performance-optimization

Performance Optimization Skill

When to apply

• Performance requirements are stated in the spec or issue (e.g., "list endpoint must p95 < 200ms")
• Users or monitoring report slow behavior with a reproducible scenario
• Core Web Vitals scores fall below "Good" thresholds (LCP < 2.5s, INP < 200ms, CLS < 0.1)
• A specific commit or PR introduced a measurable regression vs prior baseline
• Code handles datasets large enough that complexity dominates (n > 10k or rps > 100)

Skip when:

• There's no measurement showing a problem — "feels slow" without a profile is not a justification
• The fix would add complexity disproportionate to the win (5% improvement at the cost of unreadable code)
• Performance is dominated by a downstream system you don't control (e.g., the LLM round-trip)
• The hot path is run once per cold-start — micro-optimizing startup isn't worth the readability cost

"Premature optimization is the root of all evil." Don't optimize before you have evidence. The cost of complexity is permanent; the cost of waiting for evidence is one more profile run.

The MIFVG cycle

1. MEASURE — establish baseline with real data. Synthetic benchmarks are starting points, not ground truth. Capture both p50 and p95.
2. IDENTIFY — profile. Don't guess the bottleneck — measure it. Tools: node --prof, clinic.js, browser DevTools Performance, pprof for memory.
3. FIX — address the specific bottleneck. One change at a time. If two things are slow, fix the worst one first and re-measure.
4. VERIFY — re-measure with the same scenario. The improvement must be reproducible, not a single lucky run.
5. GUARD — add a monitoring assertion, a test budget, or a performance gate so the fix doesn't silently regress. The Beyoncé Rule applies — if you measured it, put a guard on it.

Common patterns to fix (when evidence supports)

Pattern	Symptom	Fix
N+1 queries	One query per record in a loop	Single batched query with IN clause or proper join
Unbounded data	Fetching all rows when 50 would do	Pagination + cursor-based limits
Synchronous I/O in hot path	readFileSync, execSync per request	Async + worker pool, or cache the result
Unnecessary serialization	JSON-parse-and-stringify the same blob multiple times	Pass through, or hash-cache by input
Cache miss patterns	Read-only data fetched on every render/request	TTL cache or memoization (with size bound)
Bundle size growth	First-paint regression after a feature add	Dynamic import for heavy features, code splitting at route boundaries
Re-render storms (React/Svelte/etc.)	Component renders 100x for one state change	Stable references, key-based memoization, lift state up

Anti-rationalization — Performance

Excuse	Counter
"I'll add the optimization while I'm in here"	Optimization without a profile is gambling. The added complexity is permanent; the win may be zero or negative.
"Big-O says it's faster"	Big-O is asymptotic. For your actual n, the constant factors and cache effects often dominate. Measure.
"JavaScript engines optimize this"	Modern engines optimize hot loops, not your data flow. Don't assume — profile.
"It's a tiny win but free"	Free in code, expensive in review and bisect. Many "tiny wins" together obscure the actual hot path.
"Premature optimization is fine if it's clean"	The dichotomy isn't "messy vs clean optimization." It's "with evidence vs without." Without evidence, even clean optimization is premature.
"We'll measure in production"	You won't. Production has too much noise. Build a representative reproduction first.

Red flags

• Optimization PR with no before/after numbers
• "Optimization" that's actually a refactor disguised as a perf win
• Removing logging, validation, or error handling "for performance" without a profile justifying it
• Use of useMemo / React.memo / signal() everywhere as a precaution
• Bundle size grew significantly with no entry in the changelog
• New caching layer without a documented invalidation strategy or size bound

Verification checklist

• Before/after measurements with specific numbers (p50 and p95) attached to the PR
• Reproduction scenario documented so the next reviewer can verify
• The bottleneck was identified by profiler, not guessed
• Existing tests still pass: pnpm lint && pnpm typecheck && pnpm test
• Bundle size hasn't increased (run a size check if the PR touches the website or CLI bundle)
• No new caching without an invalidation/size strategy
• Performance guard added (test budget, monitoring threshold, or CI gate) to prevent silent regression
• If applicable: Core Web Vitals still in "Good" thresholds after the change