GEO SEO Claude

What it proposes

A collection of Claude Code slash commands and Python helper scripts that audit websites for “Generative Engine Optimization” (GEO), the practice of structuring web content so that AI-powered search engines (ChatGPT, Perplexity, Google AI Overviews, etc.) are more likely to cite it. The tool breaks the problem into 13 sub-skills covering crawler access checks, citability scoring (optimizing passage length and fact density for AI extraction), structured data generation via JSON-LD templates, brand mention scanning across AI-cited platforms, and traditional technical SEO audits. A full audit dispatches five parallel subagents and produces a client-ready markdown or PDF report. A lightweight CRM layer tracks prospects and proposals, positioning the tool for agency or freelance use.

Best used when

The user operates a content-heavy website or marketing agency and needs to systematically audit and improve how AI search engines surface and cite that content. The skill set is most valuable when SEO optimization is a recurring professional concern rather than a one-off task, and when the user already works inside Claude Code as a primary development environment. The CRM and proposal features make it particularly oriented toward client-facing consulting workflows.

Poor fit when

The user’s primary workflows center on local knowledge management, writing, or personal tooling rather than web publishing and SEO. The tool’s value depends entirely on having public-facing web content to optimize. It also carries several friction points for general-purpose adoption: it requires Python 3.8+ and several helper scripts, the one-command curl/bash installer assumes a level of trust in third-party install scripts, and the README’s marketing claims (“+527% AI-referred traffic growth”) are presented without cited sources, which undermines confidence in the methodology’s empirical grounding. The heavy promotion of a paid community suggests the open-source tool may function partly as a lead funnel, raising questions about long-term maintenance incentives independent of community upsells.

Verdict

Catalog. This is a competent, well-structured Claude Code skill collection for a real and growing problem space (optimizing content for AI-powered search). The architecture, with parallel subagents, modular sub-skills, and structured output, reflects genuine thought about the workflow. However, it addresses a domain (web SEO and marketing) that sits outside the scope of local-first knowledge management, writing, and personal tooling workflows. There is no in-scope alternative that does the same thing better because the problem itself is out of scope. Worth knowing about for anyone who does web content optimization inside Claude Code, but not actionable for vault-centric or non-web-publishing projects.