Diagnose slow launches and API cold starts in my mobile app
A codebase-specific diagnosis of startup time, serverless cold starts, and network waste, with fixes ranked by impact on perceived speed.
The prompt
Analyze why my mobile app feels slow and rank what to fix. This is a diagnosis, not a refactor - do not rewrite code unless I ask.
App stack: [e.g. "Expo / React Native", "Flutter", "native iOS"]
Backend: [e.g. "Supabase Edge Functions", "AWS Lambda", "Firebase Cloud Functions"]
Symptoms: [e.g. "first screen takes 4s", "first API call after idle is slow", "lists stutter"]
Measurements I have: [timings, traces, logs - or "none yet"]
Code: [PASTE app startup code, data-fetching layer, key screens, and the serverless functions they call]
Check specifically:
1. App startup - work done before first render, bundle size, eager imports, font/asset loading, splash screen timing.
2. Cold starts - function bundle size and dependencies, initialization at module scope, database client creation per request, region distance from users, whether functions are called in sequence on launch.
3. Network - request waterfalls, chatty endpoints that could be batched, over-fetching, missing caching, no retry/timeout handling on poor mobile connections.
4. Perceived performance - missing skeletons or optimistic UI, blocking spinners, data that could be prefetched or cached locally.
5. Rendering - unnecessary re-renders, heavy lists without virtualization, large images.
Base every finding on the code I shared; where you would need a measurement to confirm, say so and tell me how to measure it.
Return a table: Issue | Evidence in the code | Likely impact (high/med/low) | Fix | Effort. Then a top-3 action list and a short plan to measure before and after.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Software Development prompts
What to fill in
- e.g. "Expo / React Native", "Flutter", "native iOS"
- timings, traces, logs - or "none yet"
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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