MCPInstall

performance

Performance optimization patterns covering Core Web Vitals, React render optimization, lazy loading, image optimization, backend profiling, LLM inference, and sustainability UX. Use when improving page speed, debugging slow renders, optimizing bundles, reducing image payload, profiling backend, deploying LLMs efficiently, or reducing digital carbon footprint.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/yonatangross/orchestkit --skill performance
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads performance automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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 Optimization2HIGHAVIF/WebP formats, 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: 23 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
Format Selectionrules/images-formats.mdAVIF/WebP, quality 75-85, picture element
Responsive Imagesrules/images-responsive.mdsizes prop, art direction, CDN loaders

Next.js Image component usage and the v16 image config defaults are first-party territory; see "Upstream coverage (do not restate)" below.

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 (port 3000)

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

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

# Or direct Lighthouse CLI
npx lighthouse https://app.localhost --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

Upstream coverage (do not restate)

These topics used to be restated in this skill's references, checklists, and examples. They are owned by first-party sources now; consult those instead of re-adding tutorials here. The ork-specific floors and scars that survived the cut live in references/ork-delta.md.

TopicFirst-party source
Core Web Vitals mechanics, audit checklists, before/after examplesskill: web-perf / cloudflare:web-perf (Chrome DevTools MCP); https://web.dev/vitals/
Real User Monitoring with the web-vitals libraryskill: web-perf; https://github.com/GoogleChrome/web-vitals
Next.js Image component, v16 image config defaults, image CDN loadersskills: vercel:nextjs + vercel:next-upgrade; https://nextjs.org/docs/app/api-reference/components/image
Image format selection and optimization checklistsskill: vercel:nextjs; https://web.dev/learn/images
React Compiler migration, memoization escape hatches, state colocationskill: vercel-react-best-practices; https://react.dev/learn/react-compiler
React DevTools Profiler workflow, render auditsskill: vercel-react-best-practices; https://react.dev/reference/react/Profiler
TanStack Virtual list/grid virtualization patternshttps://tanstack.com/virtual/latest/docs/introduction
Route-based code splitting (React Router, Vite manual chunks)https://reactrouter.com/ and https://vite.dev/guide/build
Generic profiling workflows (Lighthouse, py-spy, bundle analyzers)skill: web-perf; https://github.com/benfred/py-spy
Redis and HTTP caching strategy patternsskill: upstash-redis-js; https://redis.io/docs/latest/
vLLM deployment, quantization, speculative decoding, edge inferencehttps://docs.vllm.ai/
Full-stack performance audit walkthroughhttps://developer.chrome.com/docs/lighthouse/; ork delta in references/ork-delta.md + examples/orchestkit-performance-wins.md

References

Load on demand with Read("references/<file>"):

FileContent
ork-delta.mdOrchestKit floors, scars, and house decisions for this skill
cc-prompt-cache-guide.mdCC 2.1.72 prompt cache optimization, stable-first prompt structure
database-optimization.mdPostgres indexing and N+1 fixes backing the recorded audit wins

Real production before/after evidence (cache hierarchy, cost math): examples/orchestkit-performance-wins.md.

FAQ

What does the performance skill do?

Performance optimization patterns covering Core Web Vitals, React render optimization, lazy loading, image optimization, backend profiling, LLM inference, and sustainability UX. Use when improving page speed, debugging slow renders, optimizing bundles, reducing image payload, profiling backend, deploying LLMs efficiently, or reducing digital carbon footprint.

How do I install performance?

Run npx skills add https://github.com/yonatangross/orchestkit --skill performance in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/performance/ (personal) or .claude/skills/performance/ (project).

Which AI agents can use performance?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is performance safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains performance?

It is published by yonatangross in yonatangross/orchestkit. It has 225 recorded installs.

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