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/porcupine-md/jonggrang/optimizing-react-performancenpx skills add porcupine-md/jonggrang --skill optimizing-react-performancegit clone --depth 1 https://github.com/porcupine-md/jonggrangWhat 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 | $0.00021 | $0.00885 |
| Opus 5 | $0.00010 | $0.00443 |
| Sonnet 5 | $0.00004 | $0.00177 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
optimizing-react-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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Profile First — Never Optimize Blindly
// 1. Enable React DevTools Profiler
// 2. Record the slow interaction
// 3. Find components with long render times
// Rule: Only optimize what the profiler shows as slow
The Three Memoization Tools
React.memo — Prevent Child Re-renders
// Without memo: re-renders whenever parent re-renders (even same props)
const ExpensiveList = React.memo(({ items, onItemClick }) => {
return items.map(item => (
<Item key={item.id} item={item} onClick={onItemClick} />
));
});
// Only re-renders when items or onItemClick reference changes
useCallback — Stable Function References
// Without useCallback: new function reference every render → breaks React.memo
const Parent = () => {
const [count, setCount] = useState(0);
// BAD: new reference every render
const handleClick = (id) => console.log(id);
// GOOD: stable reference (only changes if count changes)
const handleClick = useCallback((id) => {
console.log(id, count);
}, [count]);
return <ExpensiveList onItemClick={handleClick} />;
};
useMemo — Expensive Computations
// Only use useMemo for genuinely expensive calculations
const sortedAndFilteredItems = useMemo(() => {
return items
.filter(item => item.active)
.sort((a, b) => b.priority - a.priority);
}, [items]); // only recalculate when items changes
Anti-Patterns
// Anti-pattern 1: Premature optimization
const name = useMemo(() => `${first} ${last}`, [first, last]);
// String concatenation is instant — useMemo adds overhead here
// Anti-pattern 2: Object in deps breaks memoization
const options = { limit: 10 };
const result = useMemo(() => fetchData(options), [options]);
// options is a new object every render! Memoization is pointless.
// Fix: primitives in deps
const result = useMemo(() => fetchData({ limit }), [limit]);
Virtualization for Long Lists
// Don't render 10,000 items in the DOM
import { FixedSizeList } from 'react-window';
const VirtualList = ({ items }) => (
<FixedSizeList height={600} width="100%" itemCount={items.length} itemSize={50}>
{({ index, style }) => (
<div style={style}>{items[index].name}</div>
)}
</FixedSizeList>
);
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 · 131 lines · 21 tokens per session scan A 48890dd4223b
optimizing-react-performance is a skill published in the GitHub repository porcupine-md/jonggrang (11 stars, last pushed 6d ago), licensed MIT. It adds 21 tokens to every session and 885 once invoked, about $0.0001 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 2026-08-30.
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