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Agent Skill · 研究检索

performance(性能)

performance 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 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,权限为操作浏览器;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 26,016。

总安装 3,648周安装 149GitHub Stars 160下载 1,168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(性能)
来源仓库:https://github.com/yonatangross/orchestkit
仓库路径:skills/performance
安装命令:
npx skills add https://github.com/yonatangross/orchestkit --skill performance
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/yonatangross/orchestkit --skill performance

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。

performance 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。通过 npx skills add 命令从 GitHub 仓库安装使用。安装前需确认权限范围、维护状态,以及是否触发联网或文件读写操作。

建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/performance

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/yonatangross/orchestkit --skill performance;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

    它需要操作浏览器,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 怎么安装?

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

performance 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 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

performance 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 用的人多吗?

公开数据是累计安装 3,648 次,近一周安装 149 次,下载 1,168 次,GitHub 160 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 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。

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

性能 · 本页

3,648160操作浏览器未明确通过

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

Comprehensive performance optimization patterns for frontend, backend, and LLM inference.

Quick Reference

CategoryRulesImpactWhen to Use
Core Web Vitals4CRITICALLCP, INP, CLS optimization with 2026 thresholds
Render Optimization3HIGHReact Compiler, memoization, virtualization
Lazy Loading3HIGHCode splitting, route splitting, preloading
Image Optimization3HIGHNext.js Image, AVIF/WebP, responsive images
Profiling & Backend3MEDIUMReact DevTools, py-spy, bundle analysis
LLM Inference3MEDIUMvLLM, quantization, speculative decoding
Caching2HIGHRedis cache-aside, prompt caching, HTTP cache headers
Query & Data Fetching2HIGHTanStack Query prefetching, optimistic updates, rollback
Sustainability1MEDIUMPage weight budgets, lazy loading, optimized formats, dark mode

Total: 24 rules across 9 categories

Core Web Vitals

Google's Core Web Vitals with 2026 stricter thresholds.

RuleFileKey Pattern
LCP Optimizationrules/cwv-lcp.mdPreload hero, SSR, fetchpriority="high"
INP Optimizationrules/cwv-inp.mdscheduler.yield, useTransition, requestIdleCallback
INP Advancedrules/cwv-inp-advanced.mdLayout thrashing, third-party scripts, rAF patterns
CLS Preventionrules/cwv-cls.mdExplicit dimensions, aspect-ratio, font-display

2026 Thresholds

MetricCurrent Good2026 Good
LCP<= 2.5s<= 2.0s
INP<= 200ms<= 150ms
CLS<= 0.1<= 0.08

Render Optimization

React render performance patterns for React 19+.

RuleFileKey Pattern
React Compilerrules/render-compiler.mdAuto-memoization, "Memo" badge verification
Manual Memoizationrules/render-memo.mduseMemo/useCallback escape hatches, state colocation
Virtualizationrules/render-virtual.mdTanStack Virtual for 100+ item lists

Lazy Loading

Code splitting and lazy loading with React.lazy and Suspense.

RuleFileKey Pattern
React.lazy + Suspenserules/loading-lazy.mdComponent lazy loading, error boundaries
Route Splittingrules/loading-splitting.mdReact Router 7.x, Vite manual chunks
Preloadingrules/loading-preload.mdPrefetch on hover, modulepreload hints

Image Optimization

Production image optimization for modern web applications.

RuleFileKey Pattern
Next.js Imagerules/images-nextjs.mdImage component, priority, blur placeholder
Format Selectionrules/images-formats.mdAVIF/WebP, quality 75-85, picture element
Responsive Imagesrules/images-responsive.mdsizes prop, art direction, CDN loaders

Profiling & Backend

Profiling tools and backend optimization patterns.

RuleFileKey Pattern
React Profilingrules/profiling-react.mdDevTools Profiler, flamegraph, render counts
Backend Profilingrules/profiling-backend.mdpy-spy, cProfile, memory_profiler, flame graphs
Bundle Analysisrules/profiling-bundle.mdvite-bundle-visualizer, tree shaking, performance budgets

LLM Inference

High-performance LLM inference with vLLM, quantization, and speculative decoding.

RuleFileKey Pattern
vLLM Deploymentrules/inference-vllm.mdPagedAttention, continuous batching, tensor parallelism
Quantizationrules/inference-quantization.mdAWQ, GPTQ, FP8, INT8 method selection
Speculative Decodingrules/inference-speculative.mdN-gram, draft model, 1.5-2.5x throughput

Caching

Backend Redis caching and LLM prompt caching for cost savings and performance.

RuleFileKey Pattern
Redis & Backendrules/caching-redis.mdCache-aside, write-through, invalidation, stampede prevention
HTTP & Promptrules/caching-http.mdHTTP cache headers, LLM prompt caching, semantic caching

Query & Data Fetching

TanStack Query v5 patterns for prefetching and optimistic updates.

RuleFileKey Pattern
Prefetchingrules/query-prefetching.mdHover prefetch, route loaders, queryOptions, Suspense
Optimistic Updatesrules/query-optimistic.mdOptimistic mutations, rollback, cache invalidation

Sustainability

Digital sustainability patterns for reducing carbon footprint and energy usage.

RuleFileKey Pattern
Sustainability UXrules/sustainability-ux.mdPage weight budgets, AVIF/WebP, lazy loading, dark mode

Local Profiling Target

When profiling a local app (Lighthouse, Core Web Vitals, bundle analysis), use Portless named URLs for stable, self-documenting targets:

# Discover services
portless list
# app → app.localhost:1355 (port 3000)

# Profile with agent-browser (preferred for visual metrics)
agent-browser open "http://app.localhost:1355"
agent-browser profiler start
agent-browser wait --load networkidle
agent-browser profiler stop /tmp/profile.json

# Lighthouse via agent-browser
agent-browser open "http://app.localhost:1355"
agent-browser screenshot /tmp/perf-baseline.png

# Or direct Lighthouse CLI
npx lighthouse http://app.localhost:1355 --output=json --output-path=/tmp/lighthouse.json

Named URLs are stable across restarts and self-documenting in performance reports. Install Portless with npm i -g portless.

Quick Start Example

// LCP: Priority hero image with SSR
import Image from 'next/image';

export default async function Page() {
  const data = await fetchHeroData();
  return (
    <Image
      src={data.heroImage}
      alt="Hero"
      priority
      placeholder="blur"
      sizes="100vw"
      fill
    />
  );
}

Key Decisions

DecisionRecommendation
MemoizationLet React Compiler handle it (2026 default)
Lists 100+ itemsUse TanStack Virtual
Image formatAVIF with WebP fallback (30-50% smaller)
LCP contentSSR/SSG, never client-side fetch
Code splittingPer-route for most apps, per-component for heavy widgets
Prefetch strategyOn hover for nav links, viewport for content
QuantizationAWQ for 4-bit, FP8 for H100/H200
Bundle budgetHard fail in CI to prevent regression

Common Mistakes

  1. Client-side fetching LCP content (delays render)
  2. Images without explicit dimensions (causes CLS)
  3. Lazy loading LCP images (delays largest paint)
  4. Heavy computation in event handlers (blocks INP)
  5. Layout-shifting animations (use transform instead)
  6. Lazy loading tiny components < 5KB (overhead > savings)
  7. Missing error boundaries on lazy components
  8. Using GPTQ without calibration data
  9. Not benchmarking actual workload patterns
  10. Only measuring in lab environment (need RUM)

Related Skills

  • ork:react-server-components-framework - Server-first rendering
  • ork:vite-advanced - Build optimization
  • browser-tools - Visual profiling with agent-browser + Portless
  • caching - Cache strategies for responses
  • ork:monitoring-observability - Production monitoring and alerting
  • ork:database-patterns - Query and index optimization
  • ork:llm-integration - Local inference with Ollama

Capability Details

lcp-optimization

Keywords: LCP, largest-contentful-paint, hero, preload, priority, SSR Solves:

  • Optimize hero image loading
  • Server-render critical content
  • Preload and prioritize LCP resources

inp-optimization

Keywords: INP, interaction, responsiveness, long-task, transition, yield Solves:

  • Break up long tasks with scheduler.yield
  • Defer non-urgent updates with useTransition
  • Optimize event handler performance

cls-prevention

Keywords: CLS, layout-shift, dimensions, aspect-ratio, font-display Solves:

  • Reserve space for dynamic content
  • Prevent font flash and image pop-in
  • Use transform for animations

react-compiler

Keywords: react-compiler, auto-memo, memoization, React 19 Solves:

  • Enable automatic memoization
  • Identify when manual memoization needed
  • Verify compiler is working

virtualization

Keywords: virtual, TanStack, large-list, scroll, overscan Solves:

  • Render 100+ item lists efficiently
  • Dynamic height virtualization
  • Window scrolling patterns

lazy-loading

Keywords: React.lazy, Suspense, code-splitting, dynamic-import Solves:

  • Route-based code splitting
  • Component lazy loading with error boundaries
  • Prefetch on hover and viewport

image-optimization

Keywords: next/image, AVIF, WebP, responsive, blur-placeholder Solves:

  • Next.js Image component patterns
  • Format selection and quality settings
  • Responsive sizing and CDN configuration

profiling

Keywords: profiler, flame-graph, py-spy, DevTools, bundle-analyzer Solves:

  • Profile React renders and backend code
  • Generate and interpret flame graphs
  • Analyze and optimize bundle size

inp-advanced

Keywords: INP, scheduler-yield, layout-thrashing, third-party-scripts, requestAnimationFrame Solves:

  • Break long tasks with scheduler.yield()
  • Audit and defer blocking third-party scripts
  • Avoid synchronous layout thrashing in event handlers
  • Optimize form submissions, dropdowns, accordions, filters

sustainability

Keywords: sustainability, carbon-footprint, page-weight, green-ux, dark-mode, lazy-loading Solves:

  • Enforce page weight budgets (< 1MB)
  • Eliminate auto-playing videos and heavy decorative animations
  • Serve optimized image formats (AVIF/WebP)
  • Implement cursor-based pagination to prevent over-fetching

llm-inference

Keywords: vllm, quantization, speculative-decoding, inference, throughput Solves:

  • Deploy LLMs with vLLM for production
  • Choose quantization method for hardware
  • Accelerate generation with speculative decoding

References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

FileContent
rum-setup.mdReal User Monitoring
react-compiler-migration.mdCompiler adoption
tanstack-virtual-patterns.mdVirtualization patterns
vllm-deployment.mdProduction vLLM config
quantization-guide.mdMethod comparison
cdn-setup.mdImage CDN configuration
cc-prompt-cache-guide.mdCC 2.1.72 prompt cache optimization, stable-first prompt structure

平台分布

Codex

33.37%
按下载量换算390

Claude

33.23%
按下载量换算388

Cursor

21.01%
按下载量换算245

Gemini CLI

8.88%
按下载量换算104

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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