Geo Fundamentals
wasp-lang/wasp
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
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.
Install AI Visibility Audit
$ npx skills add unifapi-agent/agents --skill ai-visibility-audit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-audit --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-audit .claude/skills/ai-visibility-audit && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ai-visibility-audit" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit into .claude/skills/ai-visibility-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-audit", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-auditType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add unifapi-agent/agents --skill ai-visibility-audit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-audit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-audit .agents/skills/ai-visibility-audit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-visibility-audit" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit into .agents/skills/ai-visibility-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-audit", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add unifapi-agent/agents --skill ai-visibility-audit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-audit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-audit .cursor/skills/ai-visibility-audit && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ai-visibility-audit" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit into .cursor/skills/ai-visibility-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-audit", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/unifapi-agent/agents.git --path skills/ai-visibility-agent/ai-visibility-audit--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add unifapi-agent/agents --skill ai-visibility-audit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-audit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-audit .gemini/skills/ai-visibility-audit && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ai-visibility-audit" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit into .gemini/skills/ai-visibility-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-audit", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install unifapi-agent/agents ai-visibility-auditInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add unifapi-agent/agents --skill ai-visibility-audit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-audit .github/skills/ai-visibility-audit && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ai-visibility-audit" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit into .github/skills/ai-visibility-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-audit", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add unifapi-agent/agents --skill ai-visibility-audit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install unifapi-agent/agents ai-visibility-audit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-visibility-agent/ai-visibility-audit .opencode/skills/ai-visibility-audit && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ai-visibility-audit" agent skill from https://github.com/unifapi-agent/agents/tree/main/skills/ai-visibility-agent/ai-visibility-audit into .opencode/skills/ai-visibility-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-visibility-audit", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ai-visibility-audit- GitHub stars
- 589
- Token cost
- ~1.2k tokens
- SKILL.md length
- 577 words
- Files
- 3 (incl. references)
- Skills in repo
- 47
- Repo updated
- First seen
- Licence
- MIT
At a glance
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.
- Works in 5 steps: Choose 10–30 prompts covering… → Use POST /geo/answers for ChatGPT or… → Use /geo/serp for Google AI Mode and… → …
- Wants to know whether their brand
- SKILL.md covers Workflow, Measure coverage and…, Investigate misses and Output
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
AI Visibility Audit is an agent skill from unifapi-agent/agents. 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…
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `README.md` and `references/geo-methodology.md`).
It sits in Marketing & SEO, covering AI search optimization. It works with OpenAI. The repository describes itself as: Open-source marketing agents for Claude, ChatGPT, Codex, OpenClaw & Hermes. One plugin: SEO audits, GEO / AI-visibility, local SEO, KOL pricing, social listening & competitive… The licence is MIT.
When your agent uses it
- Wants to know whether their brand
- Domain is cited in AI answers — AI Overviews
- AI search — for the queries that matter
- How they stack up against competitors
Example prompts
- “AI visibility audit,”
- “am I cited in AI answers,”
- “do I show up in ChatGPT,”
- “/ai-visibility-audit”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Choose 10–30 prompts covering definitions, comparisons, purchase criteria and concrete use cases. Freeze brands, aliases, citation…
- Use POST /geo/answers for ChatGPT or Gemini. Record engine, surface, exact prompt, search mode, reported model (or null), observation…
- Use /geo/serp for Google AI Mode and /seo/serp with include_ai_overview for Google Search AI Overviews. Inspect answer references; a…
- Keep corpus discovery separate. /geo/mentions/search, /top-domains and /top-pages identify indexed answers and frequently represented…
- Save raw normalized responses and billing. Use the runner in llm-mention-tracking/scripts/monitor.mjs for budgeted ChatGPT/Gemini…
What it can do on your machine
Read from SKILL.md and the folder at commit fb53247. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
AI Visibility Audit loads about 1.2k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 146 tokens; SKILL.md has 577 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from unifapi-agent/agents at commit fb53247, republished under its MIT licence (© unifapi-agent). 577 words, ~1,243 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-visibility-audit/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- ai-visibility-audit
- description
- 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.
- license
- MIT
- metadata.author
- UnifAPI
- metadata.version
- 1.1.0
- metadata.adapted_from
- https://github.com/coreyhaines31/marketingskills
- metadata.adapted_author
- Corey Haines
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/answersfor 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/serpfor Google AI Mode and/seo/serpwithinclude_ai_overviewfor 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-domainsand/top-pagesidentify indexed answers and frequently represented sources. They do not confirm the result of a particular live prompt./cross-aggregated-metricsreturns 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.mjsfor 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.
Show full SKILL.md (267 more words)Show less
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.
© unifapi-agent, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (references) in skills/ai-visibility-agent/ai-visibility-audit of unifapi-agent/agents.
- SKILL.md
- README.md
- references/geo-methodology.md
Open the folder on GitHubat commit fb53247
Compare with similar skills
AI Visibility Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Visibility Audit this skillunifapi-agent/agents | 589 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Geo Fundamentalswasp-lang/wasp | 19k | 9 repos | ~861 | Automated safety check: Pass | MIT | |
| SEO GeoReScienceLab/opc-skills | 1.8k | 4 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude | 11k | — | ~2.8k | Automated safety check: Notes | MIT | |
| SEO DataforseoAgriciDaniel/codex-seo | 799 | 2 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Fire Your SEO Agencyleopard627/fire-your-seo-agency | 711 | — | ~1.1k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about AI Visibility Audit
What does AI Visibility Audit do?
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. AI Visibility Audit is an agent skill from unifapi-agent/agents. 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.
When should I use AI Visibility Audit?
AI Visibility Audit fits situations like: wants to know whether their brand; domain is cited in AI answers — AI Overviews; AI search — for the queries that matter; how they stack up against competitors.
How do I install AI Visibility Audit in Claude Code?
Run `npx skills add unifapi-agent/agents --skill ai-visibility-audit -a claude-code`. Or copy the skill folder (skills/ai-visibility-agent/ai-visibility-audit in unifapi-agent/agents) into .claude/skills/ai-visibility-audit in your project. Claude Code loads it when a task matches its description.
How do I install AI Visibility Audit in Codex?
Run `npx skills add unifapi-agent/agents --skill ai-visibility-audit -a codex`. Or copy the skill folder (skills/ai-visibility-agent/ai-visibility-audit in unifapi-agent/agents) into .agents/skills/ai-visibility-audit in your project. Codex loads it when a task matches its description.
Can I use AI Visibility Audit in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add unifapi-agent/agents --skill ai-visibility-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-visibility-audit, .gemini/skills/ai-visibility-audit, .github/skills/ai-visibility-audit and .opencode/skills/ai-visibility-audit in your project.
What does AI Visibility Audit need to run?
SKILL.md names no scripts, command-line tools or credentials: AI Visibility Audit is instructions for the agent only.
Does AI Visibility Audit access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is AI Visibility Audit safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does AI Visibility Audit use?
AI Visibility Audit is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does AI Visibility Audit use?
About 1.2k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1k tokens, read only when the agent opens those files.
What are the alternatives to AI Visibility Audit?
Skills that share tags, products or a category with AI Visibility Audit: Geo Fundamentals (wasp-lang/wasp, 19k stars), SEO Geo (ReScienceLab/opc-skills, 1.8k stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars) and SEO Dataforseo (AgriciDaniel/codex-seo, 799 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Visibility Audit?
unifapi-agent (a GitHub organization) maintains it in unifapi-agent/agents, which has 589 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on September 5, 2026.
Source: unifapi-agent/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.