How does this skill improve Core Web Vitals?

It focuses on reducing Largest Contentful Paint (LCP) through streaming and next/image optimization, while improving Time to First Byte (TTFB) via Server Components and efficient caching.

Is it compatible with the App Router?

The skill is specifically designed for the Next.js App Router and utilizes modern React Server Components architecture as its foundation.

Does it support Next.js 14 features like PPR?

Yes, it includes specific implementation patterns for Partial Prerendering to serve static shells immediately while streaming dynamic content.

Does it cover data fetching deduplication?

Yes, it leverages the extended Next.js fetch API to ensure automatic request memoization and provides logic for both path-based and tag-based revalidation.

Can it help identify performance anti-patterns?

Absolutely, the skill provides a detailed checklist to detect and fix common mistakes like client-side waterfalls, missing Suspense boundaries, and over-caching.

Next.js RSC Performance Patterns

Name: Next.js RSC Performance Patterns
Author: Agentient

byAgentient

Learning & Documentation

Optimizes Next.js 14+ applications using React Server Components, streaming, and advanced caching strategies to improve Core Web Vitals.

About

This skill provides a comprehensive suite of performance optimization patterns specifically for Next.js 14+ and the App Router architecture. It empowers developers to implement efficient data fetching strategies like parallelizing requests and leveraging React Server Components to minimize client-side JavaScript. By integrating expert guidance on Streaming, Suspense boundaries, and Partial Prerendering (PPR), the skill ensures applications deliver fast Time to First Byte (TTFB) and optimized Largest Contentful Paint (LCP) while maintaining granular control over Next.js's multi-layered caching system.

Key Features

Advanced Suspense and streaming patterns for progressive UI loading
Anti-pattern detection for React Server Components and image optimization
Implementation of parallel data fetching to eliminate request waterfalls
Configuration of Partial Prerendering (PPR) for hybrid static-dynamic routes
0 GitHub stars
Granular caching strategies using time-based and on-demand revalidation

Use Cases

Improving Core Web Vitals for high-traffic e-commerce product pages
Migrating legacy client-side fetching to high-performance Server Components
Optimizing slow-loading dashboards with complex data dependencies

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add agentient/vibekit nextjs-rsc-performance-patterns

For use in Claude.ai and ChatGPT

Download Skill

GitHub