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SEO-to-Code Pipeline for Next.js
Turn target keywords into production-ready, search-optimized Next.js App Router components with JSON-LD schema & Cursor rules.
$5
SEO-to-Code Pipeline for Next.js
Example session with this skill installed
Create a search-optimized landing page for an AI code review tool named "CodeGuard".
- Primary Keyword: "AI code reviewer for Next.js"
- Secondary Keywords: "automated pull request analysis", "TypeScript code quality tool", "Cursor IDE rules"
- Page Type: SoftwareApplication
- Page Goal: Product landing page with feature breakdown, pricing snippet, and FAQ section.
- Read your context and instructions
- Compiled the seo-to-code pipeline
// app/code-reviewer/page.tsx
import { Metadata } from 'next';
export const metadata: Metadata = {
title: 'CodeGuard - AI Code Reviewer for Next.js & TypeScript',
description: 'Automate pull request analysis and enforce Cursor IDE rules with CodeGuard, the intelligent AI code review tool.',
openGraph: {
title: 'CodeGuard - AI Code Reviewer for Next.js',
description: 'Automate pull request analysis with deep TypeScript static inspection.',
type: 'website',
},
};
export default function CodeReviewerPage() {
const jsonLd = {
'@context': 'https://schema.org',
'@type': 'SoftwareApplication',
'name': 'CodeGuard',
'applicationCategory': 'DeveloperApplication',
'operatingSystem': 'Web, Cursor IDE',
'offers': {
'@type': 'Offer',
'price': '0',
'priceCurrency': 'USD'
}
};
return (
<main className="max-w-7xl mx-auto px-4 py-12">
<script
type="application/ld+json"
dangerouslySetInnerHTML={{ __html: JSON.stringify(jsonLd) }}
/>
<header className="mb-12 text-center">
<h1 className="text-4xl font-bold tracking-tight">
AI Code Reviewer for Next.js Applications
</h1>
<p className="mt-4 text-xl text-gray-600">
Automate pull request analysis and maintain code quality across engineering teams.
</p>
</header>
<section className="my-16">
<h2 className="text-2xl font-semibold mb-6">Automated Pull Request Analysis</h2>
<article className="prose lg:prose-xl">
<p>
CodeGuard integrates with your GitHub repository to deliver instant static analysis and automated TypeScript checks...
</p>
</article>
</section>
</main>
);
}
Connects securely to your tools. The creator never sees your data.
About this skill
Automate your Technical SEO workflow directly inside your AI workspace. The SEO-to-Code Pipeline takes target keywords, entity specifications, and content goals, converting them into structured, production-ready Next.js React code.
Core Capabilities
- Keyword & Entity Mapping: Automatically generates strict heading trees (
<h1>-><h2>-><h3>) without skipped levels, mapping LSI keywords naturally into component sections. - Schema.org Integration: Generates valid JSON-LD payloads for
Article,SoftwareApplication,Product, orFAQPageschemas alongside OpenGraph and Twitter card parameters. - App Router Native: Outputs clean TypeScript (
page.tsx) featuring nativegenerateMetadataimplementation, strict semantic HTML tags (<article>,<section>,<header>), and zero generic<div>bloat. - Cursor Rules Generation: Automatically writes a
.cursor/rules/seo.mdcfile to lock in SEO standards across your entire codebase.
Generated Deliverables
page.tsx: Fully typed, semantic Next.js App Router page.schema.json: Validated Schema.org structured data script..cursor/rules/seo.mdc: Machine-readable IDE rules to enforce standards in future edits.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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