Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Profile and optimize application performance including load times, memory usage, and rendering. Use when debugging slow performance, memory leaks, or optimizing app speed.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 }
});
loading="lazy")まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。