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346fc4617e
♻️ refactor: migrate AI Rules to Claude Code Skills system
Migrate all AI Rules from .cursor/rules/ to .agents/skills/ directory:
- Move 23 skills to .agents/skills/ (main directory)
- Update symlinks: .claude/skills, .cursor/skills, .codex/skills
- Create project-overview skill from project documentation
- Add references/ subdirectories for complex skills
- Remove LobeChat references from skill descriptions
- Delete obsolete .cursor/rules/ and .claude/commands/prompts/ directories
Skills structure enables better portability and maintainability across AI tools.
1.3 KiB
1.3 KiB
| title | impact | impactDescription | tags |
|---|---|---|---|
| Cross-Request LRU Caching | HIGH | caches across requests | 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:
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: 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