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Docker Containerization

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ailabs-393
docker-containerization

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.

Overview

Publisherailabs-393
Repositoryai-labs-claude-skills
Skill namedocker-containerization
Stars
447
Forks
115
Bundled files
13
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ailabs-393 on GitHub. Read the source before you install it.

Installation

Install the Docker Containerization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ailabs-393/ai-labs-claude-skills.git /tmp/ai-labs-claude-skills
mkdir -p .claude/skills
cp -r /tmp/ai-labs-claude-skills/packages/skills/docker-containerization .claude/skills/docker-containerization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docker Containerization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Docker Containerization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Docker Containerization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
./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:

bash
./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):

bash
./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:

bash
./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?

  • DevelopmentDockerfile.development (hot reload, all dependencies)
  • ProductionDockerfile.production (minimal, secure, optimized)
  • Static ExportDockerfile.nginx (smallest footprint)

2. Single or Multi-container?

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

3. Which registry?

  • Docker Hubdocker.io/username/image
  • AWS ECR123456789012.dkr.ecr.region.amazonaws.com/image
  • Google GCRgcr.io/project-id/image
  • Azure ACRregistry.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

bash
# 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

yaml
- 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

yaml
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Docker Containerization AI 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.

Why use Docker Containerization on TypingMind?

Because you install it once and use it with any model. Docker Containerization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Docker Containerization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ailabs-393/ai-labs-claude-skills/tree/main/packages/skills/docker-containerization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Docker Containerization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Docker Containerization?

As many as you like. As long as a model supports skills, you can use Docker Containerization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Docker Containerization AI skill free?

Yes. It is published on GitHub by ailabs-393 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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