This toolkit addresses the critical challenge of making websites visible and citable by emerging AI search engines like ChatGPT, Perplexity, Claude, and Gemini. It targets website owners, developers, and SEO professionals seeking to ensure their content is discovered and utilized by AI, offering a significant benefit by preventing invisibility in AI-driven search results. The project is grounded in academic research from Princeton KDD 2024.

How It Works

The GEO Optimizer audits websites against 47 research-backed methods, focusing on technical infrastructure rather than content rewriting. It analyzes critical elements such as robots.txt, llms.txt, Schema JSON-LD, meta tags, and brand signals. The tool generates actionable recommendations and can automatically create or fix essential files like robots.txt, llms.txt, and schema markup. Its approach prioritizes discoverability by AI crawlers, aiming to improve citation rates and AI search visibility.

Quick Start & Requirements

• Primary install: pip install geo-optimizer-skill
• Prerequisites: Python 3.9+. Optional LLM features require pip install geo-optimizer-skill[llm], and MCP compatibility requires pip install geo-optimizer-skill[mcp].
• Links:
  • Live Demo: https://geo-optimizer-web.onrender.com
  • Documentation: https://auriti-labs.github.io/geo-optimizer-skill/
  • Changelog: CHANGELOG.md

Highlighted Details

• Audits against 47 AI search visibility methods, including research from Princeton KDD 2024 and AutoGEO ICLR 2026.
• Comprehensive checks cover 8 areas: Robots.txt, llms.txt, Schema JSON-LD, Meta Tags, Content, Brand & Entity, Signals, and AI Discovery.
• Includes advanced checks for CDN crawler access, JS rendering, WebMCP readiness, negative signals, prompt injection, trust stack score, RAG chunk readiness, content decay, and platform citation profiles.
• Supports CI/CD integration with multiple output formats (SARIF, JSON, HTML, GitHub annotations) for automated workflows.
• Offers a Python API and is compatible with Model Context Protocol (MCP) clients.
• Provides a dynamic badge to display GEO scores directly in README files.

Maintenance & Community

The project is built by Auriti Labs. While specific community channels like Discord or Slack are not listed, a detailed roadmap is available in docs/ROADMAP.md.

Licensing & Compatibility

The project is released under the MIT License, which is permissive for commercial use and integration into closed-source projects. It is also noted as "MCP Compatible."

Limitations & Caveats

The project is actively developed, with significant architectural evolution planned for v5.0 in May 2027. While extensive, the focus is primarily on technical infrastructure for AI discoverability, with content optimization being a secondary consideration. Specific details on unsupported platforms or known bugs are not detailed in the README.