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Performance Engineering
A systematic framework for diagnosing, measuring, and improving application performance.
This skill covers the full performance lifecycle - from identifying bottlenecks with profilers
and flame graphs, to eliminating memory leaks with heap snapshots, to validating improvements
with rigorous benchmarks. It applies across the stack: Node.js backend, browser frontend,
and database query layer. The guiding philosophy is always measure first, optimize second.
When to use this skill
Trigger this skill when the user:
Observes high P95/P99 latency or slow response times in production
Reports memory growing unboundedly or OOM crashes
Wants to profile CPU usage or generate a flame graph
Needs to benchmark two implementations to decide between them
Is investigating event loop blocking or long tasks in the browser
Wants to reduce JavaScript bundle size, TTI, or Core Web Vitals scores
Is tuning garbage collection, heap limits, or worker thread pools
Needs to set up continuous performance monitoring or performance budgets
Is debugging N+1 queries, slow database queries, or connection pool exhaustion
Do NOT trigger this skill for:
General code quality refactors with no performance goal (use clean-code skill)
Capacity planning and infrastructure scaling decisions (use backend-engineering skill)
Key principles
Measure first, always - Never optimize based on intuition. Instrument the code,
collect data, and let profiler output tell you where time actually goes. Assumptions
about bottlenecks are wrong more often than not.
Optimize the bottleneck, not the code - Amdahl's Law: speeding up a component
that is 5% of total runtime yields at most 5% improvement. Find the dominant cost,
fix that, then re-measure to find the new dominant cost. Repeat.
Set performance budgets upfront - Define what "fast enough" means before writing
a line. A target of "P99 < 200ms" or "bundle < 150KB" creates a measurable pass/fail
criterion. Without a budget, optimization is endless.
Test under realistic load - A function that takes 1ms with 10 users may take
800ms with 1000 concurrent users due to lock contention, cache pressure, or
connection pool exhaustion. Always load-test against production-like data volumes.
Premature optimization is the root of all evil - (Knuth) Write correct, readable
code first. Profile in a realistic environment. Only then optimize the measured
hot path. Code that sacrifices clarity for unmeasured performance gains is technical debt.
Core concepts
Latency vs throughput - Latency is how long one request takes. Throughput is how
many requests complete per second. Optimizing one does not automatically improve the
other. A batching strategy can dramatically increase throughput while increasing
individual request latency.
Percentiles (P50/P95/P99) - Averages hide outliers. P99 latency is the experience
of 1 in 100 users. In high-traffic systems, the P99 user matters. Never report only
averages - always report P50, P95, and P99 together.
Flame graphs - A visualization of sampled call stacks where width represents time
spent. Wide bars at the top of a flame are hot functions to optimize. Generated by
0x, clinic flame, or Chrome DevTools CPU profiler.
Heap snapshots - A point-in-time dump of all live objects in the JS heap. Compare
two snapshots (before/after a suspected leak window) to find objects accumulating
without being GC'd. Available in Chrome DevTools and Node.js v8.writeHeapSnapshot().
Profiler types - Sampling profilers (low overhead, statistical) vs instrumentation
profilers (exact counts, higher overhead). Use sampling for production diagnosis, instrumentation
for precise benchmark attribution.
Amdahl's Law - Max speedup = 1 / (1 - P + P/N) where P is the parallelizable fraction
and N is the number of processors. A program that is 90% parallelizable has a theoretical
max speedup of 10x regardless of how many cores you add.
Common tasks
Profile CPU usage
Use Node.js built-in profiler or 0x for flame graphs:
# Built-in V8 profiler - generates isolate-*.log
node --prof server.js
# Run your load, then process the log
node --prof-process isolate-*.log > profile.txt
# 0x - generates interactive flame graph HTML
npx 0x -- node server.js
# Then apply load; 0x auto-generates flamegraph.html
In TypeScript, mark hot sections explicitly for DevTools profiling:
// Wrap suspected hot paths to isolate them in profilesfunctionprocessItems(items: Item[]): Result[] {
console.time('processItems');
const result = items.map(transform);
console.timeEnd('processItems');
return result;
}
For browser CPU profiling, open Chrome DevTools > Performance tab > Record while
reproducing the slow interaction. Look for long tasks (>50ms) in the flame chart.
Debug memory leaks
Capture two heap snapshots - one before and one after a suspected leak window - then
compare retained objects:
import { writeHeapSnapshot } from'v8';
import { setInterval } from'timers';
// Snapshot 1: baselinewriteHeapSnapshot(); // writes Heap-<pid>-<seq>.heapsnapshot// Simulate load / time passingawaitrunWorkload();
// Snapshot 2: after suspected leakwriteHeapSnapshot();
// Load both files in Chrome DevTools > Memory > Compare snapshots
Avoid closure-based leaks by using WeakRef and FinalizationRegistry for
optional references that should not prevent GC:
Common leak sources: event listeners never removed, global maps/sets that grow forever,
closures capturing large objects, and timers/intervals not cleared.
Benchmark code
Proper microbenchmarking requires warmup to let V8 JIT compile, multiple iterations
to reduce noise, and statistical comparison:
importBenchmarkfrom'benchmark';
const suite = newBenchmark.Suite();
suite
.add('Array.from', () => {
Array.from({ length: 1000 }, (_, i) => i * 2);
})
.add('for loop', () => {
constarr: number[] = newArray(1000);
for (let i = 0; i < 1000; i++) arr[i] = i * 2;
})
.on('cycle', (event: Benchmark.Event) => {
console.log(String(event.target));
})
.on('complete', function (this: Benchmark.Suite) {
console.log('Fastest: ' + this.filter('fastest').map('name'));
})
.run({ async: true });
Rules for valid microbenchmarks:
Warmup at least 3 iterations before measuring
Run for at least 1 second per case to smooth JIT variance
Prevent dead-code elimination - consume the result
Test with realistic input size and shape
Optimize Node.js event loop
Detect blocking with clinic bubbleprof or manual measurement:
importDataLoaderfrom'dataloader';
// Without DataLoader: 1 query per user = N+1// With DataLoader: batches into 1 query per tickconst userLoader = newDataLoader(async (ids: readonlystring[]) => {
const users = await db.users.findMany({ where: { id: { in: [...ids] } } });
const map = newMap(users.map((u) => [u.id, u]));
return ids.map((id) => map.get(id) ?? null);
});
// Each call is automatically batchedconst user = await userLoader.load(userId);
Use connection pooling and avoid pool exhaustion:
import { Pool } from'pg';
const pool = newPool({
max: 20, // max connections - tune to (2 * CPU cores + 1) as starting pointidleTimeoutMillis: 30_000,
connectionTimeoutMillis: 2_000,
});
// Always release connections - use try/finallyconst client = await pool.connect();
try {
const result = await client.query('SELECT ...', [params]);
return result.rows;
} finally {
client.release(); // critical - never omit
}
Anti-patterns / common mistakes
Mistake
Why it's wrong
What to do instead
Optimizing without profiling
Fixes the wrong thing; wastes time; may degrade perf elsewhere
Profile first, let data identify the bottleneck
Benchmarking without warmup
V8 JIT hasn't compiled the hot path; results are misleading
Run 3+ warmup iterations before measuring
Using averages instead of percentiles
Hides tail latency that real users experience
Report P50, P95, P99 together
Caching everything eagerly
Stale data, unbounded memory growth, invalidation nightmares
Cache only measured hot reads; define TTL and invalidation upfront
Blocking the event loop with sync I/O
Freezes all concurrent requests for the duration
Use async fs/net APIs; move CPU work to worker threads
Measuring in development, deploying to production
V8 opts, GC pressure, and concurrency behave differently in prod
Profile under production-like load with production build
Gotchas
Microbenchmarks without preventing dead-code elimination produce meaningless results - V8 will optimize away computations whose results are never used. A benchmark that calls computeResult() without consuming the return value may be measuring near-zero work. Always store the result in a variable and use it (e.g., sum += result) so the compiler cannot eliminate the hot path.
Connection pool exhaustion masquerades as slow queries - If all DB connections are in use, new queries queue behind them and appear in traces as 500ms+ "database time" when the query itself takes 5ms. Check pool.totalCount, pool.idleCount, and pool.waitingCount before optimizing queries. Pool exhaustion often looks like slow DB, not like a pool problem.
Profiling in development produces unrepresentative results - V8 optimizes differently in development (no minification, source maps active, NODE_ENV=development guards enabled). Profiling a dev build and optimizing based on that output can be entirely misleading. Always profile against a production build with production environment variables and realistic data volume.
Heap snapshots taken during GC produce inflated retained sizes - If you trigger a heap snapshot during a GC cycle, the snapshot may show objects that are already queued for collection but not yet freed. Compare two snapshots taken at the same phase of your workload (e.g., both after processing 100 requests) to get valid comparisons.
Worker threads do not share memory by default - serialization overhead can exceed compute savings - Offloading a task to a worker thread requires serializing input data (via postMessage) and deserializing results back. For tasks involving large objects, this serialization cost can exceed the compute benefit. Use SharedArrayBuffer for large data payloads that need to cross the worker boundary frequently.
References
Load the relevant reference file only when the current task requires it:
references/profiling-tools.md - Node.js profiler, Chrome DevTools, Lighthouse,
clinic.js, 0x, and how to choose between them
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
