Frontend Performance

Community

MartinOlivero

frontend-performance

This skill should be used when a frontend feels slow, fails Core Web Vitals, has a large bundle, or needs performance optimization. Trigger phrases include "make it faster", "improve performance", "Core Web Vitals", "LCP", "INP", "CLS", "reduce bundle size", "code splitting", "lazy load", "it's slow to load", "optimize images", "bundle too big", "Lighthouse score". It targets the real Core Web Vitals thresholds with stack-specific fixes for React + Vite + Vercel.

Overview

Publisher	MartinOlivero
Repository	`saas-builder`
Skill name	`frontend-performance`
Stars	4
Forks	2
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by MartinOlivero on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/MartinOlivero/saas-builder/tree/main/skills/frontend-performance
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.

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/MartinOlivero/saas-builder.git /tmp/saas-builder
mkdir -p .claude/skills
cp -r /tmp/saas-builder/skills/frontend-performance .claude/skills/frontend-performance

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Performance 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 Frontend Performance 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 Frontend Performance 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.

Frontend Performance

This skill makes a React + Vite app hit the Core Web Vitals bar — the same metrics Google uses for ranking and that users feel as "fast." It optimizes against real thresholds, not vibes.

Analogy: shipping a fast app is like packing for a flight. You don't drag every possession to the gate — you pack the carry-on with what's needed now (above-the-fold) and check the rest (lazy-load below-the-fold). Overpacking the initial bundle is what makes the page heavy.

The targets (field data, 75th percentile)

LCP < 2.5s — largest content paint.
INP < 200ms — interaction to next paint (replaced FID in March 2024).
CLS < 0.1 — cumulative layout shift.

Discovery (max 3 questions, only if unknown)

What's slow — first load (LCP/bundle) or interactions (INP)?
Do you have field data (real users) or only a Lighthouse lab score?
What's the biggest payload — JS bundle, images, or fonts?

Step 1 — Measure real users first

Install web-vitals (attribution build) and report onLCP/onINP/onCLS to your analytics. Lab tools (Lighthouse) miss field INP — optimize against real-user data, not just a local score. The Vercel Speed Insights package gives this with no backend.

Step 2 — Cut the bundle

Route-level code splitting with React.lazy + <Suspense>. Lazy-load below-the-fold and modal/dialog components so the initial bundle ships only above-the-fold code.
Split vendors in vite.config.ts via build.rollupOptions.output.manualChunks (separate react/react-dom, charts, icons) for long-term cache hits.
Keep the largest initial chunk under ~200KB gzipped. Treat Vite's "chunks larger than 500KB" warning as a budget failure, not a suggestion.
Enforce the budget in CI with size-limit; fail the build on regression. Inspect with vite-bundle-visualizer.
Tree-shake via named imports; prefer lucide-react over monolithic icon packs.

Step 3 — Fix LCP

Preload the LCP image: <link rel="preload" fetchpriority="high">. Never lazy-load the hero.
Serve images as AVIF/WebP through Vercel image optimization; always set explicit width/height (or aspect-ratio) to prevent CLS.
Self-host or preconnect fonts; use font-display: swap.

Step 4 — Fix INP

Break up long tasks; defer non-critical JS.
Use useTransition / startTransition for non-urgent React updates so input handlers stay responsive.
Avoid synchronous work inside input/click handlers.

Step 5 — Fix CLS

Reserve space for images, ads, and embeds (explicit dimensions / aspect-ratio).
Don't inject content above existing content after load.
Animate only transform/opacity (GPU-composited) — never width/height/top/left, which trigger layout and hurt both CLS and INP.

Output

Deliver: a before/after of the three vitals (or the targets if no baseline), the concrete vite.config.ts chunk config, the React.lazy split points, the image/font fixes, and the CI bundle-budget step.

Reference

GoogleChrome/web-vitals (~8.5k⭐), web.dev Core Web Vitals, Vite build docs (manualChunks), Vercel image optimization, size-limit.

Frequently asked questions

What does the Frontend Performance AI skill do?

Why use Frontend Performance on TypingMind?

Because you install it once and use it with any model. Frontend Performance 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 Frontend Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MartinOlivero/saas-builder/tree/main/skills/frontend-performance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Frontend Performance?

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 Frontend Performance?

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

Is the Frontend Performance AI skill free?

Yes. It is published on GitHub by MartinOlivero under the MIT license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Frontend Performance

The targets (field data, 75th percentile)

Discovery (max 3 questions, only if unknown)

Step 1 — Measure real users first

Step 2 — Cut the bundle

Step 3 — Fix LCP

Step 4 — Fix INP

Step 5 — Fix CLS

Output

Reference

Frequently asked questions

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