Docker, Kubernetes, and AWS ECS/Fargate patterns. Triggers on: Dockerfile, docker-compose, kubernetes, k8s, helm, pod, deployment, service, ingress, container, image, ecs, fargate, task definition, ecs service, awsvpc, FARGATE_SPOT, ALB, ecs vs kubernetes.
npx skills add https://github.com/aiskillstore/marketplace --skill container-orchestration
> Facts verified as of 2026-07.
Docker and Kubernetes patterns for containerized applications.
# Use specific version, not :latest
FROM python:3.11-slim AS builder
# Set working directory
WORKDIR /app
# Copy dependency files first (better caching)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy application code
COPY src/ ./src/
# Production stage (multi-stage build)
FROM python:3.11-slim
WORKDIR /app
# Create non-root user
RUN useradd --create-home appuser
USER appuser
# Copy from builder
COPY --from=builder /app /app
# Set environment
ENV PYTHONUNBUFFERED=1
# Health check
HEALTHCHECK --interval=30s --timeout=3s \
CMD curl -f http://localhost:8000/health || exit 1
EXPOSE 8000
CMD ["python", "-m", "uvicorn", "src.main:app", "--host", "0.0.0.0"]
DO:
- Use specific base image versions
- Use multi-stage builds
- Run as non-root user
- Order commands by change frequency
- Use .dockerignore
- Add health checks
DON'T:
- Use :latest tag
- Run as root
- Copy unnecessary files
- Store secrets in image
- Install dev dependencies in production
# docker-compose.yml
version: "3.9"
services:
app:
build:
context: .
dockerfile: Dockerfile
ports:
- "8000:8000"
environment:
- DATABASE_URL=postgres://user:pass@db:5432/app
depends_on:
db:
condition: service_healthy
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 3
db:
image: postgres:15-alpine
volumes:
- postgres_data:/var/lib/postgresql/data
environment:
POSTGRES_USER: user
POSTGRES_PASSWORD: pass
POSTGRES_DB: app
healthcheck:
test: ["CMD-SHELL", "pg_isready -U user -d app"]
interval: 10s
timeout: 5s
retries: 5
volumes:
postgres_data:
apiVersion: apps/v1
kind: Deployment
metadata:
name: app
labels:
app: myapp
spec:
replicas: 3
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: app
image: myapp:1.0.0
ports:
- containerPort: 8000
resources:
requests:
memory: "128Mi"
cpu: "100m"
limits:
memory: "256Mi"
cpu: "500m"
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 10
periodSeconds: 30
readinessProbe:
httpGet:
path: /ready
port: 8000
initialDelaySeconds: 5
periodSeconds: 10
env:
- name: DATABASE_URL
valueFrom:
secretKeyRef:
name: app-secrets
key: database-url
apiVersion: v1
kind: Service
metadata:
name: app-service
spec:
selector:
app: myapp
ports:
- port: 80
targetPort: 8000
type: ClusterIP
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: app-ingress
annotations:
nginx.ingress.kubernetes.io/rewrite-target: /
spec:
ingressClassName: nginx
rules:
- host: app.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: app-service
port:
number: 80
| Command | Description |
|---------|-------------|
| kubectl get pods | List pods |
| kubectl logs <pod> | View logs |
| kubectl exec -it <pod> -- sh | Shell into pod |
| kubectl apply -f manifest.yaml | Apply config |
| kubectl rollout restart deployment/app | Restart deployment |
| kubectl rollout status deployment/app | Check rollout |
| kubectl describe pod <pod> | Debug pod |
| kubectl port-forward svc/app 8080:80 | Local port forward |
./references/dockerfile-patterns.md - Advanced Dockerfile techniques./references/k8s-manifests.md - Full Kubernetes manifest examples./references/helm-patterns.md - Helm chart structure and values./references/ecs-fargate.md - Amazon ECS on AWS Fargate (task definitions, services, awsvpc networking, IAM roles, secrets, scaling, ALB/NLB, ECS vs Kubernetes)./scripts/build-push.sh - Build and push Docker image./assets/Dockerfile.template - Production Dockerfile template./assets/docker-compose.template.yml - Compose starter templateAssess 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 aiskillstore/container-orchestration from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.
The instructions reference pip.
Without those the skill loads but fails at the first command.