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performance-optimization

Optimize a measured performance problem when the user names a latency/load/Core Web Vitals/resource target or current profiling evidence identifies a bottleneck; do not trigger from generic implementation, review, or an unsupported suspicion that code may be slow.

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Performance Optimization

Overview

Measure before optimizing. Performance work without measurement is guessing — and guessing leads to premature optimization that adds complexity without improving what matters. Profile first, identify the actual bottleneck, fix it, measure again. Optimize only what measurements prove matters.

When to Use

  • Performance requirements exist in the spec (load time budgets, response time SLAs)
  • Users or monitoring report slow behavior
  • Core Web Vitals scores are below thresholds
  • You suspect a change introduced a regression
  • Building features that handle large datasets or high traffic

When NOT to use: Don't optimize before you have evidence of a problem. Premature optimization adds complexity that costs more than the performance it gains.

Core Web Vitals Targets

MetricGoodNeeds ImprovementPoor
LCP (Largest Contentful Paint)≤ 2.5s≤ 4.0s> 4.0s
INP (Interaction to Next Paint)≤ 200ms≤ 500ms> 500ms
CLS (Cumulative Layout Shift)≤ 0.1≤ 0.25> 0.25

The Optimization Workflow

1. MEASURE  → Establish baseline with real data
2. IDENTIFY → Find the actual bottleneck (not assumed)
3. FIX      → Address the specific bottleneck
4. VERIFY   → Measure again, confirm improvement
5. GUARD    → Add monitoring or tests to prevent regression

🔴 CHECKPOINT / 🛑 STOP: No Speculative Optimization

Do not optimize because a change "seems faster" or an anti-pattern is familiar. If there is no baseline measurement, user-reported symptom, production signal, or explicit performance budget, stop and return BLOCKED_NEEDS_MEASUREMENT instead of changing code.

Step 1: Measure

Choose the smallest measurement source that can establish the accepted performance claim; use both only when the claim explicitly spans lab reproducibility and real-user impact:

  • Synthetic (Lighthouse, OpenCode Playwright/browser tooling, DevTools Performance tab): Controlled conditions, reproducible. Best for CI regression detection and isolating specific issues.
  • RUM (web-vitals library, CrUX): Real-user data in real conditions. Use when the claim concerns production/user impact and current policy allows the data source.

Frontend:

# Synthetic: Lighthouse in CI/manual runs
# OpenCode: return a concrete direct-browser evidence need to ROSE when runtime UI measurement is the smallest sufficient path
# Manual reference: Chrome DevTools → Performance tab → Record

# RUM: Web Vitals library in code
import { onLCP, onINP, onCLS } from 'web-vitals';

onLCP(console.log);
onINP(console.log);
onCLS(console.log);

Backend:

# Response time logging
# Application Performance Monitoring (APM)
# Database query logging with timing

# Simple timing
console.time('db-query');
const result = await db.query(...);
console.timeEnd('db-query');

Where to Start Measuring

Use the symptom to decide what to measure first:

What is slow?
├── First page load
│   ├── Large bundle? --> Measure bundle size, check code splitting
│   ├── Slow server response? --> Measure TTFB in OpenCode browser network requests or DevTools Network waterfall
│   │   ├── DNS long? --> Add dns-prefetch / preconnect for known origins
│   │   ├── TCP/TLS long? --> Enable HTTP/2, check edge deployment, keep-alive
│   │   └── Waiting (server) long? --> Profile backend, check queries and caching
│   └── Render-blocking resources? --> Check network waterfall for CSS/JS blocking
├── Interaction feels sluggish
│   ├── UI freezes on click? --> Profile main thread, look for long tasks (>50ms)
│   ├── Form input lag? --> Check re-renders, controlled component overhead
│   └── Animation jank? --> Check layout thrashing, forced reflows
├── Page after navigation
│   ├── Data loading? --> Measure API response times, check for waterfalls
│   └── Client rendering? --> Profile component render time, check for N+1 fetches
└── Backend / API
    ├── Single endpoint slow? --> Profile database queries, check indexes
    ├── All endpoints slow? --> Check connection pool, memory, CPU
    └── Intermittent slowness? --> Check for lock contention, GC pauses, external deps

Measurement-Unavailable Fallbacks

TriggerFirst actionIf still unresolved
Browser profiling unavailableCapture network timing, console warnings, screenshot/snapshot, and available Performance API entriesMark Core Web Vitals as [UNVERIFIED]; do not claim frontend improvement
Production/RUM data unavailableUse reproducible synthetic measurements with device/network settings statedLabel the result synthetic_only and request RUM before broad rollout
Backend profiler unavailableAdd bounded timing around the suspected path or inspect existing logs/APMMark root cause as Hypothesis; do not rewrite unrelated code
Measurements conflictPrefer production/RUM for user impact and synthetic traces for root causeHold optimization until the conflict is explained or scoped

Step 2: Identify the Bottleneck

Common bottlenecks by category:

Frontend:

SymptomLikely CauseInvestigation
Slow LCPLarge images, render-blocking resources, slow serverCheck network waterfall, image sizes
High CLSImages without dimensions, late-loading content, font shiftsCheck layout shift attribution
Poor INPHeavy JavaScript on main thread, large DOM updatesCheck long tasks in Performance trace
Slow initial loadLarge bundle, many network requestsCheck bundle size, code splitting

Backend:

SymptomLikely CauseInvestigation
Slow API responsesN+1 queries, missing indexes, unoptimized queriesCheck database query log
Memory growthLeaked references, unbounded caches, large payloadsHeap snapshot analysis
CPU spikesSynchronous heavy computation, regex backtrackingCPU profiling
High latencyMissing caching, redundant computation, network hopsTrace requests through the stack

Step 3: Fix Common Anti-Patterns

N+1 Queries (Backend)

// BAD: N+1 — one query per task for the owner
const tasks = await db.tasks.findMany();
for (const task of tasks) {
  task.owner = await db.users.findUnique({ where: { id: task.ownerId } });
}

// GOOD: Single query with join/include
const tasks = await db.tasks.findMany({
  include: { owner: true },
});

Unbounded Data Fetching

// BAD: Fetching all records
const allTasks = await db.tasks.findMany();

// GOOD: Paginated with limits
const tasks = await db.tasks.findMany({
  take: 20,
  skip: (page - 1) * 20,
  orderBy: { createdAt: 'desc' },
});

Missing Image Optimization (Frontend)

<!-- BAD: No dimensions, no format optimization -->
<img src="/hero.jpg" />

<!-- GOOD: Hero / LCP image — art direction + resolution switching, high priority -->
<!--
  Two techniques combined:
  - Art direction (media): different crop/composition per breakpoint
  - Resolution switching (srcset + sizes): right file size per screen density
-->
<picture>
  <!-- Mobile: portrait crop (8:10) -->
  <source
    media="(max-width: 767px)"
    srcset="/hero-mobile-400.avif 400w, /hero-mobile-800.avif 800w"
    sizes="100vw"
    width="800"
    height="1000"
    type="image/avif"
  />
  <source
    media="(max-width: 767px)"
    srcset="/hero-mobile-400.webp 400w, /hero-mobile-800.webp 800w"
    sizes="100vw"
    width="800"
    height="1000"
    type="image/webp"
  />
  <!-- Desktop: landscape crop (2:1) -->
  <source
    srcset="/hero-800.avif 800w, /hero-1200.avif 1200w, /hero-1600.avif 1600w"
    sizes="(max-width: 1200px) 100vw, 1200px"
    width="1200"
    height="600"
    type="image/avif"
  />
  <source
    srcset="/hero-800.webp 800w, /hero-1200.webp 1200w, /hero-1600.webp 1600w"
    sizes="(max-width: 1200px) 100vw, 1200px"
    width="1200"
    height="600"
    type="image/webp"
  />
  <img
    src="/hero-desktop.jpg"
    width="1200"
    height="600"
    fetchpriority="high"
    alt="Hero image description"
  />
</picture>

<!-- GOOD: Below-the-fold image — lazy loaded + async decoding -->
<img
  src="/content.webp"
  width="800"
  height="400"
  loading="lazy"
  decoding="async"
  alt="Content image description"
/>

Unnecessary Re-renders (React)

// BAD: Creates new object on every render, causing children to re-render
function TaskList() {
  return <TaskFilters options={{ sortBy: 'date', order: 'desc' }} />;
}

// GOOD: Stable reference
const DEFAULT_OPTIONS = { sortBy: 'date', order: 'desc' } as const;
function TaskList() {
  return <TaskFilters options={DEFAULT_OPTIONS} />;
}

// Use React.memo for expensive components
const TaskItem = React.memo(function TaskItem({ task }: Props) {
  return <div>{/* expensive render */}</div>;
});

// Use useMemo for expensive computations
function TaskStats({ tasks }: Props) {
  const stats = useMemo(() => calculateStats(tasks), [tasks]);
  return <div>{stats.completed} / {stats.total}</div>;
}

Large Bundle Size

// Modern bundlers (Vite, webpack 5+) handle named imports with tree-shaking automatically,
// provided the dependency ships ESM and is marked `sideEffects: false` in package.json.
// Profile before changing import styles — the real gains come from splitting and lazy loading.

// GOOD: Dynamic import for heavy, rarely-used features
const ChartLibrary = lazy(() => import('./ChartLibrary'));

// GOOD: Route-level code splitting wrapped in Suspense
const SettingsPage = lazy(() => import('./pages/Settings'));

function App() {
  return (
    <Suspense fallback={<Spinner />}>
      <SettingsPage />
    </Suspense>
  );
}

Missing Caching (Backend)

// Cache frequently-read, rarely-changed data
const CACHE_TTL = 5 * 60 * 1000; // 5 minutes
let cachedConfig: AppConfig | null = null;
let cacheExpiry = 0;

async function getAppConfig(): Promise<AppConfig> {
  if (cachedConfig && Date.now() < cacheExpiry) {
    return cachedConfig;
  }
  cachedConfig = await db.config.findFirst();
  cacheExpiry = Date.now() + CACHE_TTL;
  return cachedConfig;
}

// HTTP caching headers for static assets
app.use('/static', express.static('public', {
  maxAge: '1y',           // Cache for 1 year
  immutable: true,        // Never revalidate (use content hashing in filenames)
}));

// Cache-Control for API responses
res.set('Cache-Control', 'public, max-age=300'); // 5 minutes

Performance Budget

Set budgets and enforce them:

JavaScript bundle: < 200KB gzipped (initial load)
CSS: < 50KB gzipped
Images: < 200KB per image (above the fold)
Fonts: < 100KB total
API response time: < 200ms (p95)
Time to Interactive: < 3.5s on 4G
Lighthouse Performance score: ≥ 90

Enforce in CI:

# Bundle size check
npx bundlesize --config bundlesize.config.json

# Lighthouse CI
npx lhci autorun

Inline Performance Checklist

Use the workflow, bottleneck tables, budget, and verification checklist in this file as the source of truth. Do not reference external checklist files unless they exist in this skill directory.

Common Rationalizations

RationalizationReality
"We'll optimize later"Performance debt compounds. Fix obvious anti-patterns now, defer micro-optimizations.
"It's fast on my machine"Your machine isn't the user's. Profile on representative hardware and networks.
"This optimization is obvious"If you didn't measure, you don't know. Profile first.
"Users won't notice this delay"Do not assume impact; compare the named budget or representative measurement.
"The framework handles performance"Frameworks prevent some issues but can't fix N+1 queries or oversized bundles.

Red Flags

  • Optimization without profiling data to justify it
  • Speculative performance rewrites when measurement is unavailable
  • N+1 query patterns in data fetching
  • List endpoints without pagination
  • Images without dimensions, lazy loading, or responsive sizes
  • Bundle size growing without review
  • No performance monitoring in production
  • React.memo and useMemo everywhere (overusing is as bad as underusing)

Verification

For the selected measured performance claim, apply only relevant rows and let the canonical owner choose the final evidence:

  • Before and after measurements exist (specific numbers)
  • The specific bottleneck is identified and addressed
  • Core Web Vitals are checked only when a Web Vitals claim is in scope
  • Bundle size is checked only when the changed bundle path is in scope
  • Query count is checked only when data-fetching/database behavior is in scope
  • Performance budget passes in CI (if configured)
  • Behavior-regression evidence is run only when the exact changed claim requires it

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