Docker and Docker Compose patterns for local development, container security, networking, volume strategies, and multi-service orchestration.
npx skills add https://github.com/loulanyue/awesome-claude-notes --skill docker-patterns
Docker and Docker Compose best practices for containerized development.
# 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:
# 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"]
# 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
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
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:
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
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
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
# 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
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
# 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
node_modules
.git
.env
.env.*
dist
coverage
*.log
.next
.cache
docker-compose*.yml
Dockerfile*
README.md
tests/
# 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
# 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
# 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
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
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