How do I install the Aeo LLM Visibility Audit skill?

Run npx skillmds@latest add saurabhshuklagrowisto/aeo-llm-visibility-audit in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Aeo LLM Visibility Audit safe to use?

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Which AI agents work with Aeo LLM Visibility Audit?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Aeo LLM Visibility Audit free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Aeo LLM Visibility Audit?

saurabhshuklagrowisto (@saurabhshuklagrowisto) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Aeo LLM Visibility Audit by saurabhshuklagrowisto

Name: Aeo LLM Visibility Audit
Author: saurabhshuklagrowisto

Aeo LLM Visibility Audit

Measures whether a brand shows up when buyers ask AI assistants (ChatGPT, Perplexity, Gemini) about its category, and turns the gaps into a content plan. Takes a brand name and a set of real buyer questions, checks which brands each engine names, and returns a share-of-voice score, the competitors winning the answers, and the exact questions where the brand is invisible. Use to audit AI/answer-engine discoverability (AEO/GEO), to prioritise content that AI assistants will cite, or to track share of voice in AI answers over time.

File contents

AEO / LLM Visibility Audit Skill

Search is moving from ten blue links to one AI answer. This skill measures the new page one: when your buyer asks ChatGPT, Perplexity, or Gemini for the best tool in your category, does your brand get named — and if not, who does, and on which questions.

When to use

To baseline and track answer-engine optimisation (AEO/GEO) — visibility inside AI-generated answers.
To decide which content to write next: the questions where you are invisible are your highest-leverage targets.
To benchmark share of voice against named competitors in AI answers over time.

When NOT to use

For classic Google SEO rank tracking — that is a different surface; use an SEO tool.
For brand sentiment — this measures presence in answers, not tone.

Method

Take the real questions your buyers type into AI assistants.
For each question, across each engine, capture which brands the answer names.
Score: share of voice = the share of answers that name you; list the competitors named most; flag the questions where you appear in zero engines (true content gaps).

Inputs

brand — your brand name
questions — the buyer questions to test
answers — for each (question, engine), the brands named. In production these come from live API calls to each assistant; the demo ships a sampled set so the logic runs with no keys.

Output (JSON)

share_of_voice_pct, answers_checked, mentions, top_competitors[], and gap_questions[] — the questions to target first.

Run it

python scripts/aeo_score.py         # built-in sample
python scripts/aeo_score.py in.json # your own brand + questions + answers

Zero dependencies, no API keys. To go live, replace the sampled answers with real calls to the ChatGPT / Perplexity / Gemini APIs; the scoring stays identical. A scheduled n8n version of this exact logic ships in pipeline-automation/aeo-llm-visibility-monitor.json.

---name: aeo-llm-visibility-auditdescription: Measures whether a brand shows up when buyers ask AI assistants (ChatGPT, Perplexity, Gemini) about its category, and turns the gaps into a content plan. Takes a brand name and a set of real buyer questions, checks which brands each engine names, and returns a share-of-voice score, the competitors winning the answers, and the exact questions where the brand is invisible. Use to audit AI/answer-engine discoverability (AEO/GEO), to prioritise content that AI assistants will cite, or to track share of voice in AI answers over time.---# AEO / LLM Visibility Audit SkillSearch is moving from ten blue links to one AI answer. This skill measures the new page one: when your buyer asks ChatGPT, Perplexity, or Gemini for the best tool in your category, does your brand get named — and if not, who does, and on which questions.## When to use- To baseline and track **answer-engine optimisation (AEO/GEO)** — visibility inside AI-generated answers.- To decide which content to write next: the questions where you are invisible are your highest-leverage targets.- To benchmark share of voice against named competitors in AI answers over time.## When NOT to use- For classic Google SEO rank tracking — that is a different surface; use an SEO tool.- For brand sentiment — this measures presence in answers, not tone.## Method1. Take the real questions your buyers type into AI assistants.2. For each question, across each engine, capture which brands the answer names.3. Score: **share of voice** = the share of answers that name you; list the competitors named most; flag the questions where you appear in **zero** engines (true content gaps).## Inputs- `brand` — your brand name- `questions` — the buyer questions to test- `answers` — for each (question, engine), the brands named. In production these come from live API calls to each assistant; the demo ships a sampled set so the logic runs with no keys.## Output (JSON)`share_of_voice_pct`, `answers_checked`, `mentions`, `top_competitors[]`, and `gap_questions[]` — the questions to target first.## Run it```bashpython scripts/aeo_score.py # built-in samplepython scripts/aeo_score.py in.json # your own brand + questions + answers```Zero dependencies, no API keys. To go live, replace the sampled answers with real calls to the ChatGPT / Perplexity / Gemini APIs; the scoring stays identical. A scheduled n8n version of this exact logic ships in `pipeline-automation/aeo-llm-visibility-monitor.json`.

Frequently asked questions