Performance
midudev/100cosas.dev
Optimize web performance for faster loading and better user experience.
Optimizes application performance across frontend, backend, queries, and databases.
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install dzhalaevd/Donatello performance-optimization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/dzhalaevd/Donatello.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/performance-optimization .claude/skills/performance-optimization && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "performance-optimization" agent skill from https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimization into .claude/skills/performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-optimization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimizationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install dzhalaevd/Donatello performance-optimization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhalaevd/Donatello.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/performance-optimization .agents/skills/performance-optimization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "performance-optimization" agent skill from https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimization into .agents/skills/performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-optimization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install dzhalaevd/Donatello performance-optimization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhalaevd/Donatello.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/performance-optimization .cursor/skills/performance-optimization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "performance-optimization" agent skill from https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimization into .cursor/skills/performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-optimization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/dzhalaevd/Donatello.git --path .agents/skills/performance-optimization--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install dzhalaevd/Donatello performance-optimization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhalaevd/Donatello.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/performance-optimization .gemini/skills/performance-optimization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "performance-optimization" agent skill from https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimization into .gemini/skills/performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-optimization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install dzhalaevd/Donatello performance-optimizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/dzhalaevd/Donatello.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/performance-optimization .github/skills/performance-optimization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "performance-optimization" agent skill from https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimization into .github/skills/performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-optimization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install dzhalaevd/Donatello performance-optimization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dzhalaevd/Donatello.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/performance-optimization .opencode/skills/performance-optimization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "performance-optimization" agent skill from https://github.com/dzhalaevd/Donatello/tree/main/.agents/skills/performance-optimization into .opencode/skills/performance-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performance-optimization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
performance-optimizationOptimizes application performance across frontend, backend, queries, and databases.
Performance Optimization is an agent skill from dzhalaevd/Donatello. Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Frontend & Design, covering Web performance and Performance optimization. The repository describes itself as: Make Dating Great Again. An open source dating platform. The licence is Apache-2.0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit b57816e. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
npxlighthouseFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Performance Optimization loads about 3.7k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 1,085 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from dzhalaevd/Donatello at commit b57816e, republished under its Apache-2.0 licence (© dzhalaevd). 1,085 words, ~3,740 tokens.
.claude/skills/performance-optimization/SKILL.md (or your agent's skills folder).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 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.
| 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 |
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; keep or revert
5. GUARD → Add monitoring or tests to prevent regressionTwo complementary approaches — use both:
Frontend:
# Synthetic: Lighthouse in Chrome DevTools (or CI)
# Chrome DevTools → Performance tab → Record
# Available browser tooling → Performance trace
# 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');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 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 depsCommon 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 |
// 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 },
});// 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' },
});<!-- 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"
/>// 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>;
}// 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>
);
}// 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 minutesA fix is a hypothesis until you re-measure. This step decides whether it survives.
Re-measure the way you measured the baseline: same command, same conditions, same fixed budget (wall-clock, sample count, or request count). A baseline taken on a cold cache against a result taken on a warm one measures the cache, not your change.
Change one thing at a time. Three optimizations landed together produce one number, and you cannot attribute it. If they must ship together, measure each in isolation first.
Beat the noise, not just the mean. Repeat the measurement and compare the delta against run-to-run variance. A 3% gain inside ±5% variance is not a gain; it is a different sample.
Then decide, strictly:
| Result vs. baseline | Action |
|---|---|
| Past the threshold, tests green | Keep. Commit with the before/after numbers in the message. |
| Within noise (no measurable change) | Revert. |
| Worse | Revert. |
| Improved, but a test went red | Revert. A regression wearing a win's clothing. |
"Neutral" is a revert, not a keep. This is the step teams skip: the change is already written, throwing it away feels wasteful, so it lands unmeasured, and the codebase accretes complexity that never bought anything. Code you keep, you maintain forever. Make it pay for itself.
Correctness gates the metric. The suite stays green and the number moves. An "optimization" that wins by dropping work the product needed (skipping a validation, caching something that must be fresh, removing an await that was load-bearing) is a regression, not a win.
Reverted work leaves no trace in git history, which is exactly why the same dead idea gets tried again next quarter. Keep a short ledger so a discarded idea stays discarded:
| Idea | Baseline → Result | Verdict | Why |
|---|---|---|---|
| Memoize the row component | INP 240ms → 235ms | reverted | Inside noise (±15ms). Rows weren't the bottleneck. |
| Virtualize the list | INP 240ms → 90ms | kept | Long tasks gone from the trace. |
| Preconnect to the API origin | LCP 2.8s → 2.8s | reverted | Already same-origin. |
A section in the PR description or a PERF.md in the repo both work. What matters is that the next person (or the next agent) reads it before proposing an experiment, and doesn't re-run one that already failed.
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 autorunFor detailed performance checklists, optimization commands, and anti-pattern reference, see references/performance-checklist.md.
| 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. |
| "It didn't help much, but it doesn't hurt" | Neutral changes are a revert. You pay maintenance on them forever and got nothing back. |
| "We already wrote it, may as well keep it" | Sunk cost. The measurement doesn't care how long the change took to write. |
| "The improvement is obvious, no need to re-measure" | Then re-measuring is cheap and proves it. Unmeasured wins are how neutral complexity lands. |
React.memo and useMemo everywhere (overusing is as bad as underusing)After any performance-related change:
© dzhalaevd, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/performance-optimization of dzhalaevd/Donatello.
Open the folder on GitHubat commit b57816e
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in dzhalaevd/Donatello, which our catalogue first saw on October 7, 2026.
Performance Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Performance Optimization this skilldzhalaevd/Donatello | 135 | 2 repos | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| Performancemidudev/100cosas.dev | 114 | 6 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Web Performancemozilla/firefox-devtools-mcp | 470 | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Performance ProfilingxenitV1/Antigravity-Workflows | 130 | 8 repos | ~772 | Automated safety check: Notes | MIT | |
| Optimizing PerformanceCloudAI-X/claude-workflow-v2 | 1.4k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Performance Optimizationsanity-io/sanity | 6.4k | 7 repos | ~3.1k | Automated safety check: Pass | MIT |
midudev/100cosas.dev
Optimize web performance for faster loading and better user experience.
mozilla/firefox-devtools-mcp
Find and fix why a website is slow by capturing and analyzing a Firefox performance profile, or by analyzing a profile the user already has (a saved file or a profiler.firefox.com share link).
xenitV1/Antigravity-Workflows
Performance profiling principles. An agent skill from xenitV1/Antigravity-Workflows.
CloudAI-X/claude-workflow-v2
Analyzes and optimizes application performance across frontend, backend, and database layers.
sanity-io/sanity
Optimizes application performance. An agent skill from sanity-io/sanity.
textura-agency/next16-claude-starter
Get a page into Lighthouse's green zone on desktop and mobile, for people AND for the robot form crawlers get — build it, audit all four categories (Performance, Accessibility, Best Practices, SEO)…
dzhalaevd/Donatello
Guides stable API and interface design. An agent skill from dzhalaevd/Donatello.
dzhalaevd/Donatello
Records decisions and documentation. An agent skill from dzhalaevd/Donatello.
dzhalaevd/Donatello
Automates CI/CD pipeline setup. An agent skill from dzhalaevd/Donatello.
dzhalaevd/Donatello
Manages deprecation and migration. An agent skill from dzhalaevd/Donatello.
dzhalaevd/Donatello
Instruments code so production behavior is visible and diagnosable.
dzhalaevd/Donatello
Prepares production launches. An agent skill from dzhalaevd/Donatello.
Categories
Optimizes application performance across frontend, backend, queries, and databases. Performance Optimization is an agent skill from dzhalaevd/Donatello. Optimizes application performance across frontend, backend, queries, and databases.
Performance Optimization fits situations like: performance requirements exist; you suspect performance regressions; core Web Vitals; load times need improvement.
Run `npx skills add dzhalaevd/Donatello --skill performance-optimization -a claude-code`. Or copy the skill folder (.agents/skills/performance-optimization in dzhalaevd/Donatello) into .claude/skills/performance-optimization in your project. Claude Code loads it when a task matches its description.
Run `npx skills add dzhalaevd/Donatello --skill performance-optimization -a codex`. Or copy the skill folder (.agents/skills/performance-optimization in dzhalaevd/Donatello) into .agents/skills/performance-optimization in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add dzhalaevd/Donatello --skill performance-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performance-optimization, .gemini/skills/performance-optimization, .github/skills/performance-optimization and .opencode/skills/performance-optimization in your project.
Going by SKILL.md and its folder, Performance Optimization needs the command-line tools its instructions call (npx and lighthouse).
SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Performance Optimization is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Performance Optimization: Performance (midudev/100cosas.dev, 114 stars), Web Performance (mozilla/firefox-devtools-mcp, 470 stars), Performance Profiling (xenitV1/Antigravity-Workflows, 130 stars) and Optimizing Performance (CloudAI-X/claude-workflow-v2, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
dzhalaevd (a GitHub user) maintains it in dzhalaevd/Donatello, which has 135 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on October 3, 2026.
Source: dzhalaevd/Donatello on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.