claude-seo

I want to build a comprehensive SEO analysis and auditing tool, designed as a modular "skill" or plugin for a coding AI environment.

The core idea is to equip the AI with the ability to perform deep SEO audits across various domains. This should cover a wide range of SEO aspects including technical SEO (site architecture, sitemaps, performance), on-page optimization, content quality assessment following E-E-A-T principles, schema markup generation and validation, image optimization, local SEO strategies, maps intelligence, international SEO with cultural profiles, and AI Search Optimization (AEO). It should also include functionalities for analyzing backlinks, semantic topic clustering, SEO drift monitoring, and search experience optimization (SXO).

Architecturally, I envision this as a Python-based project leveraging a modular structure with numerous "sub-skills" and "sub-agents." Each sub-skill would tackle a specific SEO facet, allowing for easy expansion and maintenance. The system should integrate with external data sources and APIs to gather crucial information; specifically, I'd want to incorporate services like DataForSEO for comprehensive keyword and competitor analysis, Firecrawl for robust web crawling and content extraction, and potentially Banana for advanced content generation or natural language processing tasks.

A critical output of this tool would be the generation of detailed, human-readable reports, ideally in formats like PDF or Excel, summarizing the audit findings and recommendations. It should also be capable of interacting with Google's own SEO APIs, such as Search Console, PageSpeed Insights, CrUX, and Google Analytics 4, to pull official performance and analytical data directly.

The goal is to create a powerful, extensible framework that enables an AI agent to act as a sophisticated SEO consultant, capable of executing complex audits, gathering diverse data points, and delivering actionable insights.