Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Profile and optimize application performance including load times, memory usage, and rendering. Use when debugging slow performance, memory leaks, or optimizing app speed.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
When profiling performance:
# 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 |
# Next.js
ANALYZE=true npm run build
# Webpack
npx webpack-bundle-analyzer stats.json
# Vite
npx vite-bundle-visualizer
// 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>
);
}
# CPU profile
node --prof app.js
node --prof-process isolate-*.log > profile.txt
# Heap snapshot
node --inspect app.js
# Then use Chrome DevTools Memory tab
# Clinic.js (comprehensive)
npx clinic doctor -- node app.js
npx clinic flame -- node app.js
npx clinic bubbleprof -- node app.js
// Add to app for debugging
const used = process.memoryUsage();
console.log({
heapUsed: `${Math.round(used.heapUsed / 1024 / 1024)} MB`,
heapTotal: `${Math.round(used.heapTotal / 1024 / 1024)} MB`,
external: `${Math.round(used.external / 1024 / 1024)} MB`,
});
-- PostgreSQL: Analyze slow queries
EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'test@example.com';
-- Find missing indexes
SELECT relname, seq_scan, idx_scan
FROM pg_stat_user_tables
WHERE seq_scan > idx_scan;
// Bad: N+1 query
const users = await db.user.findMany();
for (const user of users) {
const posts = await db.post.findMany({ where: { userId: user.id } });
}
// Good: Single query with include
const users = await db.user.findMany({
include: { posts: true }
});
// Good: Select only needed fields
const users = await db.user.findMany({
select: { id: true, name: true, email: true }
});
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
中文简介正在准备中。当前显示原文说明。
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
中文简介正在准备中。当前显示原文说明。
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
中文简介正在准备中。当前显示原文说明。
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
中文简介正在准备中。当前显示原文说明。
Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
中文简介正在准备中。当前显示原文说明。
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
中文简介正在准备中。当前显示原文说明。