Ai Visibility Audit

Organization

unifapi-agent

ai-visibility-audit

When the user wants to know whether their brand or domain is cited in AI answers — AI Overviews, ChatGPT, or AI search — for the queries that matter, and how they stack up against competitors. Also use on "AI visibility audit," "am I cited in AI answers," "do I show up in ChatGPT," "AI Overviews audit," "GEO audit," "answer engine audit," "AEO audit," "why isn't my brand in AI results," or "who gets cited instead of me." The GEO equivalent of an SEO audit. For ongoing mention tracking, see llm-mention-tracking. For prioritizing the gaps, see ai-answer-gap.

Overview

Publisher	unifapi-agent
Repository	`agents`
Skill name	`ai-visibility-audit`
Stars	590
Forks	115
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by unifapi-agent on GitHub. Read the source before you install it.

Installation

Install the Ai Visibility Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/unifapi-agent/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/ai-visibility-agent/ai-visibility-audit .claude/skills/ai-visibility-audit

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Visibility Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Visibility Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Visibility Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI Visibility Audit

Audit how a brand appears in a dated sample of AI answers and identify useful investigations. Connect through the unifapi skill. Read the current schema and price for each operation, set a budget, and reuse existing product context.

Workflow

Choose 10–30 prompts covering definitions, comparisons, purchase criteria and concrete use cases. Freeze brands, aliases, citation domains, market and language.
Use POST /geo/answers for ChatGPT or Gemini. Record engine, surface, exact prompt, search mode, reported model (or null), observation time, answer, sources, brand observations and request id. ChatGPT natural and forced-search samples belong in separate groups.
Use /geo/serp for Google AI Mode and /seo/serp with include_ai_overview for Google Search AI Overviews. Inspect answer references; a top-level target match can also refer to another link. A result's rank is not a brand recommendation rank. Do not attribute either Google response to ChatGPT.
Keep corpus discovery separate. /geo/mentions/search, /top-domains and /top-pages identify indexed answers and frequently represented sources. They do not confirm the result of a particular live prompt. /cross-aggregated-metrics returns group counts with potentially overlapping groups, not a computed citation share.
Save raw normalized responses and billing. Use the runner in llm-mention-tracking/scripts/monitor.mjs for budgeted ChatGPT/Gemini collection, resumable snapshots and CSV evidence.

Measure coverage and tracked-brand share

Use the definitions in references/geo-methodology.md. Report completion rate, answer rate, brand mention coverage and citation coverage per engine. Coverage measures presence across successful collections, including valid no-answer results. Share measures a brand's fraction of the summed observations for tracked brands; it does not estimate total market share.

Do not merge citation links with unused search results. Deduplicate a brand within each answer. Missing denominators are N/A. If weighting by estimated AI search volume, show the unweighted result and use a denominator summed across every tracked brand. Never use an “any brand appeared” denominator and label the result share.

Investigate misses

For prompts where competitors appear and the brand does not, inspect the actual cited pages and the brand's closest relevant page. /browser/markdown can help inspect rendered content, while /seo/serp provides conventional search context.

Classify evidence as:

Content and extractability: missing direct answers, ambiguous product facts, outdated comparisons, inaccessible text or confusing page structure. Preserve excerpts and URLs that show the difference.
Source credibility: absent evidence, unclear authorship or dates, unsupported claims, or a source that the engine appears to rely on repeatedly. Suggest original data and verifiable sources where relevant.
Third-party representation: a cited review, directory or discussion omits the brand. Propose legitimate participation or corrections where appropriate.

These are hypotheses to test, not proven causes of exclusion. Ranking in organic search does not prove a missing AI citation is a formatting problem. Structured data and llms.txt do not guarantee inclusion. Do not promise a percentage lift from historical GEO studies for this website.

Output

Deliver a dated table of prompt, engine, status, mention evidence, citation URLs, competitor evidence and request id. Follow it with per-engine metrics, collection limitations, total billed credits and an ordered list of investigations. Each recommendation needs an observed gap, a supporting source and a practical next check.

A name-only mention is a different observation from a citation, not an automatic “quick win”. Do not claim that the brand is absent from an engine merely because it was absent from a small sample or an indexed-corpus query.

Use llm-mention-tracking to collect repeatable future samples and ai-answer-gap to develop a content backlog. Public-data collection does not authorize publishing or contacting third parties.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

references/geo-methodology.md4 KB

Frequently asked questions

What does the Ai Visibility Audit AI skill do?

Why use Ai Visibility Audit on TypingMind?

Because you install it once and use it with any model. Ai Visibility Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Visibility Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ai Visibility Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Visibility Audit?

As many as you like. As long as a model supports skills, you can use Ai Visibility Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Visibility Audit AI skill free?

Yes. It is published on GitHub by unifapi-agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AI Visibility Audit

Workflow

Measure coverage and tracked-brand share

Investigate misses

Output

Bundled files

Frequently asked questions

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