What is a landing page prompt generator?

A tool that assembles a structured natural-language spec describing a landing page so that an LLM-driven builder like v0.dev, Claude Artifacts, Cursor, or Windsurf can generate component code in one pass. The prompt covers tech stack, brand mood, color guidance, section list, CTA copy direction, accessibility notes, and responsive notes.

Which AI builders does this prompt work with?

v0.dev (Vercel), Claude Artifacts, Cursor, Windsurf, Lovable, Bolt.new, and Replit Agent. Any tool that accepts a natural-language brief and returns React or HTML+Tailwind code can use this prompt without modification.

Why does the prompt include a tech stack constraint?

Stack-specific output reduces rewrite cost. A v0 prompt that specifies Next.js App Router gets server components; an Astro prompt skips client-only patterns. Without a stack constraint, the LLM defaults to whatever pattern dominates its training data, which is rarely the right answer for your project.

How specific should the section list be?

Specific enough to anchor structure, vague enough to let the LLM make local design decisions. Hero plus three feature cards plus social-proof plus pricing plus FAQ plus final-CTA is the right granularity. Listing every sub-component (logo, nav-link, button-state) over-constrains the model and produces brittle output.

Does this tool save my prompts?

No. Every value you enter and every prompt assembled lives in memory for this browser tab only. Nothing is transmitted to a server, stored in a database, or synced across devices. Close the tab and the data is gone. The Copy prompt button puts the assembled text on your clipboard.

Home Services Work About Book

§ · free tool

Landing page prompts. Built for v0.

Assemble a v0.dev-ready spec for any landing page: tech stack, mood, sections, CTA, accessibility. Browser-only; nothing is saved.

Book my 30-min build call Build a prompt

Browser-only · nothing leaves this device

§ 01 · load a preset

§ 02 · inputs

Brand, audience, mood, sections.

Product / brand name

Tech stack

One-line value prop (max 200 chars)

Target audience

Primary CTA action

Brand mood (pick up to 3)

Editorial warm Minimal tech Bold playful

Color lean

Sections to include

Hero Features Social proof Pricing FAQ Final CTA

§ 03 · at a glance

prompt length-

sections-

stack-

Adjust inputs; the prompt updates in real time.

§ 04 · generated prompt

Paste this into v0 or Claude.

§ 05 · three example prompts

What good prompts read like.

DTC skincare

Build a single-page landing page for "Hush Skincare". Tech: Next.js + Tailwind. Audience: DTC consumer 28-45, skincare-curious. Value prop: a fragrance-free toner with two ingredients. Mood: editorial-warm, off-white background, moss-green accents. Sections: hero with hero-bottle photo right, ingredient breakdown grid, two before/after testimonials with photo, FAQ on returns and ingredients, final CTA "Shop the toner". WCAG AA. Stack hero on mobile.

B2B SaaS workflow tool

Build a single-page landing page for "Tallyloop". Tech: Next.js + Tailwind. Audience: B2B SaaS buyer, mid-market ops manager. Value prop: connect Stripe + QuickBooks; close books 4 days faster. Mood: minimal-tech, slate-blue text, single accent. Sections: hero with product screenshot right, three feature cards, logo-wall social proof, pricing (3 plans, monthly toggle), FAQ on integrations, final CTA "Book a demo". Sticky nav. Visible focus rings.

Local-services agency

Build a single-page landing page for "Stillwater Law", a Charleston estate-planning practice. Tech: Astro + Tailwind. Audience: high-net-worth couples 50-70. Value prop: durable trusts written in plain English. Mood: editorial-warm with monochrome accents. Sections: hero with attorney portrait right, three service cards (will, trust, probate), testimonial with photo, FAQ on cost and timeline, final CTA "Book a 30-minute call". Phone-friendly tap targets ≥44px.

§ 06 · what makes a good landing-page prompt

A prompt is a brief the LLM can build from.

A landing-page prompt that produces usable code on the first pass does five things at once. It names the tech stack so the model picks the right component pattern. It states the value prop in 200 characters or fewer so there is one anchor for the H1. It lists sections in order with one-line guidance per section. It specifies the CTA verb so the same call repeats consistently across hero and final-CTA. And it constrains the model with explicit no-go items: no carousel libraries, no external animation deps, no analytics scripts. The right granularity is structural; let the model make local design decisions inside each section.

Clarity over cleverness

Prompts that read like a human briefing another human work better than prompts engineered with role-play tricks or reasoning traces. Tools like v0.dev from Vercel and Claude Artifacts from Anthropic are tuned on explicit step-by-step instructions. A short brief that names the audience, the action, and the sections beats a long brief filled with adjective-heavy adjectives. If a junior designer could build the page from your prompt without asking questions, an LLM can too.

Component-aware specs

Match the prompt to the framework. A v0 prompt that targets Next.js App Router should ask for server components by default and explicit "use client" only where interactivity demands it. An Astro prompt should ask for static rendering and component islands. A plain HTML+Tailwind prompt should specify CDN inclusion and a single `