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docker-patterns

Docker and Docker Compose patterns for local development, hardened CLI installer harnesses, container security, networking, volumes, and multi-service orchestration. Use when creating or reviewing Dockerfiles and Compose services, testing installers across Linux distributions, or planning accurate native macOS and Windows validation.

docker-patterns 是什么?

docker-patterns is a Claude Code agent skill that docker and Docker Compose patterns for local development, hardened CLI installer harnesses, container security, networking, volumes, and multi-service orchestration. Use when creating or reviewing Dockerfiles and Compose services, testing installers across Linux distributions, or planning accurate native macOS and Windows validation.

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Docker Patterns

Docker and Docker Compose best practices for containerized development.

Docker Compose for Local Development

Standard Web App Stack

# 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:

Development vs Production 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"]

Override Files

# 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
# Development (auto-loads override)
docker compose up

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

Networking

Service Discovery

Services in the same Compose network resolve by service name:

# 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

Custom Networks

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:

Exposing Only What's Needed

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

Volume Strategies

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

Common Patterns

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

Container Security

Dockerfile Hardening

# 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 Security

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

Secret Management

# 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

Hardened CLI Installer Harnesses

Use containers to test installer behavior against disposable project copies without allowing the test to mutate the source checkout.

Respect the Platform Boundary

  • Run real containers for Linux distributions such as Debian and Ubuntu.
  • macOS cannot run as a Docker container because Docker shares a Linux kernel. Run the same shell-free test entry point natively on macOS.
  • Windows containers require a Windows Docker engine. Run platform-independent logic on a native Windows CI runner and reserve Windows containers for a Windows host.
  • Keep a native Ubuntu/macOS/Windows CI matrix for host-specific paths, command shims, quoting, and filesystem behavior.

Do not claim that a Linux container validates macOS or Windows behavior.

Enforce the Isolation Contract

  • Pin base images by immutable digest and pin installed CLI versions.
  • Run as a non-root numeric UID/GID when distro account names differ.
  • Mount the repository and source project read-only.
  • Copy the source project into a writable tmpfs workspace before any mutation.
  • Mount /workspace with noexec, UID/GID 1000, and mode=0700 so only the container user can inspect project data.
  • Keep npm and npx's executable cache at NPM_CONFIG_CACHE=/tmp/npm-cache on the executable /tmp mount. Its default size is 2 GiB and can be adjusted with ECC_TMPFS_SIZE; ECC_WORKSPACE_SIZE separately controls the private workspace mount.
  • Set read_only: true, no-new-privileges:true, cap_drop: [ALL], and a finite pids_limit.
  • Keep the default real-CLI services on network_mode: none. Add network access only through a visibly named opt-in service for an authenticated provider session; never make it an accidental environment-driven default.
  • Create only the writable temporary paths the tool needs.
  • Do not pass host credentials into the container by default.
  • Default to a dry run and whitelist only the explicit dry-run, install, plugin, and shell modes.
  • Use argument arrays or spawnSync(..., { shell: false }) for cross-platform runners. Never interpolate project paths into a shell command.

Exercise the ECC Plugin Setup Harness

Use docker/plugin-setup/compose.yaml as the reference implementation. It provides:

  • fixture-tests for the focused install manifest, target, and executor suite.
  • real-cli for the pinned Debian-based generic Linux image.
  • real-cli-ubuntu for the pinned Ubuntu image.

Validate the Compose model before building:

docker compose -f docker/plugin-setup/compose.yaml config --quiet

Build both real Linux images:

docker compose -f docker/plugin-setup/compose.yaml \
  build real-cli real-cli-ubuntu

Run the safe default flow in each image:

docker compose -p ecc-plugin-debian-test \
  -f docker/plugin-setup/compose.yaml \
  run --rm -T real-cli dry-run

docker compose -p ecc-plugin-ubuntu-test \
  -f docker/plugin-setup/compose.yaml \
  run --rm -T real-cli-ubuntu dry-run

The dry run executes the current public command contract:

ecc install --profile core --target claude-project --dry-run --json

Before that command runs, the container creates a locally packed npm artifact from the read-only checkout with npm pack --ignore-scripts. It extracts the self-created tarball under /tmp, validates the ecc-universal package name, required install manifests, and the confined package.json bin.ecc mapping, then invokes the extracted ecc executable. The runtime stays on network_mode: none, does not execute package lifecycle scripts, and does not rely on host node_modules; its exact pinned production dependencies are already present in the image.

The harness rejects an empty plan, a non-claude-project target, any operation outside /workspace/project/.claude, or any dry run that creates the target directory. install performs the isolated apply twice, checks its managed install state, lists the installed target, and runs doctor.

Start, Open, Reconnect, and Clean Up a Named Session

Start a detached container without --rm so leaving a terminal does not remove the session:

docker compose -p ecc-plugin-session \
  -f docker/plugin-setup/compose.yaml \
  run --detach --name ecc-plugin-shell real-cli shell

The container copies the read-only fixture to the stable private directory /workspace/project. Confirm it is running, then emit the Docker side of the terminal-opener v1 data contract:

docker inspect --format '{{.State.Running}}' ecc-plugin-shell
node docker/plugin-setup/interactive-plan.js \
  --container ecc-plugin-shell \
  --workdir /workspace/project \
  --json \
  -- bash

The JSON result has exactly an executable and argv boundary (plus contractVersion: 1): the executable is docker, and argv begins with exec, -it, and -w. Pass that data to the separate terminal-opener skill when it is installed. This Docker harness deliberately does not import a terminal adapter, interpolate a shell command, or manage a host GUI process. Until then, open the same PTY in the current host terminal directly:

docker exec -it -w /workspace/project ecc-plugin-shell bash

Exit the shell without stopping the detached container. Reconnect with the same docker exec -it command. When finished, remove the exact named container and its Compose project resources:

docker rm --force ecc-plugin-shell
docker compose -p ecc-plugin-session \
  -f docker/plugin-setup/compose.yaml \
  down --remove-orphans

Host credentials are absent by default and credential directories are never mounted. The default service also has no network access. When an authenticated provider session genuinely needs a network, build real-cli first and then opt in visibly with docker compose --profile networked run real-cli-networked shell. Prefer authenticating inside that disposable session. If a CI run must inherit a host environment credential, make that opt-in at invocation with an explicit Compose --env NAME flag, understand that the value is inspectable and can be exfiltrated for the container lifetime, and remove the exact named container immediately after.

Run the same focused suite natively on the host:

npm run test:plugin-setup-platform

Inspect the produced identity and environment before trusting the image:

docker image inspect ecc-plugin-setup:debian ecc-plugin-setup:ubuntu

Clean each named test project without deleting unrelated volumes or images:

docker compose -p ecc-plugin-debian-test \
  -f docker/plugin-setup/compose.yaml down --remove-orphans
docker compose -p ecc-plugin-ubuntu-test \
  -f docker/plugin-setup/compose.yaml down --remove-orphans

.dockerignore

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

Debugging

Common Commands

# 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

Debugging Network Issues

# 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

Anti-Patterns

# 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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

相关技能