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.
npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill docker-containerization
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.
Create optimized Dockerfiles for different environments:
Production (assets/Dockerfile.production):
Development (assets/Dockerfile.development):
Nginx Static (assets/Dockerfile.nginx):
Multi-container orchestration with assets/docker-compose.yml:
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
.dockerignore: Excludes unnecessary files (node_modules, .git, logs)nginx.conf: Production-ready Nginx configuration with compression, caching, security headersdocker-best-practices.md covers:
container-orchestration.md covers deployment to:
Includes configuration examples, commands, auto-scaling setup, and monitoring.
Dockerfile.development (hot reload, all dependencies)Dockerfile.production (minimal, secure, optimized)Dockerfile.nginx (smallest footprint)docker-compose.yml (app + database, microservices)docker.io/username/image123456789012.dkr.ecr.region.amazonaws.com/imagegcr.io/project-id/imageregistry.azurecr.io/imagereferences/container-orchestration.md K8s sectionUser: "Containerize my Next.js app for production"
Steps:
assets/Dockerfile.production to project root as Dockerfileassets/.dockerignore to project root./docker-build.sh -e prod -n my-app -t v1.0.0./docker-run.sh -i my-app -t v1.0.0 -p 3000:3000 -d./docker-push.sh -n my-app -t v1.0.0 --repo username/my-appUser: "Set up Docker Compose for local development"
Steps:
assets/Dockerfile.development and assets/docker-compose.yml to projectdocker-compose up -ddocker-compose logs -f app-devUser: "Deploy my containerized app to Kubernetes"
Steps:
references/container-orchestration.md Kubernetes sectionkubectl apply -f deployment.yamlkubectl get pods && kubectl logs -f deployment/appUser: "Deploy to AWS ECS Fargate"
Steps:
references/container-orchestration.md ECS sectionaws ecs register-task-definition --cli-input-json file://task-def.jsonaws ecs create-service --cluster my-cluster --service-name app --desired-count 3✅ 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
✅ Optimize layer caching order
✅ Use Alpine images (~85% smaller)
✅ Enable BuildKit for parallel builds
✅ Set resource limits
✅ Use compression
✅ Add comments for complex steps
✅ Use build arguments for flexibility
✅ Keep Dockerfiles DRY
✅ Version control all configs
✅ Document environment variables
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
# 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
- 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
build:
script:
- chmod +x docker-build.sh
- ./docker-build.sh -e prod -t $CI_COMMIT_SHA
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/)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/)Ready-to-use templates:
Dockerfile.production - Multi-stage production DockerfileDockerfile.development - Development DockerfileDockerfile.nginx - Static export with Nginxdocker-compose.yml - Multi-container orchestration.dockerignore - Optimized exclusion rulesnginx.conf - Production Nginx configurationAssess 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.
Take ailabs-393/docker-containerization from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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same name cannot sit side by side — one of them will be ignored.