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Skills/ailabs-393/ai-labs-claude-skills/docker-containerization
docker-containerization logo

docker-containerization

ailabs-393/ai-labs-claude-skills
824 installs409 stars
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Installation

npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill docker-containerization

Summary

This skill should be used when containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc.

SKILL.md

Docker Containerization Skill

Overview

Generate production-ready Docker configurations for modern web applications, particularly Next.js and Node.js projects. This skill provides Dockerfiles, docker-compose setups, bash scripts for container management, and comprehensive deployment guides for various orchestration platforms.

Core Capabilities

1. Dockerfile Generation

Create optimized Dockerfiles for different environments:

Production (assets/Dockerfile.production):

  • Multi-stage build reducing image size by 85%
  • Alpine Linux base (~180MB final image)
  • Non-root user execution for security
  • Health checks and resource limits

Development (assets/Dockerfile.development):

  • Hot reload support
  • All dev dependencies included
  • Volume mounts for live code updates

Nginx Static (assets/Dockerfile.nginx):

  • Static export optimization
  • Nginx reverse proxy included
  • Smallest possible footprint

2. Docker Compose Configuration

Multi-container orchestration with assets/docker-compose.yml:

  • Development and production services
  • Network and volume management
  • Health checks and logging
  • Restart policies

3. Bash Scripts for Container Management

docker-build.sh - Build images with comprehensive options:

./docker-build.sh -e prod -t v1.0.0
./docker-build.sh -n my-app --no-cache --platform linux/amd64

docker-run.sh - Run containers with full configuration:

./docker-run.sh -i my-app -t v1.0.0 -d
./docker-run.sh -p 8080:3000 --env-file .env.production

docker-push.sh - Push to registries (Docker Hub, ECR, GCR, ACR):

./docker-push.sh -n my-app -t v1.0.0 --repo username/my-app
./docker-push.sh -r gcr.io/project --repo my-app --also-tag stable

docker-cleanup.sh - Free disk space:

./docker-cleanup.sh --all --dry-run  # Preview cleanup
./docker-cleanup.sh --containers --images  # Clean specific resources

4. Configuration Files

  • .dockerignore: Excludes unnecessary files (node_modules, .git, logs)
  • nginx.conf: Production-ready Nginx configuration with compression, caching, security headers

5. Reference Documentation

docker-best-practices.md covers:

  • Multi-stage builds explained
  • Image optimization techniques (50-85% size reduction)
  • Security best practices (non-root users, vulnerability scanning)
  • Performance optimization
  • Health checks and logging
  • Troubleshooting guide

container-orchestration.md covers deployment to:

  • Docker Compose (local development)
  • Kubernetes (enterprise scale with auto-scaling)
  • Amazon ECS (AWS-native orchestration)
  • Google Cloud Run (serverless containers)
  • Azure Container Instances
  • Digital Ocean App Platform

Includes configuration examples, commands, auto-scaling setup, and monitoring.

Workflow Decision Tree

1. What environment?

  • Development → Dockerfile.development (hot reload, all dependencies)
  • Production → Dockerfile.production (minimal, secure, optimized)
  • Static Export → Dockerfile.nginx (smallest footprint)

2. Single or Multi-container?

  • Single → Generate Dockerfile only
  • Multi → Generate docker-compose.yml (app + database, microservices)

3. Which registry?

  • Docker Hub → docker.io/username/image
  • AWS ECR → 123456789012.dkr.ecr.region.amazonaws.com/image
  • Google GCR → gcr.io/project-id/image
  • Azure ACR → registry.azurecr.io/image

4. Deployment platform?

  • Kubernetes → See references/container-orchestration.md K8s section
  • ECS → See ECS task definition examples
  • Cloud Run → See deployment commands
  • Docker Compose → Use provided compose file

5. Optimizations needed?

  • Image size → Multi-stage builds, Alpine base
  • Build speed → Layer caching, BuildKit
  • Security → Non-root user, vulnerability scanning
  • Performance → Resource limits, health checks

Usage Examples

Example 1: Containerize Next.js App for Production

User: "Containerize my Next.js app for production"

Steps:

  1. Copy assets/Dockerfile.production to project root as Dockerfile
  2. Copy assets/.dockerignore to project root
  3. Build: ./docker-build.sh -e prod -n my-app -t v1.0.0
  4. Test: ./docker-run.sh -i my-app -t v1.0.0 -p 3000:3000 -d
  5. Push: ./docker-push.sh -n my-app -t v1.0.0 --repo username/my-app

Example 2: Development with Docker Compose

User: "Set up Docker Compose for local development"

Steps:

  1. Copy assets/Dockerfile.development and assets/docker-compose.yml to project
  2. Customize services in docker-compose.yml
  3. Start: docker-compose up -d
  4. Logs: docker-compose logs -f app-dev

Example 3: Deploy to Kubernetes

User: "Deploy my containerized app to Kubernetes"

Steps:

  1. Build and push image to registry
  2. Review references/container-orchestration.md Kubernetes section
  3. Create K8s manifests (deployment, service, ingress)
  4. Apply: kubectl apply -f deployment.yaml
  5. Verify: kubectl get pods && kubectl logs -f deployment/app

Example 4: Deploy to AWS ECS

User: "Deploy to AWS ECS Fargate"

Steps:

  1. Build and push to ECR
  2. Review references/container-orchestration.md ECS section
  3. Create task definition JSON
  4. Register: aws ecs register-task-definition --cli-input-json file://task-def.json
  5. Create service: aws ecs create-service --cluster my-cluster --service-name app --desired-count 3

Best Practices

Security

✅ Use multi-stage builds for production ✅ Run as non-root user ✅ Use specific image tags (not latest) ✅ Scan for vulnerabilities ✅ Never hardcode secrets ✅ Implement health checks

Performance

✅ Optimize layer caching order ✅ Use Alpine images (~85% smaller) ✅ Enable BuildKit for parallel builds ✅ Set resource limits ✅ Use compression

Maintainability

✅ Add comments for complex steps ✅ Use build arguments for flexibility ✅ Keep Dockerfiles DRY ✅ Version control all configs ✅ Document environment variables

Troubleshooting

Image too large (>500MB) → Use multi-stage builds, Alpine base, comprehensive .dockerignore

Build is slow → Optimize layer caching, use BuildKit, review dependencies

Container exits immediately → Check logs: docker logs container-name → Verify CMD/ENTRYPOINT, check port conflicts

Changes not reflecting → Rebuild without cache, check .dockerignore, verify volume mounts

Quick Reference

# Build
./docker-build.sh -e prod -t latest

# Run
./docker-run.sh -i app -t latest -d

# Logs
docker logs -f app

# Execute
docker exec -it app sh

# Cleanup
./docker-cleanup.sh --all --dry-run  # Preview
./docker-cleanup.sh --all            # Execute

Integration with CI/CD

GitHub Actions

- run: |
    chmod +x docker-build.sh docker-push.sh
    ./docker-build.sh -e prod -t ${{ github.sha }}
    ./docker-push.sh -n app -t ${{ github.sha }} --repo username/app

GitLab CI

build:
  script:
    - chmod +x docker-build.sh
    - ./docker-build.sh -e prod -t $CI_COMMIT_SHA

Resources

Scripts (scripts/)

Production-ready bash scripts with comprehensive features:

  • docker-build.sh - Build images (400+ lines, colorized output)
  • docker-run.sh - Run containers (400+ lines, auto conflict resolution)
  • docker-push.sh - Push to registries (multi-registry support)
  • docker-cleanup.sh - Clean resources (dry-run mode, selective cleanup)

References (references/)

Detailed documentation loaded as needed:

  • docker-best-practices.md - Comprehensive Docker best practices (~500 lines)
  • container-orchestration.md - Deployment guides for 6+ platforms (~600 lines)

Assets (assets/)

Ready-to-use templates:

  • Dockerfile.production - Multi-stage production Dockerfile
  • Dockerfile.development - Development Dockerfile
  • Dockerfile.nginx - Static export with Nginx
  • docker-compose.yml - Multi-container orchestration
  • .dockerignore - Optimized exclusion rules
  • nginx.conf - Production Nginx configuration

Score

0–100
63/ 100

Grade

C

Popularity15/30

824 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Docker Containerization skill score badge previewScore badge

Markdown

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HTML

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Docker Containerization FAQ

How do I install the Docker Containerization skill?

Run “npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill docker-containerization” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Docker Containerization skill do?

This skill should be used when containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Docker Containerization skill free?

Yes. Docker Containerization is a free, open-source skill published from ailabs-393/ai-labs-claude-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Docker Containerization work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Docker Containerization works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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