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Performance · Version 1.6.0 · Reviewed 2026-08-02

Frontend Performance Agent

Locate and remove the dominant bottleneck in bundle analysis and rendering strategy with evidence, explicit trade-offs, and a verification plan.

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

Improves Core Web Vitals through bundle analysis, rendering strategy, and asset delivery.

₹199 one-time

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What this skill helps you do

  • Bundle analysis
  • Rendering strategy
  • Core Web Vitals

How Frontend Performance Agent works

You provide

Component code, field metrics, and the failing interaction

It inspects

Render triggers and layout stability for bundle analysis

It decides

A rendering strategy fix targeting the measured vital

You verify

Field Core Web Vitals and keyboard traversal re-checked

What it checks first

Frontend Performance Skill improves Core Web Vitals through bundle analysis, rendering strategy, and asset delivery. Use it when the work involves Bundle analysis, Rendering strategy, Core Web Vitals.

  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. Define the measured baseline and user-visible target for bundle analysis.
  2. Attribute the dominant cost or latency mechanism affecting rendering strategy.
  3. Rank Core Web Vitals changes by expected impact, confidence, effort, and regression risk.
  4. Validate under representative load and retain guardrail metrics that detect a shifted bottleneck.

Deliverables

  • Bundle analysis assessment
  • Rendering strategy decision and action plan
  • Core Web Vitals verification checklist

Evidence requirements

  • Profiles, traces, timings, resource metrics, and workload shape
  • Baseline and target percentile
  • Environment, concurrency, and payload details

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

Our LCP is 4.2 seconds on mobile while our lab scores look fine. Where do we start?

Expected output

The hero image is loaded by JavaScript after hydration, so LCP waits for the entire bundle to parse and execute. Lab runs hide this because they warm the cache, which is also why field data and lab data disagree. Preload the image and serve it from markup rather than from a client-side effect...

Boundaries and compatibility

Ideal for

  • Bundle analysis: produce a decision or artifact grounded in supplied evidence.
  • Rendering strategy: produce a decision or artifact grounded in supplied evidence.
  • Core Web Vitals: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Optimizing without a baseline
  • Using averages where tail latency determines experience

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.