),
    timestamp: Date.now(),
    database: "unknown",
    redis: "unknown",
  };

  try {
    await db.query("SELECT 1");
    checks.database = "healthy";
  } catch (e) {
    checks.database = "unhealthy";
  }

  try {
    await redis.ping();
    checks.redis = "healthy";
  } catch (e) {
    checks.redis = "unhealthy";
  }

  const isHealthy = checks.database === "healthy";
  res.status(isHealthy ? 200 : 503).json(checks);
});

Alerting Rules

Good alerts:
- Error rate > 1% for 5 minutes (actionable)
- P99 latency > 2s for 10 minutes (meaningful)
- Disk usage > 80% (preventive)

Bad alerts:
- CPU spike for 30 seconds (too noisy)
- Any single 500 error (too sensitive)
- "Something might be wrong" (not actionable)

Alert fatigue is real. Every alert should require human action.

Environment Management

Dev/Staging/Prod Parity

# docker-compose.yml for local development
services:
  app:
    build: .
    env_file: .env
    ports: ["3000:3000"]
    depends_on:
      postgres:
        condition: service_healthy

  postgres:
    image: postgres:16
    environment:
      POSTGRES_DB: myapp
    healthcheck:
      test: ["CMD-SHELL", "pg_isready"]
      interval: 5s
    volumes:
      - pgdata:/var/lib/postgresql/data

  redis:
    image: redis:7-alpine
    ports: ["6379:6379"]

volumes:
  pgdata:

Environment Variables

# .env.example (committed to git, no real values)
DATABASE_URL=postgresql://user:placeholder@localhost:5432/myapp
REDIS_URL=redis://localhost:6379
LOG_LEVEL=debug
API_KEY=your-key-here

# .env (never committed, listed in .gitignore)
# Contains real values for local development

Common Anti-Patterns Summary

AVOID                              DO INSTEAD
-------------------------------------------------------------------
FROM node:latest                   Pin exact versions (node:20.11.0-alpine)
Running as root in container       Create and use non-root user
No .dockerignore                   Exclude .git, node_modules, .env
Single CI job does everything      Separate lint, test, build, deploy stages
Manual deployment                  Automated pipeline with approvals
No health checks                   Liveness + readiness probes
Alerts on every error              Alert on error RATE thresholds
Same config in all environments    Per-environment configuration
No rollback plan                   Test rollback before every deploy
Logs as unstructured strings       Structured JSON logs with correlation IDs

平台分布

Codex

38.58%
按下载量换算259

Claude

28.64%
按下载量换算192

Cursor

19.68%
按下载量换算132

Gemini CLI

9.52%
按下载量换算64

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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