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Next Cache Components

Organization
vercel
next-cache-components

Next.js 16 Cache Components guidance — PPR, use cache directive, cacheLife, cacheTag, updateTag, and migration from unstable_cache. Use when implementing partial prerendering, caching strategies, or migrating from older Next.js cache patterns.

Overview

Publishervercel
Repositoryvercel-plugin
Skill namenext-cache-components
Stars
286
Forks
56
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by vercel on GitHub. Read the source before you install it.

Installation

Install the Next Cache Components AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vercel/vercel-plugin.git /tmp/vercel-plugin
mkdir -p .claude/skills
cp -r /tmp/vercel-plugin/skills/next-cache-components .claude/skills/next-cache-components
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Next Cache Components in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Next Cache Components on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Next Cache Components is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cache Components (Next.js 16+)

Cache Components enable Partial Prerendering (PPR) - mix static, cached, and dynamic content in a single route.

Enable Cache Components

ts
// next.config.ts
import type { NextConfig } from 'next'

const nextConfig: NextConfig = {
  cacheComponents: true,
}

export default nextConfig

This replaces the old experimental.ppr flag.


Three Content Types

With Cache Components enabled, content falls into three categories:

1. Static (Auto-Prerendered)

Synchronous code, imports, pure computations - prerendered at build time:

tsx
export default function Page() {
  return (
    <header>
      <h1>Our Blog</h1>  {/* Static - instant */}
      <nav>...</nav>
    </header>
  )
}

2. Cached (use cache)

Async data that doesn't need fresh fetches every request:

tsx
async function BlogPosts() {
  'use cache'
  cacheLife('hours')

  const posts = await db.posts.findMany()
  return <PostList posts={posts} />
}

3. Dynamic (Suspense)

Runtime data that must be fresh - wrap in Suspense:

tsx
import { Suspense } from 'react'

export default function Page() {
  return (
    <>
      <BlogPosts />  {/* Cached */}

      <Suspense fallback={<p>Loading...</p>}>
        <UserPreferences />  {/* Dynamic - streams in */}
      </Suspense>
    </>
  )
}

async function UserPreferences() {
  const theme = (await cookies()).get('theme')?.value
  return <p>Theme: {theme}</p>
}

use cache Directive

File Level

tsx
'use cache'

export default async function Page() {
  // Entire page is cached
  const data = await fetchData()
  return <div>{data}</div>
}

Component Level

tsx
export async function CachedComponent() {
  'use cache'
  const data = await fetchData()
  return <div>{data}</div>
}

Function Level

tsx
export async function getData() {
  'use cache'
  return db.query('SELECT * FROM posts')
}

Cache Profiles

Built-in Profiles

tsx
'use cache'                    // Default: 5m stale, 15m revalidate
tsx
'use cache: remote'           // Platform-provided cache (Redis, KV)
tsx
'use cache: private'          // For compliance, allows runtime APIs

cacheLife() - Custom Lifetime

tsx
import { cacheLife } from 'next/cache'

async function getData() {
  'use cache'
  cacheLife('hours')  // Built-in profile
  return fetch('/api/data')
}

Built-in profiles: 'default', 'minutes', 'hours', 'days', 'weeks', 'max'

Inline Configuration

tsx
async function getData() {
  'use cache'
  cacheLife({
    stale: 3600,      // 1 hour - serve stale while revalidating
    revalidate: 7200, // 2 hours - background revalidation interval
    expire: 86400,    // 1 day - hard expiration
  })
  return fetch('/api/data')
}

Cache Invalidation

cacheTag() - Tag Cached Content

tsx
import { cacheTag } from 'next/cache'

async function getProducts() {
  'use cache'
  cacheTag('products')
  return db.products.findMany()
}

async function getProduct(id: string) {
  'use cache'
  cacheTag('products', `product-${id}`)
  return db.products.findUnique({ where: { id } })
}

updateTag() - Immediate Invalidation

Use when you need the cache refreshed within the same request:

tsx
'use server'

import { updateTag } from 'next/cache'

export async function updateProduct(id: string, data: FormData) {
  await db.products.update({ where: { id }, data })
  updateTag(`product-${id}`)  // Immediate - same request sees fresh data
}

revalidateTag() - Background Revalidation

Use for stale-while-revalidate behavior:

tsx
'use server'

import { revalidateTag } from 'next/cache'

export async function createPost(data: FormData) {
  await db.posts.create({ data })
  revalidateTag('posts')  // Background - next request sees fresh data
}

Runtime Data Constraint

Cannot access cookies(), headers(), or searchParams inside use cache.

Solution: Pass as Arguments

tsx
// Wrong - runtime API inside use cache
async function CachedProfile() {
  'use cache'
  const session = (await cookies()).get('session')?.value  // Error!
  return <div>{session}</div>
}

// Correct - extract outside, pass as argument
async function ProfilePage() {
  const session = (await cookies()).get('session')?.value
  return <CachedProfile sessionId={session} />
}

async function CachedProfile({ sessionId }: { sessionId: string }) {
  'use cache'
  // sessionId becomes part of cache key automatically
  const data = await fetchUserData(sessionId)
  return <div>{data.name}</div>
}

Exception: use cache: private

For compliance requirements when you can't refactor:

tsx
async function getData() {
  'use cache: private'
  const session = (await cookies()).get('session')?.value  // Allowed
  return fetchData(session)
}

Cache Key Generation

Cache keys are automatic based on:

  • Build ID - invalidates all caches on deploy
  • Function ID - hash of function location
  • Serializable arguments - props become part of key
  • Closure variables - outer scope values included
tsx
async function Component({ userId }: { userId: string }) {
  const getData = async (filter: string) => {
    'use cache'
    // Cache key = userId (closure) + filter (argument)
    return fetch(`/api/users/${userId}?filter=${filter}`)
  }
  return getData('active')
}

Complete Example

tsx
import { Suspense } from 'react'
import { cookies } from 'next/headers'
import { cacheLife, cacheTag } from 'next/cache'

export default function DashboardPage() {
  return (
    <>
      {/* Static shell - instant from CDN */}
      <header><h1>Dashboard</h1></header>
      <nav>...</nav>

      {/* Cached - fast, revalidates hourly */}
      <Stats />

      {/* Dynamic - streams in with fresh data */}
      <Suspense fallback={<NotificationsSkeleton />}>
        <Notifications />
      </Suspense>
    </>
  )
}

async function Stats() {
  'use cache'
  cacheLife('hours')
  cacheTag('dashboard-stats')

  const stats = await db.stats.aggregate()
  return <StatsDisplay stats={stats} />
}

async function Notifications() {
  const userId = (await cookies()).get('userId')?.value
  const notifications = await db.notifications.findMany({
    where: { userId, read: false }
  })
  return <NotificationList items={notifications} />
}

Migration from Previous Versions

Old ConfigReplacement
experimental.pprcacheComponents: true
dynamic = 'force-dynamic'Remove (default behavior)
dynamic = 'force-static''use cache' + cacheLife('max')
revalidate = NcacheLife({ revalidate: N })
unstable_cache()'use cache' directive

Migrating unstable_cache to use cache

unstable_cache has been replaced by the use cache directive in Next.js 16. When cacheComponents is enabled, convert unstable_cache calls to use cache functions:

Before (unstable_cache):

tsx
import { unstable_cache } from 'next/cache'

const getCachedUser = unstable_cache(
  async (id) => getUser(id),
  ['my-app-user'],
  {
    tags: ['users'],
    revalidate: 60,
  }
)

export default async function Page({ params }: { params: Promise<{ id: string }> }) {
  const { id } = await params
  const user = await getCachedUser(id)
  return <div>{user.name}</div>
}

After (use cache):

tsx
import { cacheLife, cacheTag } from 'next/cache'

async function getCachedUser(id: string) {
  'use cache'
  cacheTag('users')
  cacheLife({ revalidate: 60 })
  return getUser(id)
}

export default async function Page({ params }: { params: Promise<{ id: string }> }) {
  const { id } = await params
  const user = await getCachedUser(id)
  return <div>{user.name}</div>
}

Key differences:

  • No manual cache keys - use cache generates keys automatically from function arguments and closures. The keyParts array from unstable_cache is no longer needed.
  • Tags - Replace options.tags with cacheTag() calls inside the function.
  • Revalidation - Replace options.revalidate with cacheLife({ revalidate: N }) or a built-in profile like cacheLife('minutes').
  • Dynamic data - unstable_cache did not support cookies() or headers() inside the callback. The same restriction applies to use cache, but you can use 'use cache: private' if needed.

Limitations

  • Edge runtime not supported - requires Node.js
  • Static export not supported - needs server
  • Non-deterministic values (Math.random(), Date.now()) execute once at build time inside use cache

For request-time randomness outside cache:

tsx
import { connection } from 'next/server'

async function DynamicContent() {
  await connection()  // Defer to request time
  const id = crypto.randomUUID()  // Different per request
  return <div>{id}</div>
}

Sources:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Next Cache Components AI skill do?

Next.js 16 Cache Components guidance — PPR, use cache directive, cacheLife, cacheTag, updateTag, and migration from unstable_cache. Use when implementing partial prerendering, caching strategies, or migrating from older Next.js cache patterns.

Why use Next Cache Components on TypingMind?

Because you install it once and use it with any model. Next Cache Components is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Next Cache Components in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/vercel-plugin/tree/main/skills/next-cache-components. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Next Cache Components?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Next Cache Components?

As many as you like. As long as a model supports skills, you can use Next Cache Components with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Next Cache Components AI skill free?

It is published on GitHub by vercel. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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