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Agent Skill · 研究检索

performance-optimization(性能优化)

performance-optimization 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

performance-optimization 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 3,213。

总安装 46,968周安装 1,881GitHub Stars 25,909下载 14,744核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:performance-optimization(性能优化)
来源仓库:https://github.com/addyosmani/agent-skills
仓库路径:skills/performance-optimization
安装命令:
npx skills add https://github.com/addyosmani/agent-skills --skill performance-optimization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/addyosmani/agent-skills --skill performance-optimization

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

强调测量优先原则,避免无依据的性能优化行为。适用于存在明确性能指标或用户体验下降问题的开发场景。提供 Core Web Vitals 目标值参考,倡导基于 profiling 定位真实瓶颈再实施优化。不建议在未发现问题前进行复杂改动,以免引入额外维护成本。

performance-optimization 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/performance-optimization

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/addyosmani/agent-skills --skill performance-op…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

performance-optimization 怎么安装?

在终端执行 npx skills add https://github.com/addyosmani/agent-skills --skill performance-optimization。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

performance-optimization 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

performance-optimization 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

performance-optimization 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

performance-optimization 用的人多吗?

公开数据是累计安装 46,968 次,近一周安装 1,881 次,下载 14,744 次,GitHub 25,909 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

performance-optimization 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 25,909 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
performance-optimization

性能优化 · 本页

46,96825,909调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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, confirm improvement
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:

# Synthetic: Lighthouse in Chrome DevTools (or CI)
# Chrome DevTools → Performance tab → Record
# Chrome DevTools MCP → 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');

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)
// 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 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)
// 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
// 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)
// 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

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:

# 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.

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)

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)

平台分布

Codex

36.11%
按下载量换算5,324

Claude

25.83%
按下载量换算3,808

Cursor

19.02%
按下载量换算2,804

Gemini CLI

9.25%
按下载量换算1,364

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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