Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.
npx skills add https://github.com/ancoleman/ai-design-components --skill deploying-applications
Production deployment patterns from Kubernetes to serverless and edge functions. Bridges the gap from application assembly to production infrastructure.
This skill provides clear guidance for:
Use this skill when:
assembling-components for complete deployment flowWORKLOAD TYPE?
├── COMPLEX MICROSERVICES (10+ services)
│ └─ Kubernetes + ArgoCD/Flux (GitOps)
│ ├─ Helm 4.0 for packaging
│ ├─ Service mesh: Linkerd (5-10% overhead) or Istio (25-35%)
│ └─ See references/kubernetes-patterns.md
├── VARIABLE TRAFFIC / COST-SENSITIVE
│ └─ Serverless
│ ├─ Database: Neon/Turso (scale-to-zero)
│ ├─ Compute: Vercel, AWS Lambda, Cloud Functions
│ ├─ Edge: Cloudflare Workers (<5ms cold start)
│ └─ See references/serverless-dbs.md and references/edge-functions.md
├── CONSISTENT LOAD / PREDICTABLE TRAFFIC
│ └─ Containers (ECS, Cloud Run, Fly.io)
│ ├─ ECS Fargate: AWS-native, serverless containers
│ ├─ Cloud Run: GCP, scale-to-zero containers
│ └─ Fly.io: Global edge, multi-region
├── GLOBAL LOW-LATENCY (<50ms)
│ └─ Edge Functions + Edge Database
│ ├─ Cloudflare Workers + D1 (SQLite)
│ ├─ Deno Deploy + Turso (libSQL)
│ └─ See references/edge-functions.md
└── RAPID PROTOTYPING / STARTUP MVP
└─ Managed Platform as a Service
├─ Vercel (Next.js, zero-config)
├─ Railway (any framework)
└─ Render (auto-deploy from Git)
IaC CHOICE?
├─ TypeScript-first → Pulumi (Apache 2.0, multi-cloud)
├─ HCL-based → OpenTofu (CNCF, Terraform-compatible)
└─ Serverless TypeScript → SST v3 (built on Pulumi)
Define infrastructure using code instead of manual configuration.
Primary: Pulumi (TypeScript)
/pulumi/docs (Trust: 94.6/100, 9,525 snippets)Alternative: OpenTofu (HCL)
Serverless: SST v3 (TypeScript)
Declarative infrastructure with Git as source of truth.
ArgoCD (Recommended for platform teams):
Flux (Recommended for DevOps automation):
Optional layer for microservices communication, security, and observability.
When to Use Service Mesh:
When NOT to Use:
Linkerd (Performance-focused):
Istio (Feature-rich):
See references/kubernetes-patterns.md for service mesh patterns.
# Install Vercel CLI
npm i -g vercel
# Link project
vercel link
# Deploy to production
vercel --prod
See examples/nextjs-vercel/ for complete example.
See examples/k8s-argocd/ for complete GitOps setup.
import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";
// Create Lambda function
const lambda = new aws.lambda.Function("api", {
runtime: "nodejs20.x",
handler: "index.handler",
role: role.arn,
code: new pulumi.asset.FileArchive("./dist"),
});
export const apiUrl = lambda.invokeArn;
See examples/pulumi-aws/ and references/pulumi-guide.md for patterns.
import { Hono } from 'hono'
const app = new Hono()
app.get('/api/hello', (c) => {
return c.json({ message: 'Hello from edge!' })
})
export default app
Deploy with Wrangler:
wrangler deploy
See examples/cloudflare-workers-hono/ and references/edge-functions.md.
After building an application with assembling-components, this skill provides deployment patterns:
Frontend (Next.js/Vite) → Deployment:
Backend (FastAPI/Axum) → Deployment:
See references/pulumi-guide.md for integration examples.
Scripts in scripts/ are executed without loading into context (token-free).
Generate Kubernetes Manifests:
python scripts/generate_k8s_manifests.py --app-name my-app --replicas 3
Validate Deployment Configuration:
python scripts/validate_deployment.py --config deployment.yaml
See script files for full usage documentation.
Complete, runnable examples in examples/:
Each example includes:
Primary: Pulumi
/pulumi/docs (Trust: 94.6, 9,525 snippets)Alternative: OpenTofu
Serverless: SST v3
Neon PostgreSQL:
Turso SQLite:
PlanetScale MySQL:
See references/serverless-dbs.md for comparison and integration.
Cloudflare Workers:
Deno Deploy:
Hono Framework:
See references/edge-functions.md for patterns.
Kubernetes pod fails to start:
kubectl logs <pod-name>kubectl describe pod <pod-name>Serverless cold starts too slow:
GitOps sync errors (ArgoCD/Flux):
kubectl apply --dry-run)High service mesh overhead:
Database connection pool exhaustion:
See references/ files for detailed troubleshooting guides.
# Install OpenTofu
brew install opentofu
# Migrate state
terraform state pull > terraform.tfstate.backup
tofu init -migrate-state
tofu plan
tofu apply
See references/opentofu-guide.md for complete migration.
After deploying applications:
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.
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 ancoleman/deploying-applications 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 npm, brew.
Without those the skill loads but fails at the first command.