Imported skill server_cache_lru from vercel
Scanned 9/11/2026
Install to Claude Code
npx -y skills add bitwikiorg/skills.md --skill server_cache_lru --agent claude-codeInstalls into .claude/skills of the current project.
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---
description: Imported skill server_cache_lru from vercel
name: server_cache_lru
signature: 1924b64561841923b88a657085097a0aeba3e0ba2d5470b9f5c15cc10d6ae70f
source: /a0/tmp/skills_research/vercel/skills/react-best-practices/rules/server-cache-lru.md
---
---
title: Cross-Request LRU Caching
impact: HIGH
impactDescription: caches across requests
tags: server, cache, lru, cross-request
---
## Cross-Request LRU Caching
`React.cache()` only works within one request. For data shared across sequential requests (user clicks button A then button B), use an LRU cache.
**Implementation:**
```typescript
import { LRUCache } from 'lru-cache'
const cache = new LRUCache<string, any>({
max: 1000,
ttl: 5 * 60 * 1000 // 5 minutes
})
export async function getUser(id: string) {
const cached = cache.get(id)
if (cached) return cached
const user = await db.user.findUnique({ where: { id } })
cache.set(id, user)
return user
}
// Request 1: DB query, result cached
// Request 2: cache hit, no DB query
```
Use when sequential user actions hit multiple endpoints needing the same data within seconds.
**With Vercel's [Fluid Compute](https://vercel.com/docs/fluid-compute):** LRU caching is especially effective because multiple concurrent requests can share the same function instance and cache. This means the cache persists across requests without needing external storage like Redis.
**In traditional serverless:** Each invocation runs in isolation, so consider Redis for cross-process caching.
Reference: [https://github.com/isaacs/node-lru-cache](https://github.com/isaacs/node-lru-cache)
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