Deploy frontend and full-stack apps on Vercel with previews, edge functions, environment promotion, and production guardrails. Use when shipping Next.js, SvelteKit, or static sites with zero-config CI/CD.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill vercel-deployments
Ship web apps quickly with preview environments and managed edge infrastructure.
Use this skill when:
# Install CLI
npm i -g vercel
# Login and link project
vercel login
vercel link
# Deploy to preview
vercel
# Deploy to production
vercel --prod
# Pull environment variables locally
vercel env pull .env.local
// vercel.json
{
"framework": "nextjs",
"buildCommand": "npm run build",
"outputDirectory": ".next",
"installCommand": "npm ci",
"regions": ["iad1", "sfo1", "cdg1"],
"headers": [
{
"source": "/api/(.*)",
"headers": [
{ "key": "Cache-Control", "value": "no-store" },
{ "key": "X-Content-Type-Options", "value": "nosniff" }
]
},
{
"source": "/(.*)",
"headers": [
{ "key": "X-Frame-Options", "value": "DENY" },
{ "key": "Strict-Transport-Security", "value": "max-age=63072000; includeSubDomains" }
]
}
],
"redirects": [
{ "source": "/blog/:slug", "destination": "/posts/:slug", "permanent": true }
],
"rewrites": [
{ "source": "/api/v1/:path*", "destination": "https://api.example.com/:path*" }
]
}
# Add environment variables
vercel env add DATABASE_URL production
vercel env add DATABASE_URL preview
vercel env add NEXT_PUBLIC_API_URL production
# List all env vars
vercel env ls
# Pull to local .env.local
vercel env pull .env.local
# Remove an env var
vercel env rm SECRET_KEY production
# Production — real credentials
vercel env add DATABASE_URL production <<< "postgresql://prod-host:5432/app"
vercel env add STRIPE_SECRET_KEY production
# Preview — staging/test credentials
vercel env add DATABASE_URL preview <<< "postgresql://staging-host:5432/app"
vercel env add STRIPE_SECRET_KEY preview # Use test mode key
# Development — local values
vercel env add DATABASE_URL development <<< "postgresql://localhost:5432/app"
// app/api/geo/route.ts — Edge API route (Next.js App Router)
import { NextRequest } from 'next/server';
export const runtime = 'edge';
export function GET(request: NextRequest) {
const country = request.geo?.country || 'US';
const city = request.geo?.city || 'Unknown';
return Response.json({
country,
city,
region: request.geo?.region,
timestamp: new Date().toISOString(),
});
}
// middleware.ts — Edge middleware for auth/redirects
import { NextResponse } from 'next/server';
import type { NextRequest } from 'next/server';
export function middleware(request: NextRequest) {
// Block non-US traffic from admin
if (request.nextUrl.pathname.startsWith('/admin')) {
if (request.geo?.country !== 'US') {
return NextResponse.redirect(new URL('/blocked', request.url));
}
}
// Add security headers
const response = NextResponse.next();
response.headers.set('X-Request-Id', crypto.randomUUID());
return response;
}
export const config = {
matcher: ['/admin/:path*', '/api/:path*'],
};
# .github/workflows/preview.yml
name: Vercel Preview
on: pull_request
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- run: npm ci
- run: npm run lint
- run: npm run test
- name: Deploy to Vercel Preview
id: deploy
run: |
npm i -g vercel
URL=$(vercel --token ${{ secrets.VERCEL_TOKEN }} --yes)
echo "url=$URL" >> "$GITHUB_OUTPUT"
- name: Comment PR with preview URL
uses: actions/github-script@v7
with:
script: |
github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: `Preview deployed: ${{ steps.deploy.outputs.url }}`
});
# Deployments
vercel # Deploy to preview
vercel --prod # Deploy to production
vercel rollback # Rollback last production deploy
vercel promote <url> # Promote preview to production
# Domains
vercel domains add example.com
vercel domains ls
vercel certs ls
# Logs
vercel logs <deployment-url>
vercel logs <deployment-url> --follow
# Project management
vercel project ls
vercel project rm <name>
# Inspect deployment
vercel inspect <deployment-url>
vercel.json headers for security (CSP, HSTS, X-Frame-Options)# Enable Speed Insights in Next.js
npm install @vercel/speed-insights
# Enable Web Analytics
npm install @vercel/analytics
// app/layout.tsx
import { Analytics } from '@vercel/analytics/react';
import { SpeedInsights } from '@vercel/speed-insights/next';
export default function RootLayout({ children }) {
return (
<html>
<body>
{children}
<Analytics />
<SpeedInsights />
</body>
</html>
);
}
| Issue | Solution |
|-------|---------|
| Build fails | Check vercel logs, verify Node.js version in engines field |
| Env vars missing | Run vercel env pull, check variable scope (preview vs production) |
| Edge function timeout | Edge has 30s limit; move heavy work to serverless (no runtime = 'edge') |
| Cold starts slow | Use edge runtime where possible, reduce bundle size |
| Domain not working | Check DNS propagation, verify vercel domains configuration |
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 bagelhole/vercel-deployments 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.
Without those the skill loads but fails at the first command.