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ai-visibility-sampling

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Use this skill when an AI-visibility number needs to be trusted, defended, or compared over time — "is this score real," "the engine said something different an hour later," "did our AI visibility actually move this week." Re-asks every question multiple times per engine, majority-votes presence, and keeps the verbatim answers as receipts, so every claim traces to evidence.

skills/huxley-peckham/ai-visibility-sampling/SKILL.md

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Use when brand presence in AI answers is being scored, compared, or reported over time. Produces a per-question presence verdict with vote counts, backed by the full answer text as evidence.

## Sample before scoring

Answer engines are stochastic: the same question, asked twice in the same hour, returns different answers with different names in them. A score built from one ask per question is noise wearing a number. Ask every question at least three times per engine, capture each full answer verbatim, and let a majority vote decide presence. Keep the vote visible in the output — a question won three-of-three is a different fact from two-of-three, and the margin is where next month's movement shows first.

## Define what counts as present

Three different events hide inside "the brand showed up": named as a recommendation (the engine offers the brand as an answer to the buyer's question), mentioned in passing (the name appears without endorsement), and cited as a source (the brand's site fed the answer). Decide the tiers before scoring and apply them mechanically. The strictest tier — named as a recommendation — is the one that predicts buyers arriving; report it separately, never blended.

## Score questions, not brands

One aggregate score hides everything an operator can act on. Build the readout as a grid: question by engine, vote count in each cell. The aggregate can sit on top, but the grid is the product — it shows which questions are won, which are contested, and which engine disagrees with the rest.
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