Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mtnyilmaz/agents/web-performancenpx skills add mtnyilmaz/agents --skill web-performancegit clone --depth 1 https://github.com/mtnyilmaz/agentsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00054 | $0.01271 |
| Opus 5 | $0.00027 | $0.00635 |
| Sonnet 5 | $0.00011 | $0.00254 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
web-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Performance Engineer
Expert in production-quality web performance optimization. Focused on Core Web Vitals, bundle size, and user experience metrics.
Core Web Vitals Targets
| Metric | Good | Needs Improvement | Poor |
|---|---|---|---|
| LCP (Largest Contentful Paint) | < 2.5s | 2.5–4s | > 4s |
| FID/INP (Interaction to Next Paint) | < 200ms | 200–500ms | > 500ms |
| CLS (Cumulative Layout Shift) | < 0.1 | 0.1–0.25 | > 0.25 |
| TTFB (Time to First Byte) | < 800ms | 800ms–1.8s | > 1.8s |
Next.js Performance Patterns
Image Optimization
import Image from 'next/image'
// ✅ Correct
<Image
src="/hero.jpg"
alt="Hero image"
width={1200}
height={630}
priority // for LCP images
sizes="(max-width: 768px) 100vw, 50vw"
quality={85}
/>
// ❌ Wrong
<img src="/hero.jpg" alt="Hero" />
Dynamic Import / Code Splitting
import dynamic from 'next/dynamic'
// ✅ Lazy load large components
const HeavyChart = dynamic(() => import('./HeavyChart'), {
loading: () => <ChartSkeleton />,
ssr: false, // for client-only components
})
// ✅ Modals, drawers — load only when opened
const Modal = dynamic(() => import('./Modal'))
Font Optimization
import { Inter, Roboto_Mono } from 'next/font/google'
const inter = Inter({
subsets: ['latin'],
display: 'swap', // prevents CLS
preload: true,
variable: '--font-inter',
})
React Suspense + Streaming
// ✅ Stream large data lists
import { Suspense } from 'react'
export default function Page() {
return (
<>
<FastComponent /> {/* renders immediately */}
<Suspense fallback={<ListSkeleton />}>
<SlowDataList /> {/* streamed */}
</Suspense>
</>
)
}
Bundle Analysis
# Next.js bundle analyzer
ANALYZE=true next build
# Check package sizes
npx bundlephobia <package-name>
# Find duplicate dependencies
npx duplicate-package-checker-webpack-plugin
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 211 lines · 54 tokens per session scan A 7a1163b15ef5
web-performance is a skill published in the GitHub repository mtnyilmaz/agents (6 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 1,271 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…