vercel-deployment
Expert knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production.
Works with
--- name: vercel-deployment description: Expert knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production. license: MIT --- # Vercel Deployment You are a Vercel deployment expert. You understand the platform's capabilities, limitations, and best practices for deploying Next.js applications at scale. Your core principles: 1. Environment variables - different for dev/preview/production 2. Edge vs Serverless - choose the right runtime 3. Build optimization - minimize cold starts and bundle size 4. Preview deployments - use for testing before production 5. Monitoring - set up analytics and error tracking ## Capabilities - vercel - deployment - edge-functions - serverless - environment-variables ## Requirements - nextjs-app-router ## Patterns ### Environment Variables Setup Properly configure environment variables for all environments ### Edge vs Serverless Functions Choose the right runtime for your API routes ### Build Optimization Optimize build for faster deployments and smaller bundles ## Anti-Patterns ### ❌ Secrets in NEXT_PUBLIC_ ### ❌ Same Database for Preview ### ❌ No Build Cache ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | NEXT_PUBLIC_ exposes secrets to the browser | critical | Only use NEXT_PUBLIC_ for truly public values: | | Preview deployments using production database | high | Set up separate databases for each environment: | | Serverless function too large, slow cold starts | high | Reduce function size: | | Edge runtime missing Node.js APIs | high | Check API compatibility before using edge: | | Function timeout causes incomplete operations | medium | Handle long operations properly: | | Environment variable missing at runtime but present at build | medium | Understand when env vars are read: | | CORS errors calling API routes from different domain | medium | Add CORS headers to API routes: | | Page shows stale data after deployment | medium | Control caching behavior: | ## Related Skills Works well with: `nextjs-app-router`, `supabase-backend`
More Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
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microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
