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performance性能

Agent Skill

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。

总安装

3,648

周安装

149

GitHub Stars

160

下载量

1,168
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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