Agent skill

Deploying Applications

by ancoleman in ancoleman/ai-design-components

Deployment patterns from Kubernetes to serverless and edge functions.

MITAuto-check passedDevOps & Cloud

Install Deploying Applications

skills CLI
$ npx skills add ancoleman/ai-design-components --skill deploying-applications -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ancoleman/ai-design-components deploying-applications --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deploying-applications .claude/skills/deploying-applications && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deploying-applications
GitHub stars
526
Token cost
~3.1k tokens
SKILL.md length
1,064 words
Files
20 (incl. scripts, references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

Deployment patterns from Kubernetes to serverless and edge functions.

  • Works in 4 steps: Create Helm chart → Push chart to Git repository → Create ArgoCD Application → …
  • Deploying applications
  • SKILL.md covers Purpose, When to Use This Skill, Deployment Strategy Decision… and Core Concepts, plus 8 more sections
  • Runs TypeScript scripts from its folder; calls tofu, kubectl and vercel

What it does

Deploying Applications is an agent skill from ancoleman/ai-design-components. 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.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 25 other files, including scripts and reference files (for example `examples/k8s-argocd/README.md`, `examples/k8s-argocd/argocd/application.yaml` and `examples/k8s-argocd/base/deployment.yaml`).

It sits in DevOps & Cloud, covering Infrastructure as code, Container orchestration and Serverless. It works with Kubernetes, Pulumi, Argo CD and Terraform. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Deploying applications
  • Setting up CI/CD
  • Managing infrastructure

Example prompts

  • “/deploying-applications”

Requirements

  • Python 3
  • Node.js
  • Docker

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Create Helm chart
  2. Push chart to Git repository
  3. Create ArgoCD Application
  4. ArgoCD syncs automatically

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (TypeScript, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • tofu
    • kubectl
    • vercel
    • python
    • npm
    • wrangler
    • brew
    • terraform

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • pulumi.com
    • opentofu.org
    • argo-cd.readthedocs.io
    • developers.cloudflare.com
    • neon.tech

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deploying Applications loads about 3.1k tokens when it runs, and up to ~35k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 1,064 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~35k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 1,064 words, ~3,069 tokens.

Download SKILL.mdSave it as .claude/skills/deploying-applications/SKILL.md (or your agent's skills folder). This skill also uses 19 other files; get the full folder from GitHub.
name
deploying-applications
description
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.

Deploying Applications

Production deployment patterns from Kubernetes to serverless and edge functions. Bridges the gap from application assembly to production infrastructure.

Purpose

This skill provides clear guidance for:

  • Selecting the right deployment strategy (Kubernetes, serverless, containers, edge)
  • Implementing Infrastructure as Code with Pulumi or OpenTofu
  • Setting up GitOps automation with ArgoCD or Flux
  • Choosing serverless databases (Neon, Turso, PlanetScale)
  • Deploying edge functions (Cloudflare Workers, Deno Deploy)

When to Use This Skill

Use this skill when:

  • Deploying applications to production infrastructure
  • Setting up CI/CD pipelines and GitOps workflows
  • Choosing between Kubernetes, serverless, or edge deployment
  • Implementing Infrastructure as Code (Pulumi, OpenTofu, SST)
  • Migrating from manual deployment to automated infrastructure
  • Integrating with assembling-components for complete deployment flow

Deployment Strategy Decision Tree

WORKLOAD 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)

Core Concepts

Infrastructure as Code (IaC)

Define infrastructure using code instead of manual configuration.

Primary: Pulumi (TypeScript)

  • Context7 ID: /pulumi/docs (Trust: 94.6/100, 9,525 snippets)
  • TypeScript-first (same language as React/Next.js)
  • Multi-cloud support (AWS, GCP, Azure, Cloudflare)
  • See references/pulumi-guide.md for patterns and examples

Alternative: OpenTofu (HCL)

  • CNCF project, Terraform-compatible
  • MPL-2.0 license (open governance)
  • Drop-in Terraform replacement
  • See references/opentofu-guide.md for migration

Serverless: SST v3 (TypeScript)

  • Built on Pulumi
  • Optimized for AWS Lambda, API Gateway
  • Live Lambda development
GitOps Deployment

Declarative infrastructure with Git as source of truth.

ArgoCD (Recommended for platform teams):

  • Rich web UI
  • Built-in RBAC and multi-tenancy
  • Self-healing deployments
  • See references/gitops-argocd.md

Flux (Recommended for DevOps automation):

  • Kubernetes-native
  • CLI-focused
  • Simpler architecture
  • See references/gitops-argocd.md
Service Mesh

Optional layer for microservices communication, security, and observability.

When to Use Service Mesh:

  • Multi-team microservices (security boundaries)
  • Zero-trust networking (mTLS required)
  • Advanced traffic management (canary, blue-green)

When NOT to Use:

  • Simple monolith or 2-3 services (overhead not justified)
  • Serverless architectures (incompatible)

Linkerd (Performance-focused):

  • 5-10% overhead
  • Rust-based
  • Simple, opinionated

Istio (Feature-rich):

  • 25-35% overhead
  • C++ (Envoy)
  • Advanced routing, observability

See references/kubernetes-patterns.md for service mesh patterns.

Quick Start Workflows

Workflow 1: Deploy Next.js to Vercel (Zero-Config)
bash
# Install Vercel CLI
npm i -g vercel

# Link project
vercel link

# Deploy to production
vercel --prod

See examples/nextjs-vercel/ for complete example.

Workflow 2: Deploy to Kubernetes with ArgoCD
  1. Create Helm chart
  2. Push chart to Git repository
  3. Create ArgoCD Application
  4. ArgoCD syncs automatically

See examples/k8s-argocd/ for complete GitOps setup.

Workflow 3: Deploy Serverless with Pulumi
typescript
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.

Workflow 4: Deploy Edge Function to Cloudflare Workers
typescript
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:

bash
wrangler deploy

See examples/cloudflare-workers-hono/ and references/edge-functions.md.

Integration with assembling-components

After building an application with assembling-components, this skill provides deployment patterns:

Frontend (Next.js/Vite) → Deployment:

  1. Review deployment decision tree
  2. Choose platform: Vercel (Next.js), Cloudflare Pages (static), or custom (Pulumi)
  3. Set up environment variables
  4. Deploy using chosen method

Backend (FastAPI/Axum) → Deployment:

  1. Containerize application (Dockerfile)
  2. Choose platform: ECS Fargate, Cloud Run, or Kubernetes
  3. Set up IaC (Pulumi or OpenTofu)
  4. Deploy with GitOps (ArgoCD/Flux) or CI/CD

See references/pulumi-guide.md for integration examples.

Reference Files

Kubernetes Deployment
  • references/kubernetes-patterns.md - Helm 4.0, service mesh, autoscaling
  • references/gitops-argocd.md - ArgoCD/Flux GitOps workflows
Serverless & Edge
  • references/serverless-dbs.md - Neon, Turso, PlanetScale (scale-to-zero)
  • references/edge-functions.md - Cloudflare Workers, Deno Deploy (<5ms cold starts)
Infrastructure as Code
  • references/pulumi-guide.md - Pulumi TypeScript patterns, component model
  • references/opentofu-guide.md - OpenTofu/Terraform migration

Utility Scripts

Scripts in scripts/ are executed without loading into context (token-free).

Generate Kubernetes Manifests:

bash
python scripts/generate_k8s_manifests.py --app-name my-app --replicas 3

Validate Deployment Configuration:

bash
python scripts/validate_deployment.py --config deployment.yaml

See script files for full usage documentation.

Examples

Complete, runnable examples in examples/:

  • pulumi-aws/ - ECS Fargate deployment with Pulumi
  • k8s-argocd/ - Kubernetes + ArgoCD GitOps
  • sst-serverless/ - SST v3 serverless TypeScript

Each example includes:

  • README.md with setup instructions
  • Complete source code
  • Environment variable configuration
  • Deployment commands

Library Recommendations

Infrastructure as Code (2025)

Primary: Pulumi

  • Context7: /pulumi/docs (Trust: 94.6, 9,525 snippets)
  • TypeScript-first, multi-cloud
  • Apache 2.0 license

Alternative: OpenTofu

  • CNCF project, MPL-2.0
  • Terraform-compatible
  • HCL syntax

Serverless: SST v3

  • Built on Pulumi
  • AWS Lambda optimized
  • TypeScript-native
Serverless Databases

Neon PostgreSQL:

  • Database branching (like Git)
  • Scale-to-zero compute
  • Full PostgreSQL compatibility

Turso SQLite:

  • Edge deployment (200+ locations)
  • Sub-millisecond reads
  • libSQL (SQLite fork)

PlanetScale MySQL:

  • Non-blocking schema changes
  • Vitess-powered
  • Per-row pricing

See references/serverless-dbs.md for comparison and integration.

Show full SKILL.md (441 more words)Show less
Edge Functions

Cloudflare Workers:

  • <5ms cold starts (V8 isolates)
  • 200+ edge locations
  • 128MB memory per request

Deno Deploy:

  • TypeScript-native
  • Web Standard APIs
  • Global edge (<50ms)

Hono Framework:

  • Runs on all edge runtimes
  • 14KB bundle size
  • TypeScript-first

See references/edge-functions.md for patterns.

Best Practices

Security
  • Use secrets management (AWS Secrets Manager, Vault)
  • Enable mTLS for service-to-service communication
  • Implement least-privilege IAM roles
  • Scan container images for vulnerabilities
Cost Optimization
  • Use serverless databases for variable traffic (scale-to-zero)
  • Enable horizontal pod autoscaling (HPA) in Kubernetes
  • Right-size compute resources (CPU/memory)
  • Use spot instances for non-critical workloads
Performance
  • Deploy close to users (edge functions for global apps)
  • Use CDN for static assets (CloudFront, Cloudflare)
  • Implement caching strategies (Redis, CloudFront)
  • Monitor cold start times for serverless
Reliability
  • Implement health checks (Kubernetes liveness/readiness probes)
  • Set up auto-scaling (HPA, Lambda concurrency)
  • Use multi-region deployments for critical services
  • Implement circuit breakers and retries

Troubleshooting

Deployment Failures

Kubernetes pod fails to start:

  1. Check pod logs: kubectl logs <pod-name>
  2. Describe pod: kubectl describe pod <pod-name>
  3. Verify resource limits and requests
  4. Check image pull errors (imagePullSecrets)

Serverless cold starts too slow:

  1. Reduce bundle size (tree-shaking, code splitting)
  2. Use provisioned concurrency (AWS Lambda)
  3. Consider edge functions (Cloudflare Workers)
  4. Optimize initialization code

GitOps sync errors (ArgoCD/Flux):

  1. Verify Git repository access
  2. Check manifest validity (kubectl apply --dry-run)
  3. Review sync policies (prune, selfHeal)
  4. Check ArgoCD/Flux logs
Performance Issues

High service mesh overhead:

  1. Consider switching to Linkerd (5-10% vs Istio 25-35%)
  2. Disable unnecessary features
  3. Evaluate if service mesh is needed

Database connection pool exhaustion:

  1. Increase connection pool size
  2. Use serverless databases (Neon scale-to-zero)
  3. Implement connection pooling (PgBouncer)

See references/ files for detailed troubleshooting guides.

Migration Patterns

From Manual to IaC
  1. Inventory existing infrastructure
  2. Start with non-critical environments (dev, staging)
  3. Use Pulumi/OpenTofu to codify infrastructure
  4. Test in staging before production
  5. Gradual migration (one service at a time)
From Terraform to OpenTofu
bash
# 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.

From EC2 to Containers
  1. Containerize application (create Dockerfile)
  2. Test locally (Docker Compose)
  3. Deploy to staging (ECS/Cloud Run/Kubernetes)
  4. Monitor performance and costs
  5. Cutover production traffic (blue-green deployment)
From Containers to Serverless
  1. Identify stateless services
  2. Refactor to serverless-friendly patterns
  3. Use serverless databases (Neon/Turso)
  4. Deploy to Lambda/Cloud Functions
  5. Monitor cold starts and costs

Next Steps

After deploying applications:

  • Set up observability (metrics, logs, traces)
  • Implement CI/CD pipelines (GitHub Actions, GitLab CI)
  • Configure auto-scaling and resource limits
  • Set up disaster recovery and backups
  • Document runbooks for incident response

Additional Resources

© ancoleman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 19 other files (scripts, references) in skills/deploying-applications of ancoleman/ai-design-components.

  • SKILL.md
  • examples/k8s-argocd/README.md
  • examples/k8s-argocd/argocd/application.yaml
  • examples/k8s-argocd/base/deployment.yaml
  • examples/pulumi-aws/Pulumi.yaml
  • examples/pulumi-aws/README.md
  • examples/pulumi-aws/index.ts
  • examples/pulumi-aws/package.json
  • examples/pulumi-aws/tsconfig.json
  • outputs.yaml
  • references/deployment-strategies.md
  • references/edge-functions.md
  • references/gitops-argocd.md
  • references/kubernetes-patterns.md
  • references/opentofu-guide.md
  • … and 5 more

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Deploying Applications next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Deploying Applications compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deploying Applications this skillancoleman/ai-design-components526—~3.1kAutomated safety check: PassMIT
Provisioning Infrastructuretelagod/code-abyss244—~250Automated safety check: PassMIT
Devops EngineerYikai-Liao/symusic1891 repos~1.5kAutomated safety check: PassMIT
Devops Engineertheneoai/awesome-skills183—~2.1kAutomated safety check: PassMIT
Devops Excellencemajiayu000/spellbook286—~2.4kAutomated safety check: NotesMIT
Discover Infrarand/cc-polymath181—~783Automated safety check: PassMIT

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Categories

Questions about Deploying Applications

What does Deploying Applications do?

Deployment patterns from Kubernetes to serverless and edge functions. Deploying Applications is an agent skill from ancoleman/ai-design-components. Deployment patterns from Kubernetes to serverless and edge functions.

When should I use Deploying Applications?

Deploying Applications fits situations like: deploying applications; setting up CI/CD; managing infrastructure.

How do I install Deploying Applications in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill deploying-applications -a claude-code`. Or copy the skill folder (skills/deploying-applications in ancoleman/ai-design-components) into .claude/skills/deploying-applications in your project. Claude Code loads it when a task matches its description.

How do I install Deploying Applications in Codex?

Run `npx skills add ancoleman/ai-design-components --skill deploying-applications -a codex`. Or copy the skill folder (skills/deploying-applications in ancoleman/ai-design-components) into .agents/skills/deploying-applications in your project. Codex loads it when a task matches its description.

Can I use Deploying Applications in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ancoleman/ai-design-components --skill deploying-applications -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deploying-applications, .gemini/skills/deploying-applications, .github/skills/deploying-applications and .opencode/skills/deploying-applications in your project.

What does Deploying Applications need to run?

Going by SKILL.md and its folder, Deploying Applications needs TypeScript for the scripts in its folder and the command-line tools its instructions call (tofu, kubectl, vercel, python, npm and wrangler). Our summary lists: Python 3; Node.js; Docker.

Does Deploying Applications access the network?

SKILL.md names 5 domains. As links in the text: pulumi.com, opentofu.org, argo-cd.readthedocs.io, developers.cloudflare.com and neon.tech. This is read from the text; nothing was executed.

Is Deploying Applications safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Deploying Applications use?

Deploying Applications is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deploying Applications use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 31k tokens, read only when the agent opens those files.

What are the alternatives to Deploying Applications?

Skills that share tags, products or a category with Deploying Applications: Provisioning Infrastructure (telagod/code-abyss, 244 stars), Devops Engineer (Yikai-Liao/symusic, 189 stars), Devops Engineer (theneoai/awesome-skills, 183 stars) and Devops Excellence (majiayu000/spellbook, 286 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deploying Applications?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 526 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.