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/medy-gribkov/arcana/performance-optimizationnpx skills add medy-gribkov/arcana --skill performance-optimizationgit clone --depth 1 https://github.com/medy-gribkov/arcanaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/medy-gribkov/arcana/performance-optimization)<a href="https://agentmods.dev/skills/medy-gribkov/arcana/performance-optimization"><img src="https://agentmods.dev/badge/skills/medy-gribkov/arcana/performance-optimization.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00126 | $0.03619 |
| Opus 5 | $0.00063 | $0.01809 |
| Sonnet 5 | $0.00025 | $0.00724 |
| Haiku 4.5 | $0.00013 | $0.00362 |
Grade A, and why
performance-optimization 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 4d 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 — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior performance engineer who identifies bottlenecks and applies targeted optimizations with measurable impact.
Use this skill when
- Diagnosing or improving Core Web Vitals (LCP, INP, CLS)
- Reducing JavaScript bundle size or optimizing code splitting
- Implementing caching at any layer (HTTP, CDN, Redis, application)
- Optimizing database queries or fixing N+1 problems
- Debugging memory leaks in Node.js or browser
- Designing async processing pipelines for throughput
Core Web Vitals
LCP (Largest Contentful Paint) — Target: < 2.5s
LCP measures when the largest visible element finishes rendering. Usually a hero image, video, or large text block.
Common fixes:
- Preload the LCP resource:
<link rel="preload" as="image" href="/hero.webp"> - Inline critical CSS, defer the rest. Use
crittersfor automated critical CSS extraction. - Set
fetchpriority="high"on the LCP image. Removeloading="lazy"from above-the-fold images. - Serve from CDN. Eliminate redirect chains.
- Use
103 Early Hintsto let the browser start fetching before HTML arrives.
<!-- Optimal LCP image -->
<img
src="/hero.webp"
srcset="/hero-480.webp 480w, /hero-800.webp 800w, /hero-1200.webp 1200w"
sizes="(max-width: 600px) 480px, (max-width: 1024px) 800px, 1200px"
width="1200"
height="630"
alt="Hero image"
fetchpriority="high"
decoding="async"
/>
INP (Interaction to Next Paint) — Target: < 200ms
INP replaced FID in March 2024. It measures the worst-case delay between user input and visual update across the entire page lifecycle.
Common fixes:
- Break long tasks (>50ms) with
scheduler.yield()orsetTimeout(0). - Move heavy computation to Web Workers.
- Debounce rapid-fire events (scroll, resize, input) — 100-150ms debounce.
- Avoid layout thrashing: batch DOM reads, then batch DOM writes.
- Use
content-visibility: autoon offscreen sections.
// Break a long task into yielding chunks
async function processItems(items: Item[]) {
for (let i = 0; i < items.length; i++) {
processItem(items[i]);
if (i % 50 === 0) {
// Yield to main thread every 50 items
await new Promise((resolve) => setTimeout(resolve, 0));
}
}
}
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.
- 4d ago First seen · 401 lines · 126 tokens per session scan A ff119f5310df
performance-optimization is a skill published in the GitHub repository medy-gribkov/arcana (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 126 tokens to every session and 3,619 once invoked, about $0.0006 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.
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