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 skills add OneWave-AI/ClaudeCodeUnleashed --skill performance-profilergit clone --depth 1 https://github.com/OneWave-AI/ClaudeCodeUnleashedWrote 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/onewave-ai/claudecodeunleashed/performance-profiler)<a href="https://agentmods.dev/skills/onewave-ai/claudecodeunleashed/performance-profiler"><img src="https://agentmods.dev/badge/skills/onewave-ai/claudecodeunleashed/performance-profiler/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/onewave-ai/claudecodeunleashed/performance-profiler"><img src="https://agentmods.dev/badge/skills/onewave-ai/claudecodeunleashed/performance-profiler.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00033 | $0.01085 |
| Opus 5.5 | $0.00013 | $0.00434 |
| Sonnet 5.5 | $0.00007 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
performance-profiler 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 1mo 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.
This is a copy
100% identical to performance-profiler — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Profiler
Instructions
When profiling performance:
- Identify the bottleneck type: Network, rendering, memory, or compute
- Measure baseline before optimizing
- Profile with appropriate tools
- Apply optimizations
- Measure improvement
Web Performance
Core Web Vitals
# Lighthouse CLI
npx lighthouse https://yoursite.com --view
# With specific metrics
npx lighthouse https://yoursite.com --only-categories=performance
Target Metrics:
| Metric | Good | Needs Work | Poor |
|---|---|---|---|
| LCP (Largest Contentful Paint) | < 2.5s | 2.5-4s | > 4s |
| INP (Interaction to Next Paint) | < 200ms | 200-500ms | > 500ms |
| CLS (Cumulative Layout Shift) | < 0.1 | 0.1-0.25 | > 0.25 |
Bundle Analysis
# Next.js
ANALYZE=true npm run build
# Webpack
npx webpack-bundle-analyzer stats.json
# Vite
npx vite-bundle-visualizer
React Performance
React DevTools Profiler
- Install React DevTools browser extension
- Open DevTools → Profiler tab
- Click Record, interact with app, stop recording
- Analyze flame graph for slow components
Common React Optimizations
// 1. Memoize expensive components
const MemoizedList = React.memo(function List({ items }) {
return items.map(item => <Item key={item.id} {...item} />);
});
// 2. Use useMemo for expensive calculations
const sortedItems = useMemo(() => {
return [...items].sort((a, b) => a.name.localeCompare(b.name));
}, [items]);
// 3. Use useCallback for stable function references
const handleClick = useCallback((id: string) => {
setSelected(id);
}, []);
// 4. Virtualize long lists
import { FixedSizeList } from 'react-window';
function VirtualList({ items }) {
return (
<FixedSizeList
height={400}
itemCount={items.length}
itemSize={50}
width="100%"
>
{({ index, style }) => (
<div style={style}>{items[index].name}</div>
)}
</FixedSizeList>
);
}
// 5. Lazy load components
const HeavyComponent = React.lazy(() => import('./HeavyComponent'));
function App() {
return (
<Suspense fallback={<Loading />}>
<HeavyComponent />
</Suspense>
);
}
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.
- 1mo ago First seen · 182 lines · 33 tokens per session scan A e4072b6db7ba
performance-profiler is a skill published in the GitHub repository OneWave-AI/ClaudeCodeUnleashed (5 stars, last pushed 7mo ago), licensed MIT. It adds 33 tokens to every session and 1,085 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to performance-profiler, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…
incident-response-incident-response
Coordinate production incident triage, mitigation, investigation, recovery, and follow-up. Use when responding to outages or other incidents that require structured operational coordination.