AI Search Visibility skill
The tracking method Okara’s GEO Agent uses to measure brand presence inside AI answers over time — mention rate, share of voice, sentiment, and cited sources.
Download the methodology, see how the agent tracks visibility month over month, or run it live inside the GEO Agent.
- Category
- GEO
- Agent
- GEO Agent
- Format
- ai-search-visibility.md
- Price
- Free to download
The skill file
--- name: ai-search-visibility description: Track how visible your brand is inside AI answers over time — share of voice against competitors, sentiment, cited sources — and act on the changes. owner: Okara GEO Agent --- # AI Search Visibility Visibility in AI answers is a tracked metric, not a one-time audit. Answers change as models update and as the sources they cite change. This skill sets up the measurement and defines what to do when the numbers move.
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About this skill
What is the AI Search Visibility?
The AI Search Visibility skill is the exact approach Okara’s GEO Agent follows to turn AI answer presence into a tracked metric. It fixes a question set and never changes it, measures mention rate, position, accuracy, and citation share across engines, and reports share of voice against named competitors — the number a leadership team can actually act on.
It’s written as a SKILL.md, a plain-language instruction format that people and AI agents can both follow. Use it after a GEO audit, when you need to report on this channel, or when a competitor starts appearing in answers you used to own.
Downloading the instructions is free with an Okara account. Running the skill continuously — monthly measurement, accuracy alerting, and source monitoring — is part of the GEO Agent.
Example
See it in action
You ask: “Our CEO wants a monthly number for how visible we are in AI search. What do we report?”
Agent does: It sets up four metrics on a fixed question set — mention rate, position within the answer, description accuracy, and whether your own domain is the cited source — and locks the question set, because adding questions mid-quarter destroys comparability and that is the whole point. The headline number for leadership is share of voice: your mention rate against named competitors on identical questions, since absolute presence means little without the comparison. It segments by category, comparison, and brand queries, because a blended figure hides whichever segment is actually moving. Engine-level differences get logged as diagnostics rather than noise — appearing in Perplexity but not ChatGPT usually tells you which sources each is weighting. Accuracy regressions get their own alert and outrank lost mentions, since a model repeating a retired price spreads across engines. And every report states plainly that generated answers are non-deterministic, so some movement is variance rather than effect.
How it works
From input to output
- 1
You fix the question set
Define your brand, competitors, and the buyer questions that matter — then leave the set alone.
- 2
Okara measures across engines
The GEO Agent tracks mention rate, position, accuracy, and citation share on every engine, monthly.
- 3
It reports share of voice
Your presence against named competitors, segmented by category, comparison, and brand questions.
- 4
You act on deltas and alerts
Month-over-month change with noise caveats, accuracy regressions flagged, and an action log tying movement to what you shipped.
What's included
Everything it covers
Mention rate & position
How often you appear, and whether you are named first or fifth.
Share of voice
Your presence against named competitors on identical questions — the leadership metric.
Accuracy alerts
Flags models stating wrong prices or discontinued features, which outrank lost mentions.
Source monitoring
Watches the cited domains, which move before the answers do.
Question-type segmentation
Category, comparison, and brand queries tracked separately so movement isn’t hidden.
Delta reporting
Month-over-month change with honest noise caveats, tied to an action log.
Use cases
When to reach for it
- Report AI channel performance to a leadership team with a real number.
- Detect when a model starts describing your pricing or features wrongly.
- See a competitor taking share of voice in answers you used to hold.
- Attribute movement to the work you shipped rather than guessing.
- Turn a one-off GEO audit into an ongoing measurement.
Frequently Asked Questions
Related skills
Pair it with these skills
AI Overviews
The GEO playbook Okara’s GEO Agent uses to get your brand cited in AI answers across ChatGPT, Perplexity, Gemini, and Google AI Overviews.
--- name: ai-overviews description: Optimize a page to get cited in AI answers — Google AI Overviews, ChatGPT, Perplexity, and Gemini — not just ranked in blue links. owner: Okara GEO Agent --- # AI Overviews Generative Engine Optimization (GEO): make your brand the source AI assistants cite when they answer questions in your space, not just a link on page one.
llms.txt
The GEO playbook Okara’s GEO Agent uses to publish and maintain an llms.txt file — the machine-readable index that tells AI crawlers and assistants which of your pages matter and what they mean.
--- name: llms-txt description: Create and maintain an llms.txt file so AI crawlers and assistants can find, understand, and cite your most important content. owner: Okara GEO Agent --- # llms.txt A curated, machine-readable index at the root of your site that tells AI crawlers and assistants which pages matter and what they mean — so your best content is the content models read, trust, and cite.
AI Detection
The GEO playbook Okara’s GEO Agent uses to check whether your content reads as AI-generated and reshape it into natural, high-quality writing that protects your brand and earns AI citations.
--- name: ai-detection description: Check whether content reads as AI-generated and reshape it into natural, high-quality writing that protects your brand and earns AI citations. owner: Okara GEO Agent --- # AI Detection Detect the patterns that make writing read as machine-generated, then fix them — so your content sounds genuinely written, holds up to scrutiny, and is the kind of source AI assistants trust and cite.