chunking-strategy
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary…
Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing
$ npx -y skills add giuseppe-trisciuoglio/developer-kit --skill nextjs-data-fetching --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nextjs-data-fetchingContext preview
The summary Claude sees to decide when to auto-load this skill.
Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing
name: nextjs-data-fetching description: Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing between server and client fetching, setting up caching strategies, or handling loading and error states. allowed-tools: Read, Write, Edit, Bash
Provides patterns for data fetching in Next.js App Router: server-side fetching, SWR/React Query integration, ISR, revalidation, error boundaries, and loading states.
Fetch directly in async Server Components:
async function getPosts() {
const res = await fetch('https://api.example.com/posts');
if (!res.ok) throw new Error('Failed to fetch posts');
return res.json();
}
export default async function PostsPage() {
const posts = await getPosts();
return (
<ul>
{posts.map((post) => (
<li key={post.id}>{post.title}</li>
))}
</ul>
);
}Use `Promise.all()` for independent requests:
async function getDashboardData() {
const [user, posts, analytics] = await Promise.all([
fetch('/api/user').then(r => r.json()),
fetch('/api/posts').then(r => r.json()),
fetch('/api/analytics').then(r => r.json()),
]);
return { user, posts, analytics };
}
export default async function DashboardPage() {
const { user, posts, analytics } = await getDashboardData();
// Render dashboard
}async function getUserPosts(userId: string) {
const user = await fetch(`/api/users/${userId}`).then(r => r.json());
const posts = await fetch(`/api/users/${userId}/posts`).then(r => r.json());
return { user, posts };
}async function getPosts() {
const res = await fetch('https://api.example.com/posts', {
next: { revalidate: 60 } // Revalidate every 60 seconds
});
return res.json();
}// app/api/revalidate/route.ts
import { revalidateTag } from 'next/cache';
import { NextRequest } from 'next/server';
export async function POST(request: NextRequest) {
const tag = request.nextUrl.searchParams.get('tag');
if (tag) {
revalidateTag(tag);
return Response.json({ revalidated: true });
}
return Response.json({ revalidated: false }, { status: 400 });
}Tag data for selective revalidation:
async function getPosts() {
const res = await fetch('https://api.example.com/posts', {
next: { tags: ['posts'], revalidate: 3600 }
});
return res.json();
}async function getRealTimeData() {
const res = await fetch('https://api.example.com/data', {
cache: 'no-store'
});
return res.json();
}
// Or:
export const dynamic = 'force-dynamic';Install: `npm install swr`
'use client';
import useSWR from 'swr';
const fetcher = (url: string) => fetch(url).then(r => r.json());
export function Posts() {
const { data, error, isLoading } = useSWR('/api/posts', fetcher, {
refreshInterval: 5000,
revalidateOnFocus: true,
});
if (isLoading) return <div>Loading...</div>;
if (error) return <div>Failed to load posts</div>;
return (
<ul>
{data.map((post: any) => (
<li key={post.id}>{post.title}</li>
))}
</ul>
);
}Install: `npm install @tanstack/react-query`
// app/providers.tsx
'use client';
import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
import { useState } from 'react';
export function Providers({ children }: { children: React.ReactNode }) {
const [queryClient] = useState(() => new QueryClient({
defaultOptions: {
queries: {
staleTime: 60 * 1000,
refetchOnWindowFocus: false,
},
},
}));
return (
<QueryClientProvider client={queryClient}>
{children}
</QueryClientProvider>
);
}See [react-query.md](references/react-query.md) for mutations, optimistic updates, infinite queries, and advanced patterns.
Wrap client-side data fetching in Error Boundaries to handle failures gracefully:
See [error-boundaries.md](references/error-boundaries.md) for full `ErrorBoundary` implementations (basic, with reset callback) and usage examples with data fetching.
Use Server Actions for mutations with cache revalidation:
See [server-actions.md](references/server-actions.md) for complete examples including form validation with `useActionState`, error handling, and cache invalidation.
// app/posts/loading.tsx
export default function PostsLoading() {
return (
<div className="space-y-4">
{[...Array(5)].map((_, i) => (
<div key={i} className="h-16 bg-gray-200 animate-pulse rounded" />
))}
</div>
);
}// app/posts/page.tsx
import { Suspense } from 'react';
import { PostsList } from './PostsList';
import { PostsSkeleton } from './PostsSkeleton';
export default function PostsPage() {
return (
<div>
<h1>Posts</h1>
<Suspense fallback={<PostsSkeleton />}>
<PostsList />
</Suspense>
</div>
);
}1. **Default to Server Components** — Fetch in Server Components for better performance 2. **Use parallel fetching** — `Promise.all()` for indep
Modular plugin marketplace for Claude Code and agentic CLIs, with validated, spec-driven skills, agents, commands, and workflows for Java, TypeScript, Python, PHP, AWS, and AI.
Repo: giuseppe-trisciuoglio/developer-kit
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary…
Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design,…
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG…
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch…
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference…
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders,…