mcpbeat Sign in

Docker Compose Agent Skill

Define and run multi-container Docker applications using Docker Compose. Create compose files, manage service dependencies, configure networks and volumes, and orchestrate local development environments. Use when setting up multi-service applications or development environments.

2k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
511
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill docker-compose

What comes with it

1 818 bytes besides the instruction
references/compose-patterns.md

The instruction itself

37 sections, as written by the author

Docker Compose

Orchestrate multi-container applications with declarative YAML configuration.

When to Use This Skill

Use this skill when:

  • Running multi-container applications locally
  • Setting up development environments
  • Defining service dependencies and networking
  • Managing application stacks with multiple services
  • Creating reproducible development setups

Prerequisites

  • Docker Engine with Compose plugin (v2)
  • Basic Docker knowledge
  • YAML syntax understanding

Basic Configuration

Simple Application Stack

# docker-compose.yml
version: '3.8'

services:
  web:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=development
      - DATABASE_URL=postgres://postgres:secret@db:5432/myapp
    depends_on:
      - db
      - redis

  db:
    image: postgres:15-alpine
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: secret
      POSTGRES_DB: myapp
    volumes:
      - postgres-data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"

volumes:
  postgres-data:

Service Configuration

Build Options

services:
  app:
    build:
      context: ./app
      dockerfile: Dockerfile.dev
      args:
        NODE_VERSION: "20"
      target: development
      cache_from:
        - myapp:cache
    image: myapp:dev

Environment Variables

services:
  app:
    environment:
      - NODE_ENV=production
      - API_KEY=${API_KEY}  # From shell or .env file
    env_file:
      - .env
      - .env.local

Port Mapping

services:
  web:
    ports:
      - "3000:3000"           # HOST:CONTAINER
      - "127.0.0.1:9229:9229" # Bind to localhost only
      - "8080-8090:8080-8090" # Port range
    expose:
      - "3000"                # Internal only (no host binding)

Volume Mounts

services:
  app:
    volumes:
      # Named volume
      - app-data:/app/data
      # Bind mount
      - ./src:/app/src
      # Read-only bind mount
      - ./config:/app/config:ro
      # Anonymous volume (for node_modules)
      - /app/node_modules

volumes:
  app-data:
    driver: local

Dependencies

services:
  web:
    depends_on:
      db:
        condition: service_healthy
      redis:
        condition: service_started

  db:
    image: postgres:15
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U postgres"]
      interval: 10s
      timeout: 5s
      retries: 5

Networking

Custom Networks

services:
  frontend:
    networks:
      - frontend-net

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

  db:
    networks:
      - backend-net

networks:
  frontend-net:
    driver: bridge
  backend-net:
    driver: bridge
    internal: true  # No external access

Network Aliases

services:
  db:
    networks:
      backend:
        aliases:
          - database
          - postgres

networks:
  backend:

Resource Limits

services:
  app:
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 1G
        reservations:
          cpus: '0.5'
          memory: 256M

Multiple Compose Files

Override Files

# docker-compose.yml (base)
services:
  web:
    image: myapp:latest
    ports:
      - "3000:3000"

# docker-compose.override.yml (development - auto-loaded)
services:
  web:
    build: .
    volumes:
      - ./src:/app/src
    environment:
      - DEBUG=true

# docker-compose.prod.yml (production)
services:
  web:
    deploy:
      replicas: 3
    environment:
      - DEBUG=false

Using Multiple Files

# Development (uses override automatically)
docker compose up

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

# Merge and view final config
docker compose -f docker-compose.yml -f docker-compose.prod.yml config

Profiles

services:
  web:
    image: myapp

  db:
    image: postgres:15

  debug:
    image: busybox
    profiles:
      - debug

  monitoring:
    image: prometheus
    profiles:
      - monitoring
# Run without profiles (web, db only)
docker compose up

# Run with debug profile
docker compose --profile debug up

# Run with multiple profiles
docker compose --profile debug --profile monitoring up

Commands

Lifecycle

# Start services
docker compose up -d

# Start specific service
docker compose up -d web

# Stop services
docker compose stop

# Stop and remove containers
docker compose down

# Stop and remove everything including volumes
docker compose down -v --rmi all

# Restart services
docker compose restart web

Building

# Build images
docker compose build

# Build without cache
docker compose build --no-cache

# Build and start
docker compose up --build

# Pull latest images
docker compose pull

Monitoring

# View logs
docker compose logs -f

# View specific service logs
docker compose logs -f web

# View running services
docker compose ps

# View resource usage
docker compose top

Execution

# Run command in new container
docker compose run --rm web npm test

# Execute in running container
docker compose exec web /bin/sh

# Scale service
docker compose up -d --scale worker=3

Development Workflow

Watch Mode (Compose v2.22+)

services:
  web:
    build: .
    develop:
      watch:
        - action: sync
          path: ./src
          target: /app/src
        - action: rebuild
          path: ./package.json
docker compose watch

Hot Reload Setup

services:
  web:
    build:
      context: .
      target: development
    volumes:
      - ./src:/app/src
      - /app/node_modules
    environment:
      - CHOKIDAR_USEPOLLING=true
    command: npm run dev

Common Patterns

Database Initialization

services:
  db:
    image: postgres:15
    volumes:
      - postgres-data:/var/lib/postgresql/data
      - ./init-scripts:/docker-entrypoint-initdb.d:ro
    environment:
      POSTGRES_DB: myapp

Reverse Proxy

services:
  proxy:
    image: traefik:v3.0
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - ./traefik.yml:/etc/traefik/traefik.yml:ro

  web:
    labels:
      - "traefik.enable=true"
      - "traefik.http.routers.web.rule=Host(`app.localhost`)"

Common Issues

Issue: Container Cannot Resolve Service Name

Problem: Service can't connect to another service by name

Solution: Ensure services are on the same network, check depends_on

Issue: Volume Permissions

Problem: Container can't write to mounted volume

Solution: Match container user UID with host, or use named volumes

Issue: Port Already in Use

Problem: Error binding to port

Solution: Change host port or stop conflicting service

Issue: Changes Not Reflected

Problem: Code changes don't appear in container

Solution: Check volume mounts, rebuild if Dockerfile changed

Best Practices

  • Use named volumes for persistent data
  • Define healthchecks for database dependencies
  • Use profiles to separate optional services
  • Keep secrets in .env files (not committed)
  • Use override files for environment-specific config
  • Pin image versions for reproducibility
  • Use networks to isolate service groups
  • Leverage watch mode for development
  • docker-management - Docker fundamentals
  • kubernetes-ops - Production orchestration
  • reverse-proxy - Production routing

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

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.

13k tokens
Capacity
by microsoft
vendor ×3

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.

6k tokens scripts
Customize
by microsoft
vendor ×3

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).

8k tokens
Deploy Model
by microsoft
vendor ×3

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).

26k tokens scripts
Preset
by microsoft
vendor ×3

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).

9k tokens
Lamindb
by christophacham
×3

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.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

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.

17k tokens

How to use it

Copy the folder

Take bagelhole/docker-compose from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.