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Docker Patterns

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xu-xiang
docker-patterns

用于本地开发的Docker和Docker Compose模式,包括容器安全、网络、卷策略和多服务编排。

Overview

Publisherxu-xiang
Repositoryeverything-claude-code-zh
Skill namedocker-patterns
Stars
1.9K
Forks
318
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by xu-xiang on GitHub. Read the source before you install it.

Installation

Install the Docker Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zh.git /tmp/everything-claude-code-zh
mkdir -p .claude/skills
cp -r /tmp/everything-claude-code-zh/docs/zh-CN/skills/docker-patterns .claude/skills/docker-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docker Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Docker Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Docker Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Docker 模式

适用于容器化开发的 Docker 和 Docker Compose 最佳实践。

何时启用

  • 为本地开发设置 Docker Compose
  • 设计多容器架构
  • 排查容器网络或卷问题
  • 审查 Dockerfile 的安全性和大小
  • 从本地开发迁移到容器化工作流

用于本地开发的 Docker Compose

标准 Web 应用栈

yaml
# docker-compose.yml
services:
  app:
    build:
      context: .
      target: dev                     # Use dev stage of multi-stage Dockerfile
    ports:
      - "3000:3000"
    volumes:
      - .:/app                        # Bind mount for hot reload
      - /app/node_modules             # Anonymous volume -- preserves container deps
    environment:
      - DATABASE_URL=postgres://postgres:postgres@db:5432/app_dev
      - REDIS_URL=redis://redis:6379/0
      - NODE_ENV=development
    depends_on:
      db:
        condition: service_healthy
      redis:
        condition: service_started
    command: npm run dev

  db:
    image: postgres:16-alpine
    ports:
      - "5432:5432"
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: app_dev
    volumes:
      - pgdata:/var/lib/postgresql/data
      - ./scripts/init-db.sql:/docker-entrypoint-initdb.d/init.sql
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U postgres"]
      interval: 5s
      timeout: 3s
      retries: 5

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redisdata:/data

  mailpit:                            # Local email testing
    image: axllent/mailpit
    ports:
      - "8025:8025"                   # Web UI
      - "1025:1025"                   # SMTP

volumes:
  pgdata:
  redisdata:

开发与生产 Dockerfile

dockerfile
# Stage: dependencies
FROM node:22-alpine AS deps
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci

# Stage: dev (hot reload, debug tools)
FROM node:22-alpine AS dev
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .
EXPOSE 3000
CMD ["npm", "run", "dev"]

# Stage: build
FROM node:22-alpine AS build
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .
RUN npm run build && npm prune --production

# Stage: production (minimal image)
FROM node:22-alpine AS production
WORKDIR /app
RUN addgroup -g 1001 -S appgroup && adduser -S appuser -u 1001
USER appuser
COPY --from=build --chown=appuser:appgroup /app/dist ./dist
COPY --from=build --chown=appuser:appgroup /app/node_modules ./node_modules
COPY --from=build --chown=appuser:appgroup /app/package.json ./
ENV NODE_ENV=production
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s CMD wget -qO- http://localhost:3000/health || exit 1
CMD ["node", "dist/server.js"]

覆盖文件

yaml
# docker-compose.override.yml (auto-loaded, dev-only settings)
services:
  app:
    environment:
      - DEBUG=app:*
      - LOG_LEVEL=debug
    ports:
      - "9229:9229"                   # Node.js debugger

# docker-compose.prod.yml (explicit for production)
services:
  app:
    build:
      target: production
    restart: always
    deploy:
      resources:
        limits:
          cpus: "1.0"
          memory: 512M
bash
# Development (auto-loads override)
docker compose up

# Production
docker compose -f docker-compose.yml -f docker-compose.prod.yml up -d

网络

服务发现

同一 Compose 网络中的服务可通过服务名解析:

# From "app" container:
postgres://postgres:postgres@db:5432/app_dev    # "db" resolves to the db container
redis://redis:6379/0                             # "redis" resolves to the redis container

自定义网络

yaml
services:
  frontend:
    networks:
      - frontend-net

  api:
    networks:
      - frontend-net
      - backend-net

  db:
    networks:
      - backend-net              # Only reachable from api, not frontend

networks:
  frontend-net:
  backend-net:

仅暴露所需内容

yaml
services:
  db:
    ports:
      - "127.0.0.1:5432:5432"   # Only accessible from host, not network
    # Omit ports entirely in production -- accessible only within Docker network

卷策略

yaml
volumes:
  # Named volume: persists across container restarts, managed by Docker
  pgdata:

  # Bind mount: maps host directory into container (for development)
  # - ./src:/app/src

  # Anonymous volume: preserves container-generated content from bind mount override
  # - /app/node_modules

常见模式

yaml
services:
  app:
    volumes:
      - .:/app                   # Source code (bind mount for hot reload)
      - /app/node_modules        # Protect container's node_modules from host
      - /app/.next               # Protect build cache

  db:
    volumes:
      - pgdata:/var/lib/postgresql/data          # Persistent data
      - ./scripts/init.sql:/docker-entrypoint-initdb.d/init.sql  # Init scripts

容器安全

Dockerfile 加固

dockerfile
# 1. Use specific tags (never :latest)
FROM node:22.12-alpine3.20

# 2. Run as non-root
RUN addgroup -g 1001 -S app && adduser -S app -u 1001
USER app

# 3. Drop capabilities (in compose)
# 4. Read-only root filesystem where possible
# 5. No secrets in image layers

Compose 安全

yaml
services:
  app:
    security_opt:
      - no-new-privileges:true
    read_only: true
    tmpfs:
      - /tmp
      - /app/.cache
    cap_drop:
      - ALL
    cap_add:
      - NET_BIND_SERVICE          # Only if binding to ports < 1024

密钥管理

yaml
# GOOD: Use environment variables (injected at runtime)
services:
  app:
    env_file:
      - .env                     # Never commit .env to git
    environment:
      - API_KEY                  # Inherits from host environment

# GOOD: Docker secrets (Swarm mode)
secrets:
  db_password:
    file: ./secrets/db_password.txt

services:
  db:
    secrets:
      - db_password

# BAD: Hardcoded in image
# ENV API_KEY=sk-proj-xxxxx      # NEVER DO THIS

.dockerignore

node_modules
.git
.env
.env.*
dist
coverage
*.log
.next
.cache
docker-compose*.yml
Dockerfile*
README.md
tests/

调试

常用命令

bash
# View logs
docker compose logs -f app           # Follow app logs
docker compose logs --tail=50 db     # Last 50 lines from db

# Execute commands in running container
docker compose exec app sh           # Shell into app
docker compose exec db psql -U postgres  # Connect to postgres

# Inspect
docker compose ps                     # Running services
docker compose top                    # Processes in each container
docker stats                          # Resource usage

# Rebuild
docker compose up --build             # Rebuild images
docker compose build --no-cache app   # Force full rebuild

# Clean up
docker compose down                   # Stop and remove containers
docker compose down -v                # Also remove volumes (DESTRUCTIVE)
docker system prune                   # Remove unused images/containers

调试网络问题

bash
# Check DNS resolution inside container
docker compose exec app nslookup db

# Check connectivity
docker compose exec app wget -qO- http://api:3000/health

# Inspect network
docker network ls
docker network inspect <project>_default

反模式

# BAD: Using docker compose in production without orchestration
# Use Kubernetes, ECS, or Docker Swarm for production multi-container workloads

# BAD: Storing data in containers without volumes
# Containers are ephemeral -- all data lost on restart without volumes

# BAD: Running as root
# Always create and use a non-root user

# BAD: Using :latest tag
# Pin to specific versions for reproducible builds

# BAD: One giant container with all services
# Separate concerns: one process per container

# BAD: Putting secrets in docker-compose.yml
# Use .env files (gitignored) or Docker secrets

Frequently asked questions

What does the Docker Patterns AI skill do?

用于本地开发的Docker和Docker Compose模式,包括容器安全、网络、卷策略和多服务编排。

Why use Docker Patterns on TypingMind?

Because you install it once and use it with any model. Docker Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Docker Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xu-xiang/everything-claude-code-zh/tree/main/docs/zh-CN/skills/docker-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Docker Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Docker Patterns?

As many as you like. As long as a model supports skills, you can use Docker Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Docker Patterns AI skill free?

Yes. It is published on GitHub by xu-xiang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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