ai-discoverability-audit

What This Skill Does

The ai-discoverability-audit skill provides a systematic framework for evaluating and optimizing a brand’s presence within generative AI search engines and large language models (LLMs) such as ChatGPT, Perplexity, Claude, and Gemini. Unlike traditional SEO, which focuses on keyword ranking in Google, this skill focuses on 'Answer Engine Optimization' (AEO). It analyzes how AI models perceive, describe, and recommend your brand to users, identifying gaps in knowledge, potential misattributions, and opportunities to increase brand authority. The skill walks you through four rigorous phases: testing direct brand recognition, category-based recommendations, thought leadership authority, and head-to-head competitive analysis.

Installation

To install this skill, run the following command in your terminal within the OpenClaw environment: clawhub install openclaw/skills/skills/brianrwagner/brw-ai-discoverability-audit

Use Cases

• Brand Reputation Management: Regularly check if AI models are spreading misinformation or outdated details about your services.
• Competitor Intelligence: Identify why competitors are being recommended by AI when users search for your specific industry categories.
• Marketing Strategy: Optimize your website content to be better understood by crawlers, ensuring your brand is included in 'best of' listicles generated by LLMs.
• Foundational SEO: Use the output to adjust your public-facing copy and structured data to better align with how AI interprets your market position.

Example Prompts

1. "I'm worried that ChatGPT doesn't know about our new product line; can you run an ai-discoverability-audit for my company?"
2. "How do I show up in ChatGPT or Perplexity when people search for 'best accounting software for startups'? Please perform an AI discoverability audit."
3. "Our primary competitor keeps appearing as the top recommendation in Claude; help me audit our AI discoverability to understand why."

Tips & Limitations

• Consistency is Key: AI knowledge bases update asynchronously. Run this audit quarterly to track changes.
• Cross-Platform Variation: Different models weight sources differently. Ensure you test across at least three major platforms (e.g., ChatGPT, Perplexity, Claude) for a comprehensive view.
• Context Matters: The more specific your input (URL, target geography, competitors), the more actionable the audit results will be. If your business is local, always include your city/region in the search queries.