Agent skill

Performance Optimization

by dzhalaevd in dzhalaevd/Donatello

Optimizes application performance across frontend, backend, queries, and databases.

Apache-2.0Auto-check passedFrontend & Design

Install Performance Optimization

skills CLI
$ npx skills add dzhalaevd/Donatello --skill performance-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install dzhalaevd/Donatello performance-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
performance-optimization
GitHub stars
135
Used in
2 other repos
Token cost
~3.7k tokens
SKILL.md length
1,085 words
Files
1
Skills in repo
15
Repo updated
First seen
Licence
Apache-2.0

At a glance

Optimizes application performance across frontend, backend, queries, and databases.

  • Works in 4 steps: Measure → Identify the Bottleneck → Fix Common Anti-Patterns → …
  • Performance requirements exist
  • SKILL.md covers Overview, When to Use, Core Web Vitals Targets and The Optimization Workflow, plus 5 more sections
  • Calls npx and lighthouse

What it does

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.

When your agent uses it

  • Performance requirements exist
  • You suspect performance regressions
  • Core Web Vitals
  • Load times need improvement

Example prompts

  • “Use the performance-optimization skill to optimiz application performance across frontend, backend, queries, and databases”
  • “/performance-optimization”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Measure
  2. Identify the Bottleneck
  3. Fix Common Anti-Patterns
  4. Verify (Keep or Revert)

What it can do on your machine

Read from SKILL.md and the folder at commit b57816e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • npx
    • lighthouse

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    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.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from dzhalaevd/Donatello at commit b57816e, republished under its Apache-2.0 licence (© dzhalaevd). 1,085 words, ~3,740 tokens.

Download SKILL.mdSave it as .claude/skills/performance-optimization/SKILL.md (or your agent's skills folder).
name
performance-optimization
description
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.

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; keep or revert
5. GUARD    → Add monitoring or tests to prevent regression
Step 1: Measure

Two complementary approaches — use both:

  • Synthetic (Lighthouse, 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. Required to validate that a fix actually improved user experience.

Frontend:

bash
# 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:

bash
# 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 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
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)
typescript
// 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
typescript
// 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)
html
<!-- 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)
tsx
// 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
typescript
// 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)
typescript
// 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
Step 4: Verify (Keep or Revert)

A 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. baselineAction
Past the threshold, tests greenKeep. Commit with the before/after numbers in the message.
Within noise (no measurable change)Revert.
WorseRevert.
Improved, but a test went redRevert. 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.

Show full SKILL.md (484 more words)Show less
Log every attempt, including the reverted ones

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:

IdeaBaseline → ResultVerdictWhy
Memoize the row componentINP 240ms → 235msrevertedInside noise (±15ms). Rows weren't the bottleneck.
Virtualize the listINP 240ms → 90mskeptLong tasks gone from the trace.
Preconnect to the API originLCP 2.8s → 2.8srevertedAlready 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.

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:

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

# Lighthouse CI
npx lhci autorun

See Also

For detailed performance checklists, optimization commands, and anti-pattern reference, see references/performance-checklist.md.

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 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.

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.memo and useMemo everywhere (overusing is as bad as underusing)
  • Optimizations kept without a re-measurement that justifies them
  • Several optimizations bundled into one measurement, so no single change can be attributed
  • A "win" that required a test to be changed, skipped, or deleted
  • The same failed optimization attempted more than once because nobody recorded the first attempt

Verification

After any performance-related change:

  • Before and after measurements exist (specific numbers)
  • The result was re-measured the same way as the baseline (same command, same conditions)
  • The improvement exceeds run-to-run variance, not just the mean
  • Changes that didn't beat the baseline were reverted, not kept as neutral
  • Attempts are logged, kept and reverted alike, so a dead idea isn't re-run
  • 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)

© 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

Files

Just SKILL.md in .agents/skills/performance-optimization of dzhalaevd/Donatello.

Open the folder on GitHubat commit b57816e

Used in 2 other repositories

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.

Compare with similar skills

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.

Performance Optimization compared with similar skills
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Performance Optimization this skilldzhalaevd/Donatello1352 repos~3.7kAutomated safety check: PassApache-2.0
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Web Performancemozilla/firefox-devtools-mcp470—~1.2kAutomated safety check: PassCustom licence
Performance ProfilingxenitV1/Antigravity-Workflows1308 repos~772Automated safety check: NotesMIT
Optimizing PerformanceCloudAI-X/claude-workflow-v21.4k1 repos~1.5kAutomated safety check: PassMIT
Performance Optimizationsanity-io/sanity6.4k7 repos~3.1kAutomated safety check: PassMIT

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Questions about Performance Optimization

What does Performance Optimization do?

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.

When should I use Performance Optimization?

Performance Optimization fits situations like: performance requirements exist; you suspect performance regressions; core Web Vitals; load times need improvement.

How do I install Performance Optimization in Claude Code?

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.

How do I install Performance Optimization in Codex?

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.

Can I use Performance Optimization in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Performance Optimization need to run?

Going by SKILL.md and its folder, Performance Optimization needs the command-line tools its instructions call (npx and lighthouse).

Does Performance Optimization access the network?

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.

Is Performance Optimization safe to install?

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.

What licence does Performance Optimization use?

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.

How many tokens does Performance Optimization use?

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.

What are the alternatives to Performance Optimization?

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.

Who maintains Performance Optimization?

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.