xwmtzcp

AI Search Audit — Security skill for Claude Code

Security community

Open-source GEO / AEO skill for Claude.

How to install AI Search Audit

This entry records only its repository, not the path inside it, so there is no exact command to give. Open xwmtzcp/ai-search-audit and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What AI Search Audit does

Open-source GEO / AEO skill for Claude. Audits how ChatGPT, Perplexity, Gemini, Google AI Overviews — plus DeepSeek, Doubao, Qwen and Kimi — describe and cite your brand, then writes the fixes. It won't fabricate a platform answer: you run the prompts, it does the analysis.

Alternatives in Security

  • Perplexity Super Skills — Complete collection of 12 Perplexity Computer Super-Skills merging Perplexity + Claude Code capabilities acros 346 ★
  • Brand Strategy — Define or audit a brand — positioning, visual identity system, voice and tone, and brand architecture 309 ★
  • Release Audit — Use right before cutting a release — it spawns ONE fresh sibling session via CCC that audits only the JUDGMENT 130 ★

README

ai-search-audit

**A Claude skill for Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) that refuses to make anything up.** It audits how a brand appears in ChatGPT, Google AI Overviews, Perplexity, Claude and Gemini — and in the Chinese-market assistants **DeepSeek, Doubao (豆包), Qwen (通义千问) and Kimi**, which most tools in this space ignore — then produces the actual changes — across four layers: **Discoverability**, **Clarity**, **Authority**, **Trust**.

Every tool in this space says *audit, optimize and track*. Three things this one does that they don't:

  • It cannot invent a platform answer, by design. Claude has no access to ChatGPT or Perplexity. So it designs the prompt set and the recording format, a human runs them, and Claude structures and classifies what actually comes back. It will not generate a response it never received — the single most damaging failure available to a tool like this, because the user cannot tell the difference between a real citation and a plausible one.
  • It grades its own recommendations, including down. Every suggestion is marked worth doing, worth trying or not yet, with one test overriding all three: if the entire AI-citation story turned out to be false, would this still be worth doing?
  • It sorts fixes by when you could know they worked — same-day, weeks-to-months, or never individually attributable — rather than by tactic. That ordering is what stops a team starting with the slow invisible items, seeing nothing for a quarter, and concluding none of it works.

Two stages, and the second one ships files rather than advice. The audit answers *what's wrong and why*. Optimization, entered on your say-so afterwards, answers *what to change, in what order, and how you'll know it worked* — and hands over the JSON-LD block, the `robots.txt`, the page specs and the verification sheet, not a list of things someone should do.

**Topics**: `generative-engine-optimization` `answer-engine-optimizat