performance-optimization
Skill5 starsChanged 12 days agoMITView on GitHub
Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.
125 other projects use a version of this, 61 of them edited.
Adopted by
Most recently taken up by
- valentinesamuel/claude-skillstheir own version, first seen Nov 8, 2026
- ishandutta2007/Awesome-Claude-Skillstheir own version, first seen Nov 8, 2026
- ishandutta2007/Awesome-Agent-Skillsedited, first seen Nov 8, 2026
- paramcodes/skillsedited, first seen Nov 8, 2026
- nisarth/claude-skills-storetheir own version, first seen Nov 8, 2026
- DnsQueen/orcinus-skillsedited, first seen Nov 8, 2026
Used with
Projects with it also use constraint-driven-development, input-systems and 1 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
MrCipherSmithSkillconstraint-driven-development
Use when a project needs a persistent, measurable quality bar — starting a project without documented standards, 'set up constraints', or when coverage/perf/a11y thresholds get re-debated per-PR. Produces CONSTRAINTS.md with enforceable numbers, ratchets, and a guard against silent weakening. NOT for throwaway prototypes or a one-off CI setup.
Also in 15 projects that use this
ukanwatSkillinput-systems
Architect game input — action mapping (abstracting keys into named actions), rebinding with conflict detection and persistence, multi-device support (keyboard, gamepad, touch), analog deadzones, and feel features like input buffering and coyote time, plus accessibility. Engine-neutral. Use when the user mentions input mapping, rebind controls, gamepad support, deadzone, input buffering, coyote time, or accessible controls.
Also in 4 projects that use this
HK-hubSkillobservability-and-instrumentation
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
Also in 31 projects that use this
Other versions125 projects use a version of this.
Word for word · 0
None found.
Edited · 61
- addyosmani/agent-skills★ 114k30% same
- sanity-io/sanity★ 6.4k75% same
- compozy/compozy★ 2.9k30% same
- ollaya-dev/ollaya★ 1.4k33% same
- shashankswe2020-ux/whoop-mcp★ 17475% same
- vinvcn/addyosmani-agent-skills-zh★ 4621% same
- jmxt3/gitscape.ai★ 3314% same
- sampleXbro/agentsmesh★ 2775% same
- jaskirat1616/grok-skills★ 2233% same
- ishandutta2007/Awesome-Agent-Skills★ 2175% same
51 more not listed
Wrote their own version · 64
- gamedev-skills/awesome-gamedev-agent-skills★ 6.4ksame name
- buddy-compiler/buddy-mlir★ 773same name
- ThibautBaissac/rails_ai_agents★ 665same name
- derHaken/SuperAntigravity★ 439same name
- ukanwat/aaabench★ 399same name
- ukanwat/overtime★ 399same name
- ukanwat/selfstarter★ 399same name
- ryanthedev/code-foundations★ 381same name
- Mindrally/skills★ 275same name
- hmislk/hmis★ 244same name
54 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository119 other files it gives its agents.
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- templates/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 110 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
When NOT to use: Don't optimize before you have evidence of a problem.
- Distinctive: line 70
Use the symptom to decide what to measure first:
- Distinctive: line 268
No performance monitoring in production
- Distinctive: line 273
After any performance-related change:
“Common” is measured: kept by at least 6 in 10 of the 110 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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
| Metric | Good | Needs Improvement | Poor |
| 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 regressionStep 1: Measure
Frontend:
# Lighthouse in Chrome DevTools (or CI)
# Chrome DevTools → Performance tab → Record
# Chrome DevTools MCP → Performance trace
# 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, check API/database
│ └── 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 depsStep 2: Identify the Bottleneck
Common bottlenecks by category:
Frontend:
| Symptom | Likely Cause | Investigation |
| Slow LCP | Large images, render-blocking resources, slow server | Check network waterfall, image sizes |
| High CLS | Images without dimensions, late-loading content, font shifts | Check layout shift attribution |
| Poor INP | Heavy JavaScript on main thread, large DOM updates | Check long tasks in Performance trace |
| Slow initial load | Large bundle, many network requests | Check bundle size, code splitting |
Backend:
| Symptom | Likely Cause | Investigation |
| Slow API responses | N+1 queries, missing indexes, unoptimized queries | Check database query log |
| Memory growth | Leaked references, unbounded caches, large payloads | Heap snapshot analysis |
| CPU spikes | Synchronous heavy computation, regex backtracking | CPU profiling |
| High latency | Missing caching, redundant computation, network hops | Trace 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 lazy loading, no responsive sizes -->
<img src="/hero.jpg" />
<!-- GOOD: Responsive, lazy-loaded, properly sized -->
<img
src="/hero.jpg"
srcset="/hero-400.webp 400w, /hero-800.webp 800w, /hero-1200.webp 1200w"
sizes="(max-width: 768px) 100vw, 50vw"
width="1200"
height="600"
loading="lazy"
alt="Hero 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
// BAD: Importing entire library
import { format } from 'date-fns';
// GOOD: Tree-shakable import (if the library supports it)
import { format } from 'date-fns/format';
// GOOD: Dynamic import for heavy, rarely-used features
const ChartLibrary = lazy(() => import('./ChartLibrary'));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 minutesPerformance 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: ≥ 90Enforce in CI:
# Bundle size check
npx bundlesize --config bundlesize.config.json
# Lighthouse CI
npx lhci autorunCommon Rationalizations
| Rationalization | Reality |
| "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 100ms" | Research shows 100ms delays impact conversion rates. Users notice more than you think. |
| "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
- 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.memoanduseMemoeverywhere (overusing is as bad as underusing)
Verification
After any performance-related change:
- Before and after measurements exist (specific numbers)
- The specific bottleneck is identified and addressed
- Core Web Vitals are within "Good" thresholds
- Bundle size hasn't increased significantly
- No N+1 queries in new data fetching code
- Performance budget passes in CI (if configured)
- Existing tests still pass (optimization didn't break behavior)
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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