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Frontend · Version 1.5.0 · Reviewed 2026-08-02

React Native Performance Advisor

Improve JS-thread profiling and list optimization with evidence, explicit trade-offs, and a verification plan.

4 method steps 7 documented failure modes 5 diagnostic checks 7 quality gates

Diagnoses JavaScript-thread stalls, bridge traffic, list rendering, image memory, navigation transitions, and native-module bottlenecks.

₹199 one-time

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What this skill helps you do

  • JS-thread profiling
  • List optimization
  • Animation smoothness

How React Native Performance Advisor works

You provide

Component code, field metrics, and the failing interaction

It inspects

Render triggers and layout stability for JS-thread profiling

It decides

A list optimization fix targeting the measured vital

You verify

Field Core Web Vitals and keyboard traversal re-checked

What it checks first

React Native Performance Advisor diagnoses JavaScript-thread stalls, bridge traffic, list rendering, image memory, navigation transitions, and native-module bottlenecks. Use it when the work involves JS-thread profiling, List optimization, Animation smoothness.

  1. Whether re-renders come from changed props, changed context, or a new object identity created during render.
  2. Which Core Web Vital is failing, since LCP, INP, and CLS have completely different causes and fixes.
  3. Whether state lives at the right level, because state placed too high re-renders subtrees that never read it.
  4. Effect dependency arrays that lie, either omitting a dependency or including an unstable one.
  5. Bundle composition: whether a single dependency dominates the critical path.

Failure modes it recognizes

  • An inline object or arrow function in props defeating memoization on every render.
  • A `useEffect` that sets state derived from props, causing a double render and occasional flicker.
  • Layout shift from images and embeds without reserved dimensions, damaging CLS after content loads.
  • A long task on the main thread blocking interaction response and inflating INP.
  • Stale closure capturing an old value inside an interval or subscription callback.
  • Hydration mismatch from rendering time, randomness, or browser-only APIs during server render.
  • Focus lost after a route change, leaving keyboard and screen-reader users stranded.

Answers it will reject

  • Wrapping everything in `memo` and `useCallback`, which adds comparison cost without removing the identity churn.
  • Fixing a race by adding a timeout, which reorders the symptom instead of the cause.
  • Using `aria-label` to patch a control that should have been a native element with real semantics.
  • Measuring performance in development mode, where the framework runs extra work that does not ship.

Decision rules it applies

  • Move state down or split context before reaching for memoization.
  • Derive during render instead of synchronizing with an effect; effects are for external systems.
  • Reserve space for anything that loads asynchronously to protect layout stability.
  • Prefer native semantic elements; ARIA is a correction layer, not a foundation.

Evidence it asks for

  • Profile with the framework profiler to attribute renders to a specific trigger.
  • Collect field Core Web Vitals rather than lab scores, since lab conditions hide real-device behavior.
  • Test keyboard-only navigation and screen-reader output for any interactive change.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to JS-thread profiling.
  2. Trace concrete failure or abuse paths for list optimization; do not report checklist items without a mechanism.
  3. Prioritize animation smoothness findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • JS-thread profiling assessment
  • List optimization decision and action plan
  • Animation smoothness verification checklist

Evidence requirements

  • Interface code, rendered behavior, and user journey
  • Browser/device matrix and accessibility tree
  • Performance and usability observations

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Scrolling a message list drops frames on Android after several thousand messages are loaded.

Expected output

The list renders dynamic-height items without stable layout estimates and recreates every row callback on each update. Bound the window, stabilize keys/callbacks, and move image decoding pressure off the JS path...

Boundaries and compatibility

Ideal for

  • JS-thread profiling: produce a decision or artifact grounded in supplied evidence.
  • List optimization: produce a decision or artifact grounded in supplied evidence.
  • Animation smoothness: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating automated accessibility scans as complete
  • Changing visual style without preserving behavior

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.