deployment▌

Expert guidance for deploying Next.js applications to Vercel with environment and runtime optimization. \n \n Covers environment variable configuration across dev, preview, and production environments, with emphasis on preventing secrets exposure via NEXT_PUBLIC_ \n Distinguishes between Edge and Serverless function runtimes, helping you choose the right execution model for API routes and functions \n Addresses build optimization strategies to reduce cold starts, bundle size, and deployment time

View, manage, and troubleshoot Railway deployments with logs, redeploy, and removal commands. \n \n List deployments and view deploy or build logs with filtering by level, text search, time range, or specific deployment ID \n Redeploy the most recent deployment or restart containers without rebuilding to pick up external resource changes \n Remove deployments (stops the service but keeps it intact; use the environment skill to delete services entirely) \n Supports JSON output for all commands an

Validates Bicep deployments to Azure before execution, detecting syntax errors, permission issues, and previewing infrastructure changes. \n \n Supports both Azure CLI ( az ) and Azure Developer CLI ( azd ) workflows, auto-detecting project type via azure.yaml \n Runs Bicep syntax validation, what-if analysis across all deployment scopes (resource group, subscription, management group, tenant), and permission checks with fallback to reduced-permission validation \n Captures detailed what-if resu

CI/CD pipelines, Docker containerization, deployment strategies, health checks, and production readiness for web applications. \n \n Covers three deployment patterns: rolling (zero-downtime gradual updates), blue-green (atomic traffic switching), and canary (percentage-based rollout with monitoring) \n Includes multi-stage Dockerfile examples for Node.js, Go, and Python with best practices for image size, non-root users, and health checks \n Provides GitHub Actions pipeline templates, environmen

Model deployment is the process of taking a trained machine learning model and making it available for production use through APIs, web services, or batch processing systems.

Automated deployment of Expo apps to iOS App Store, Android Play Store, web hosting, and preview environments. \n \n Supports production builds and submissions for iOS (App Store and TestFlight) and Android (Google Play Store) with single commands \n Includes EAS Hosting for web deployments with automatic PR preview URLs and production domain support \n Provides CI/CD workflow automation via EAS Workflows for triggered builds and submissions on code pushes \n Automatic version management with re