Some_claude_skills docker-containerization
Write production-grade Dockerfiles, docker-compose configurations, multi-stage builds, and container optimization. Activate on "Dockerfile", "docker", "docker-compose", "container", "multi-stage
git clone https://github.com/curiositech/some_claude_skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/curiositech/some_claude_skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/docker-containerization" ~/.claude/skills/erichowens-some-claude-skills-docker-containerization && rm -rf "$T"
.claude/skills/docker-containerization/SKILL.mdDocker Containerization
Write production-grade Dockerfiles with multi-stage builds, security hardening, and size optimization. Covers docker-compose for local development, image layer caching, health checks, and the patterns that separate a 2GB image from a 50MB one.
When to Use
Use for:
- Writing Dockerfiles from scratch or improving existing ones
- Multi-stage builds for compiled languages (Go, Rust, TypeScript)
- Docker Compose for local development environments
- Image size optimization (choosing base images, layer caching)
- Docker security scanning and hardening
- Development vs production Dockerfile patterns
- Debugging container build failures
- .dockerignore optimization
NOT for:
- Kubernetes deployment/orchestration (different domain)
- Cloud-specific container services (ECS, Cloud Run, App Runner)
- CI/CD pipeline configuration (use
)github-actions-pipeline-builder - Container networking beyond docker-compose
- Docker Swarm
Dockerfile Decision Tree
flowchart TD Start[What are you building?] --> Lang{Language/runtime?} Lang -->|Node.js/TypeScript| Node[Node pattern] Lang -->|Python| Python[Python pattern] Lang -->|Go| Go[Go pattern] Lang -->|Rust| Rust[Rust pattern] Lang -->|Static site| Static[Static pattern] Node --> NQ{Need build step?} NQ -->|Yes, TypeScript/bundler| MultiNode[Multi-stage: build + runtime] NQ -->|No, plain JS| SingleNode[Single stage with slim base] Python --> PQ{Package manager?} PQ -->|pip| PipPattern[pip + venv pattern] PQ -->|uv| UvPattern[uv pattern — fastest] PQ -->|poetry| PoetryPattern[poetry export pattern] Go --> GoMulti[Multi-stage: build + scratch/distroless] Rust --> RustMulti[Multi-stage: build + debian-slim] Static --> StaticMulti[Multi-stage: build + nginx/caddy]
Production Patterns by Language
Node.js / TypeScript (Multi-Stage)
# Stage 1: Dependencies FROM node:22-alpine AS deps WORKDIR /app COPY package.json package-lock.json ./ RUN npm ci --only=production # Stage 2: Build (TypeScript/bundler) FROM node:22-alpine AS build WORKDIR /app COPY package.json package-lock.json ./ RUN npm ci COPY . . RUN npm run build # Stage 3: Production FROM node:22-alpine AS production WORKDIR /app ENV NODE_ENV=production # Security: non-root user RUN addgroup -g 1001 -S nodejs && \ adduser -S nextjs -u 1001 COPY --from=deps /app/node_modules ./node_modules COPY --from=build /app/dist ./dist COPY package.json ./ USER nextjs EXPOSE 3000 HEALTHCHECK --interval=30s --timeout=3s --start-period=5s \ CMD wget -qO- http://localhost:3000/health || exit 1 CMD ["node", "dist/index.js"]
Python (uv — Fastest)
FROM python:3.12-slim AS base # Install uv COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ WORKDIR /app # Install dependencies (cached layer) COPY pyproject.toml uv.lock ./ RUN uv sync --frozen --no-dev --no-editable # Copy application code COPY . . # Non-root user RUN useradd -r -s /bin/false appuser USER appuser EXPOSE 8000 HEALTHCHECK --interval=30s --timeout=3s \ CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
Go (Multi-Stage → Distroless)
# Build stage FROM golang:1.22-alpine AS build WORKDIR /app COPY go.mod go.sum ./ RUN go mod download COPY . . RUN CGO_ENABLED=0 GOOS=linux go build -ldflags="-s -w" -o /server ./cmd/server # Production: distroless (no shell, no package manager, minimal attack surface) FROM gcr.io/distroless/static-debian12 COPY --from=build /server /server EXPOSE 8080 USER nonroot:nonroot ENTRYPOINT ["/server"]
Layer Caching Strategy
flowchart TD subgraph "Slow to change (cache hit)" A[Base image] --> B[System packages] B --> C[Language runtime deps] end subgraph "Medium change frequency" C --> D[Application dependencies] end subgraph "Fast changing (cache miss OK)" D --> E[Application code] E --> F[Build step] end
Rule: Order Dockerfile instructions from least-frequently-changed to most-frequently-changed. Each instruction creates a layer. When a layer changes, all subsequent layers are rebuilt.
Anti-Pattern: COPY Before Dependencies
Novice:
COPY . . # ← Busts cache on ANY file change RUN npm install # ← Reinstalls everything every build
Expert:
COPY package.json package-lock.json ./ # ← Only busts on dependency changes RUN npm ci # ← Cached when deps unchanged COPY . . # ← Only app code changes trigger rebuild
Timeline: This has been best practice since Docker layer caching was introduced, but LLMs trained on older tutorials still generate the wrong order.
Docker Compose for Development
# docker-compose.yml services: app: build: context: . dockerfile: Dockerfile target: development # Use a dev-specific stage ports: - "${PORT:-3000}:3000" volumes: - .:/app # Hot reload via bind mount - /app/node_modules # Anonymous volume: don't override node_modules environment: - NODE_ENV=development - DATABASE_URL=postgresql://postgres:postgres@db:5432/myapp depends_on: db: condition: service_healthy develop: watch: # Docker Compose Watch (2024+) - action: sync path: ./src target: /app/src - action: rebuild path: package.json db: image: postgres:16-alpine volumes: - pgdata:/var/lib/postgresql/data environment: POSTGRES_PASSWORD: postgres POSTGRES_DB: myapp healthcheck: test: ["CMD-SHELL", "pg_isready -U postgres"] interval: 5s timeout: 5s retries: 5 ports: - "5432:5432" redis: image: redis:7-alpine ports: - "6379:6379" healthcheck: test: ["CMD", "redis-cli", "ping"] interval: 5s volumes: pgdata:
Anti-Pattern: No Health Checks
Novice: Relies on
depends_on alone — but that only waits for the container to START, not for the service to be READY.
Expert: Always add healthcheck to database/cache services and use condition: service_healthy in depends_on. A Postgres container that has started but hasn't finished WAL recovery will crash your app.
Image Size Optimization
| Base Image | Size | Use When |
|---|---|---|
| ~1.1 GB | Never in production |
| ~200 MB | Need apt packages |
| ~130 MB | Default choice |
| ~20 MB | Go/Rust compiled binaries |
| 0 MB | Fully static binaries |
| ~10-30 MB | Security-hardened alternatives |
Quick Wins
# 1. Use --no-cache for apk/apt RUN apk add --no-cache curl # 2. Combine RUN commands to reduce layers RUN apt-get update && \ apt-get install -y --no-install-recommends curl && \ rm -rf /var/lib/apt/lists/* # 3. Use .dockerignore aggressively # .dockerignore: node_modules .git *.md .env* dist coverage .next
Security Hardening
# 1. Non-root user (MANDATORY) RUN addgroup -g 1001 -S appgroup && \ adduser -S appuser -u 1001 -G appgroup USER appuser # 2. Read-only filesystem (in compose) # docker-compose.yml: # read_only: true # tmpfs: # - /tmp # 3. No new privileges # docker run --security-opt no-new-privileges ... # 4. Pin image digests for reproducibility FROM node:22-alpine@sha256:abc123... # 5. Scan for vulnerabilities # docker scout quickview myimage:latest # trivy image myimage:latest
Anti-Pattern: Running as Root
Novice: Skips the USER instruction. Everything runs as root. Expert: Running as root inside a container means a container escape gives the attacker root on the host. Always create and switch to a non-root user. Only use root for package installation in build stages. Detection:
docker inspect --format='{{.Config.User}}' image:tag — if empty, it's root.
Health Check Strategy by Service Type
Principle: Liveness, Not Readiness
Docker HEALTHCHECK answers one question: "Is this process alive and minimally functional?" It does NOT answer "Are all dependencies reachable?" — that's readiness (a Kubernetes concept). Conflating them causes cascading restarts: DB goes down → every API container "fails" health check → orchestrator restarts them all → thundering herd on DB recovery.
API Services
HEALTHCHECK --interval=30s --timeout=3s --start-period=10s --retries=3 \ CMD wget -qO- http://localhost:${PORT}/health || exit 1
The
/health endpoint should:
- Return 200 if the process can serve HTTP requests
- NOT check database connectivity (that's readiness)
- NOT run expensive queries or computations
- Respond in <100ms — it runs every 30 seconds
// Minimal /health endpoint app.get('/health', (req, res) => res.status(200).json({ status: 'ok' }));
If you need a richer health check for monitoring dashboards (DB status, queue depth, cache hit rate), expose it on
/health/detailed and do NOT wire it to Docker HEALTHCHECK.
Compose equivalent:
healthcheck: test: ["CMD", "wget", "-qO-", "http://localhost:3000/health"] interval: 30s timeout: 3s start_period: 10s retries: 3
Worker / Background Job Services
Workers don't serve HTTP. Use a heartbeat file pattern:
HEALTHCHECK --interval=30s --timeout=5s --start-period=15s --retries=3 \ CMD test $(find /tmp/worker-heartbeat -mmin -1 2>/dev/null | wc -l) -gt 0 || exit 1
The worker writes a timestamp file on each successful job loop iteration:
// Inside your worker loop await processJob(); fs.writeFileSync('/tmp/worker-heartbeat', Date.now().toString());
If the heartbeat file is older than 1 minute, the worker is stuck. Checks: process is alive, event loop is not blocked, jobs are being dequeued.
Static File Servers (nginx, Caddy)
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ CMD wget -qO- http://localhost:80/ || exit 1
Short start period — static servers boot fast. Just check it serves a page. No
/health endpoint needed.
Database Containers
Use the database's native client for health checks, not HTTP:
# PostgreSQL healthcheck: test: ["CMD-SHELL", "pg_isready -U postgres"] interval: 10s timeout: 5s start_period: 30s # DBs are slow to start — generous grace period retries: 5 # Redis healthcheck: test: ["CMD", "redis-cli", "ping"] interval: 10s timeout: 3s retries: 5 # MySQL healthcheck: test: ["CMD", "mysqladmin", "ping", "-h", "localhost"] interval: 10s timeout: 5s start_period: 30s retries: 5
Tuning Parameters
| Parameter | Guidance |
|---|---|
| 30s for apps, 10s for databases. Lower = more CPU overhead and log noise. |
| 3-5s. If your health check takes longer, it's too expensive. |
| How long until the first check. 5s for static, 10s for APIs, 30s for databases, 60s+ for JVM apps. |
| 3 for apps, 5 for databases. Too low = restarts on transient blips. |
References
— Consult for complex multi-stage builds: build caching with BuildKit, cross-compilation, monorepo Dockerfiles, Bun/Deno patternsreferences/multi-stage-patterns.md
— Consult for advanced docker-compose: profiles, extends, override files, networking, secrets management, GPU passthroughreferences/compose-patterns.md