Deployment Prep
A structured deployment checklist ensures nothing is missed. The scope boundary (no feature/UI changes) keeps Lovable focused on infrastructure without introducing regressions.
Prepare this Lovable app for production deployment:\n\n1. Environment variables:\n - Verify all Supabase credentials use environment variables (not hardcoded)\n - Ensure NEXT_PUBLIC_ prefix only on client-safe vars\n - Create a .env.example listing every required variable\n\n2. Error handling:\n - Add global error boundary component that shows a friendly error page\n - Add try/catch to all Supabase queries with user-facing error messages\n - Log errors to console in development, suppress in production\n\n3. Performance:\n - Add loading.tsx files for route-level suspense boundaries\n - Lazy load heavy components (charts, rich text editors)\n - Optimize images with next/image where applicable\n\n4. SEO basics:\n - Add metadata (title, description) to each page\n - Add Open Graph tags for {{app_name}}\n - Generate a sitemap.xml\n\n5. Security:\n - Verify all RLS policies are active\n - Remove any console.log statements with sensitive data\n - Set secure cookie options for auth tokens\n\nDo not change features or UI. Only add production infrastructure.Variables to customize
Why this prompt works
A structured deployment checklist ensures nothing is missed. The scope boundary (no feature/UI changes) keeps Lovable focused on infrastructure without introducing regressions.
Save this prompt to your library
Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.
Related prompts
Forcing the agent to plan before acting prevents premature execution and wasted steps. Explicit dependency mapping enables parallel execution and catches logical gaps early.
Tool Selection AgentThe ReAct pattern (Reason + Act) creates an explicit reasoning trace that improves tool selection accuracy. The error-handling rule prevents infinite retry loops.
Prompt CompressorExplicitly requiring all functional requirements to be preserved prevents the model from over-compressing and losing critical instructions.
Memory Management AgentExplicit memory read/write instructions create agents that improve over time. Categorization keeps memories organized, and the deduplication rule prevents context bloat.