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 commands/farzannajipour/cursor-react-rules/optimize-performancegit clone --depth 1 https://github.com/Farzannajipour/cursor-react-rulesWhat 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 | $0.00000 | $0.01738 |
| Opus 5 | $0.00000 | $0.00869 |
| Sonnet 5 | $0.00000 | $0.00348 |
| Haiku 4.5 | $0.00000 | $0.00174 |
Grade A, and why
optimize-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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Performance
Overview
Analyze and optimize React component performance with memoization, code splitting, and lazy loading.
Steps
-
Identify performance issues
- Check for unnecessary re-renders
- Find expensive calculations
- Look for large bundle sizes
- Identify render bottlenecks
-
Apply React optimizations
- Add React.memo() for pure components
- Use useMemo() for expensive calculations
- Use useCallback() for function props
- Optimize context usage
-
Implement code splitting
- Use React.lazy() for route-based splitting
- Use dynamic imports for large components
- Split vendor bundles
-
Optimize images and assets
- Use next/image (Next.js)
- Implement lazy loading
- Use proper image formats (WebP)
Optimization Patterns
1. Memoize Components
import { memo } from 'react';
interface ItemProps {
item: Item;
onSelect: (id: string) => void;
}
// Memoize to prevent re-renders when props don't change
export const ListItem = memo<ItemProps>(({ item, onSelect }) => {
console.log('ListItem rendered:', item.id);
return (
<div onClick={() => onSelect(item.id)}>
{item.name}
</div>
);
});
// Custom comparison function
export const ComplexItem = memo<ItemProps>(
({ item, onSelect }) => {
return <div>{/* ... */}</div>;
},
(prevProps, nextProps) => {
// Return true if props are equal (don't re-render)
return prevProps.item.id === nextProps.item.id;
}
);
2. Memoize Expensive Calculations
import { useMemo } from 'react';
function DataTable({ data, filters }: Props) {
// Expensive filtering and sorting
const processedData = useMemo(() => {
console.log('Processing data...');
let result = data.filter(item =>
filters.every(filter => filter.match(item))
);
result.sort((a, b) => a.name.localeCompare(b.name));
return result;
}, [data, filters]); // Only recalculate when these change
return (
<table>
{processedData.map(item => (
<tr key={item.id}>{/* ... */}</tr>
))}
</table>
);
}
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 · 292 lines · 0 tokens per session scan A afff9317fadf
optimize-performance is a command published in the GitHub repository Farzannajipour/cursor-react-rules (3 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,738 tokens. 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.