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/macromania/agentop/react-performancenpx skills add macromania/agentop --skill react-performancegit clone --depth 1 https://github.com/macromania/agentopWhat 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 | $0.00061 | $0.03010 |
| Opus 5 | $0.00030 | $0.01505 |
| Sonnet 5 | $0.00012 | $0.00602 |
| Haiku 4.5 | $0.00006 | $0.00301 |
Grade A, and why
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 yesterday.
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 — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
React Performance Optimization
Performance patterns to prevent unnecessary re-renders, reduce bundle size, and create responsive UIs.
When to Use This Skill
- Optimizing slow components
- Reducing bundle size
- Fixing unnecessary re-renders
- Improving initial load time
- Debugging performance issues
Critical: Prevent Waterfalls
Impact: CRITICAL - The highest impact optimization.
What is a Waterfall?
Sequential requests where each waits for the previous:
Timeline:
[----Parent Data Fetch----]
[----Child Data Fetch----]
[----Grandchild Fetch----]
Total: 900ms
Anti-Pattern: Fetch on Mount
// ❌ BAD: Creates waterfall
function ParentComponent() {
const [data, setData] = useState(null);
useEffect(() => {
fetchParentData().then(setData); // Child waits for this
}, []);
if (!data) return <Loading />;
return <ChildComponent parentId={data.id} />;
}
function ChildComponent({ parentId }: { parentId: string }) {
const [childData, setChildData] = useState(null);
useEffect(() => {
fetchChildData(parentId).then(setChildData); // Waits for parent
}, [parentId]);
return <div>{childData?.name}</div>;
}
Solution: Parallel Data Fetching
// ✅ GOOD: Fetch in parallel at top level
function ParentComponent() {
const { parentData, childData, isLoading } = useParallelData();
if (isLoading) return <Loading />;
return (
<div>
<ParentView data={parentData} />
<ChildView data={childData} />
</div>
);
}
function useParallelData() {
const [data, setData] = useState({ parentData: null, childData: null });
const [isLoading, setIsLoading] = useState(true);
useEffect(() => {
Promise.all([fetchParentData(), fetchChildData()])
.then(([parentData, childData]) => {
setData({ parentData, childData });
setIsLoading(false);
});
}, []);
return { ...data, isLoading };
}
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.
- yesterday First seen · 475 lines · 61 tokens per session scan A 1fba80b06a36
react-performance is a skill published in the GitHub repository macromania/agentop (10 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 3,010 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.
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