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Agent Skill · 运维和基础设施

docker-containerization(Docker containerization 部署)

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。

docker-containerization 是运维和基础设施类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证为 MIT,第三方扫描有未通过项,按安装量在运维和基础设施类的 2,449 个 Skill 里排第 169。

总安装 12,536周安装 533GitHub Stars 356下载 4,392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
MIT

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:docker-containerization(Docker containerization 部署)
来源仓库:https://github.com/ailabs-393/ai-labs-claude-skills
仓库路径:skills/docker-containerization
安装命令:
npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill docker-containerization
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/ailabs-393/ai-labs-claude-skills --skill docker-containerization

这个 Skill 是做什么的

运维和基础设施类 Skill 处理部署、监控、日志和云资源,多数需要较高的执行权限。

为现代 Web 应用生成生产级 Docker 配置,支持 Next.js 和 Node.js 项目。提供优化的 Dockerfile、docker-compose 编排、容器管理脚本及多平台部署指南。通过交互式问答明确项目类型、依赖和环境需求后输出完整配置方案。

需确认目标语言栈、构建目标和运行环境,涉及生产部署时应验证镜像安全策略。docker-containerization 属于运维和基础设施类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/docker-containerization

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/ailabs-393/ai-labs-claude-skills --skill docke…;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

    它需要接触敏感数据,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

docker-containerization 怎么安装?

在终端执行 npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill docker-containe…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

docker-containerization 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。

docker-containerization 需要什么权限?

权限级别是接触敏感数据。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。

docker-containerization 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

docker-containerization 用的人多吗?

公开数据是累计安装 12,536 次,近一周安装 533 次,下载 4,392 次,GitHub 356 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。

docker-containerization 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理运维和基础设施类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

运维和基础设施类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
brainstorming

头脑风暴 · Codex、Claude

3,148,776173,131操作浏览器MIT通过
copywriting

文案写作 · Codex、Claude

1,996,72425,583需要联网MIT通过
openclaw-secure-linux-cloud

OpenClaw secure linux cloud 命令行 · Codex、Claude

1,853,08840接触敏感数据未明确未通过
xget

xget 命令行 · Codex、Claude

1,799,28040执行命令未明确未通过
requesting-code-review

代码审查请求 · Codex、Claude

1,590,144173,103权限未标注MIT通过
docker-containerization

Docker containerization 部署 · 本页

12,536356接触敏感数据MIT未通过

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

Docker Containerization Skill

Overview

Generate production-ready Docker configurations for modern web applications, particularly Next.js and Node.js projects. This skill provides Dockerfiles, docker-compose setups, bash scripts for container management, and comprehensive deployment guides for various orchestration platforms.

Core Capabilities

1. Dockerfile Generation

Create optimized Dockerfiles for different environments:

Production (assets/Dockerfile.production):

  • Multi-stage build reducing image size by 85%
  • Alpine Linux base (~180MB final image)
  • Non-root user execution for security
  • Health checks and resource limits

Development (assets/Dockerfile.development):

  • Hot reload support
  • All dev dependencies included
  • Volume mounts for live code updates

Nginx Static (assets/Dockerfile.nginx):

  • Static export optimization
  • Nginx reverse proxy included
  • Smallest possible footprint

2. Docker Compose Configuration

Multi-container orchestration with assets/docker-compose.yml:

  • Development and production services
  • Network and volume management
  • Health checks and logging
  • Restart policies

3. Bash Scripts for Container Management

docker-build.sh - Build images with comprehensive options:

./docker-build.sh -e prod -t v1.0.0
./docker-build.sh -n my-app --no-cache --platform linux/amd64

docker-run.sh - Run containers with full configuration:

./docker-run.sh -i my-app -t v1.0.0 -d
./docker-run.sh -p 8080:3000 --env-file .env.production

docker-push.sh - Push to registries (Docker Hub, ECR, GCR, ACR):

./docker-push.sh -n my-app -t v1.0.0 --repo username/my-app
./docker-push.sh -r gcr.io/project --repo my-app --also-tag stable

docker-cleanup.sh - Free disk space:

./docker-cleanup.sh --all --dry-run  # Preview cleanup
./docker-cleanup.sh --containers --images  # Clean specific resources

4. Configuration Files

  • .dockerignore: Excludes unnecessary files (node_modules,.git, logs)
  • nginx.conf: Production-ready Nginx configuration with compression, caching, security headers

5. Reference Documentation

docker-best-practices.md covers:

  • Multi-stage builds explained
  • Image optimization techniques (50-85% size reduction)
  • Security best practices (non-root users, vulnerability scanning)
  • Performance optimization
  • Health checks and logging
  • Troubleshooting guide

container-orchestration.md covers deployment to:

  • Docker Compose (local development)
  • Kubernetes (enterprise scale with auto-scaling)
  • Amazon ECS (AWS-native orchestration)
  • Google Cloud Run (serverless containers)
  • Azure Container Instances
  • Digital Ocean App Platform

Includes configuration examples, commands, auto-scaling setup, and monitoring.

Workflow Decision Tree

1. What environment?

  • Development → Dockerfile.development (hot reload, all dependencies)
  • Production → Dockerfile.production (minimal, secure, optimized)
  • Static Export → Dockerfile.nginx (smallest footprint)

2. Single or Multi-container?

  • Single → Generate Dockerfile only
  • Multi → Generate docker-compose.yml (app + database, microservices)

3. Which registry?

  • Docker Hub → docker.io/username/image
  • AWS ECR → 123456789012.dkr.ecr.region.amazonaws.com/image
  • Google GCR → gcr.io/project-id/image
  • Azure ACR → registry.azurecr.io/image

4. Deployment platform?

  • Kubernetes → See references/container-orchestration.md K8s section
  • ECS → See ECS task definition examples
  • Cloud Run → See deployment commands
  • Docker Compose → Use provided compose file

5. Optimizations needed?

  • Image size → Multi-stage builds, Alpine base
  • Build speed → Layer caching, BuildKit
  • Security → Non-root user, vulnerability scanning
  • Performance → Resource limits, health checks

Usage Examples

Example 1: Containerize Next.js App for Production

User: "Containerize my Next.js app for production"

Steps:

  1. Copy assets/Dockerfile.production to project root as Dockerfile
  2. Copy assets/.dockerignore to project root
  3. Build: ./docker-build.sh -e prod -n my-app -t v1.0.0
  4. Test: ./docker-run.sh -i my-app -t v1.0.0 -p 3000:3000 -d
  5. Push: ./docker-push.sh -n my-app -t v1.0.0 --repo username/my-app

Example 2: Development with Docker Compose

User: "Set up Docker Compose for local development"

Steps:

  1. Copy assets/Dockerfile.development and assets/docker-compose.yml to project
  2. Customize services in docker-compose.yml
  3. Start: docker-compose up -d
  4. Logs: docker-compose logs -f app-dev

Example 3: Deploy to Kubernetes

User: "Deploy my containerized app to Kubernetes"

Steps:

  1. Build and push image to registry
  2. Review references/container-orchestration.md Kubernetes section
  3. Create K8s manifests (deployment, service, ingress)
  4. Apply: kubectl apply -f deployment.yaml
  5. Verify: kubectl get pods && kubectl logs -f deployment/app

Example 4: Deploy to AWS ECS

User: "Deploy to AWS ECS Fargate"

Steps:

  1. Build and push to ECR
  2. Review references/container-orchestration.md ECS section
  3. Create task definition JSON
  4. Register: aws ecs register-task-definition --cli-input-json file://task-def.json
  5. Create service: aws ecs create-service --cluster my-cluster --service-name app --desired-count 3

Best Practices

Security

✅ Use multi-stage builds for production ✅ Run as non-root user ✅ Use specific image tags (not latest) ✅ Scan for vulnerabilities ✅ Never hardcode secrets ✅ Implement health checks

Performance

✅ Optimize layer caching order ✅ Use Alpine images (~85% smaller) ✅ Enable BuildKit for parallel builds ✅ Set resource limits ✅ Use compression

Maintainability

✅ Add comments for complex steps ✅ Use build arguments for flexibility ✅ Keep Dockerfiles DRY ✅ Version control all configs ✅ Document environment variables

Troubleshooting

Image too large (>500MB) → Use multi-stage builds, Alpine base, comprehensive.dockerignore

Build is slow → Optimize layer caching, use BuildKit, review dependencies

Container exits immediately → Check logs: docker logs container-name → Verify CMD/ENTRYPOINT, check port conflicts

Changes not reflecting → Rebuild without cache, check.dockerignore, verify volume mounts

Quick Reference

# Build
./docker-build.sh -e prod -t latest

# Run
./docker-run.sh -i app -t latest -d

# Logs
docker logs -f app

# Execute
docker exec -it app sh

# Cleanup
./docker-cleanup.sh --all --dry-run  # Preview
./docker-cleanup.sh --all            # Execute

Integration with CI/CD

GitHub Actions

- run: |
    chmod +x docker-build.sh docker-push.sh
    ./docker-build.sh -e prod -t ${{ github.sha }}
    ./docker-push.sh -n app -t ${{ github.sha }} --repo username/app

GitLab CI

build:
  script:
    - chmod +x docker-build.sh
    - ./docker-build.sh -e prod -t $CI_COMMIT_SHA

Resources

Scripts (scripts/)

Production-ready bash scripts with comprehensive features:

  • docker-build.sh - Build images (400+ lines, colorized output)
  • docker-run.sh - Run containers (400+ lines, auto conflict resolution)
  • docker-push.sh - Push to registries (multi-registry support)
  • docker-cleanup.sh - Clean resources (dry-run mode, selective cleanup)

References (references/)

Detailed documentation loaded as needed:

  • docker-best-practices.md - Comprehensive Docker best practices (~500 lines)
  • container-orchestration.md - Deployment guides for 6+ platforms (~600 lines)

Assets (assets/)

Ready-to-use templates:

  • Dockerfile.production - Multi-stage production Dockerfile
  • Dockerfile.development - Development Dockerfile
  • Dockerfile.nginx - Static export with Nginx
  • docker-compose.yml - Multi-container orchestration
  • .dockerignore - Optimized exclusion rules
  • nginx.conf - Production Nginx configuration

平台分布

Claude Code

26.23%
按下载量换算1,152

Codex

21.49%
按下载量换算944

OpenCode

16.69%
按下载量换算733

Gemini CLI

13.23%
按下载量换算581

Antigravity

7.29%
按下载量换算320

Cursor

3.13%
按下载量换算137

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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