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devopsDevOps 部署

Agent Skill

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。

总安装

190

周安装

8

GitHub Stars

3

下载量

67
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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(DevOps 部署)
来源仓库:https://github.com/vibery-studio/templates
仓库路径:skills/devops
安装命令:
npx skills add https://github.com/vibery-studio/templates --skill devops
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vibery-studio/templates --skill devops

简介

devops 用于辅助云资源、部署、容器、基础设施和运维自动化任务。

  • 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。
  • 通过 npx skills add 命令从指定仓库安装,需结合原始 README 核验具体用法。
  • 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。
  • devops 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

DevOps Skill

Comprehensive guide for deploying and managing cloud infrastructure across Cloudflare edge platform, Docker containerization, and Google Cloud Platform.

When to Use This Skill

Use this skill when:

  • Deploying serverless applications to Cloudflare Workers
  • Containerizing applications with Docker
  • Managing Google Cloud infrastructure with gcloud CLI
  • Setting up CI/CD pipelines across platforms
  • Optimizing cloud infrastructure costs
  • Implementing multi-region deployments
  • Building edge-first architectures
  • Managing container orchestration with Kubernetes
  • Configuring cloud storage solutions (R2, Cloud Storage)
  • Automating infrastructure with scripts and IaC

Platform Selection Guide

When to Use Cloudflare

Best For:

  • Edge-first applications with global distribution
  • Ultra-low latency requirements (<50ms)
  • Static sites with serverless functions
  • Zero egress cost scenarios (R2 storage)
  • WebSocket/real-time applications (Durable Objects)
  • AI/ML at the edge (Workers AI)

Key Products:

  • Workers (serverless functions)
  • R2 (object storage, S3-compatible)
  • D1 (SQLite database with global replication)
  • KV (key-value store)
  • Pages (static hosting + functions)
  • Durable Objects (stateful compute)
  • Browser Rendering (headless browser automation)

Cost Profile: Pay-per-request, generous free tier, zero egress fees

When to Use Docker

Best For:

  • Local development consistency
  • Microservices architectures
  • Multi-language stack applications
  • Traditional VPS/VM deployments
  • Kubernetes orchestration
  • CI/CD build environments
  • Database containerization (dev/test)

Key Capabilities:

  • Application isolation and portability
  • Multi-stage builds for optimization
  • Docker Compose for multi-container apps
  • Volume management for data persistence
  • Network configuration and service discovery
  • Cross-platform compatibility (amd64, arm64)

Cost Profile: Infrastructure cost only (compute + storage)

When to Use Google Cloud

Best For:

  • Enterprise-scale applications
  • Data analytics and ML pipelines (BigQuery, Vertex AI)
  • Hybrid/multi-cloud deployments
  • Kubernetes at scale (GKE)
  • Managed databases (Cloud SQL, Firestore, Spanner)
  • Complex IAM and compliance requirements

Key Services:

  • Compute Engine (VMs)
  • GKE (managed Kubernetes)
  • Cloud Run (containerized serverless)
  • App Engine (PaaS)
  • Cloud Storage (object storage)
  • Cloud SQL (managed databases)

Cost Profile: Varied pricing, sustained use discounts, committed use contracts

Quick Start

Cloudflare Workers

# Install Wrangler CLI
npm install -g wrangler

# Create and deploy Worker
wrangler init my-worker
cd my-worker
wrangler deploy

See: references/cloudflare-workers-basics.md

Docker Container

# Create Dockerfile
cat > Dockerfile <<EOF
FROM node:20-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --production
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]
EOF

# Build and run
docker build -t myapp .
docker run -p 3000:3000 myapp

See: references/docker-basics.md

Google Cloud Deployment

# Install and authenticate
curl https://sdk.cloud.google.com | bash
gcloud init
gcloud auth login

# Deploy to Cloud Run
gcloud run deploy my-service \
  --image gcr.io/project/image \
  --region us-central1

See: references/gcloud-platform.md

Reference Navigation

Cloudflare Platform

  • cloudflare-platform.md - Edge computing overview, key components
  • cloudflare-workers-basics.md - Getting started, handler types, basic patterns
  • cloudflare-workers-advanced.md - Advanced patterns, performance, optimization
  • cloudflare-workers-apis.md - Runtime APIs, bindings, integrations
  • cloudflare-r2-storage.md - R2 object storage, S3 compatibility, best practices
  • cloudflare-d1-kv.md - D1 SQLite database, KV store, use cases
  • browser-rendering.md - Puppeteer/Playwright automation on Cloudflare

Docker Containerization

  • docker-basics.md - Core concepts, Dockerfile, images, containers
  • docker-compose.md - Multi-container apps, networking, volumes

Google Cloud Platform

  • gcloud-platform.md - GCP overview, gcloud CLI, authentication
  • gcloud-services.md - Compute Engine, GKE, Cloud Run, App Engine

Python Utilities

  • scripts/cloudflare-deploy.py - Automate Cloudflare Worker deployments
  • scripts/docker-optimize.py - Analyze and optimize Dockerfiles

Common Workflows

Edge + Container Hybrid

# Cloudflare Workers (API Gateway)
# -> Docker containers on Cloud Run (Backend Services)
# -> R2 (Object Storage)

# Benefits:
# - Edge caching and routing
# - Containerized business logic
# - Global distribution

Multi-Stage Docker Build

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

# Production stage
FROM node:20-alpine
WORKDIR /app
COPY --from=build /app/dist ./dist
COPY --from=build /app/node_modules ./node_modules
USER node
CMD ["node", "dist/server.js"]

CI/CD Pipeline Pattern

# 1. Build: Docker multi-stage build
# 2. Test: Run tests in container
# 3. Push: Push to registry (GCR, Docker Hub)
# 4. Deploy: Deploy to Cloudflare Workers / Cloud Run
# 5. Verify: Health checks and smoke tests

Best Practices

Security

  • Run containers as non-root user
  • Use service account impersonation (GCP)
  • Store secrets in environment variables, not code
  • Scan images for vulnerabilities (Docker Scout)
  • Use API tokens with minimal permissions

Performance

  • Multi-stage Docker builds to reduce image size
  • Edge caching with Cloudflare KV
  • Use R2 for zero egress cost storage
  • Implement health checks for containers
  • Set appropriate timeouts and resource limits

Cost Optimization

  • Use Cloudflare R2 instead of S3 for large egress
  • Implement caching strategies (edge + KV)
  • Right-size container resources
  • Use sustained use discounts (GCP)
  • Monitor usage with cloud provider dashboards

Development

  • Use Docker Compose for local development
  • Wrangler dev for local Worker testing
  • Named gcloud configurations for multi-environment
  • Version control infrastructure code
  • Implement automated testing in CI/CD

Decision Matrix

NeedChoose
Sub-50ms latency globallyCloudflare Workers
Large file storage (zero egress)Cloudflare R2
SQL database (global reads)Cloudflare D1
Containerized workloadsDocker + Cloud Run/GKE
Enterprise KubernetesGKE
Managed relational DBCloud SQL
Static site + APICloudflare Pages
WebSocket/real-timeCloudflare Durable Objects
ML/AI pipelinesGCP Vertex AI
Browser automationCloudflare Browser Rendering

Resources

Implementation Checklist

Cloudflare Workers

  • Install Wrangler CLI
  • Create Worker project
  • Configure wrangler.toml (bindings, routes)
  • Test locally with wrangler dev
  • Deploy with wrangler deploy

Docker

  • Write Dockerfile with multi-stage builds
  • Create.dockerignore file
  • Test build locally
  • Push to registry
  • Deploy to target platform

Google Cloud

  • Install gcloud CLI
  • Authenticate with service account
  • Create project and enable APIs
  • Configure IAM permissions
  • Deploy and monitor resources

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenCode

28.96%
按下载量换算19

Codex

23.24%
按下载量换算16

Claude Code

19.57%
按下载量换算13

Antigravity

11.35%
按下载量换算8

Gemini CLI

7.11%
按下载量换算5

windsurf

3.71%
按下载量换算2

安全审计

Gen Agent Trust Hub

未通过

Socket

未通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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