/aeo

name: aeo
description: This skill should be used when the user asks to "build an AEO strategy", "improve AI search visibility", "track buyer prompts", "analyze AI citations", or "plan content for answer engines". Apply an evidence-led answer engine optimization (AEO/GEO) feedback loop to any product or service, prioritizing discovery by buyers who have not heard of it.

Answer engine optimization

Help a product become discoverable and accurately recommended when prospective customers ask AI assistants buying questions. Adapt to software, physical goods, consumer products, services, marketplaces, and local businesses. Default to acquiring people who have not heard of the product. Success means the answer introduces the product or cites useful owned content without the buyer naming it.

Use the loop: buyer question → observed answer and sources → diagnosed gap → page or source improvement → repeated measurement → customer outcome.

Scope and evidence

Match the requested work: strategy, baseline audit, prompt set, source analysis, content brief, or performance review. Keep explicitly requested branded accuracy or conversion work scoped to that purpose. Use existing product material, research, analytics, and monitoring exports before requesting missing inputs. Ask only for information that changes the next decision. With no analytics or answer-engine access, produce a proposed measurement plan and label assumptions. Never fill missing observations with invented results.

Treat case-study growth figures as hypotheses to investigate, not expected returns. Neither a fixed prompt count nor a particular engine's traffic share transfers between products. AEO guidance alone does not authorize publishing, outreach, paid monitoring, or recurring jobs.

1. Define the buying context

Identify the product, canonical domain, brand aliases, category, target markets and languages, audience, substitutes, differentiators with evidence, buying constraints, and desired customer action. Distinguish the buyer, user, and decision-maker where useful. For consumer or local purchases, use the shopper's situation, location, and constraints instead of inventing enterprise personas.

Define one primary outcome, such as a paid order, qualified inquiry, booking, or activated trial. Keep intermediate events such as page views and signups separate. Record the normal purchase or sales lag and the available attribution window.

2. Build a prompt portfolio

Start with observed language from support questions, sales conversations, reviews, on-site search, search queries, and customer research. Separate observed questions from generated candidates. Group prompts by audience or purchase situation, journey stage, topic, use case, constraints, market, and language.

Build the acquisition portfolio around problems, desired outcomes, categories, constraints, and alternatives. A buyer can be ready to purchase without knowing the product name. Useful patterns include:

• What is the best way to achieve an outcome under a specific constraint?
• Which product category fits a particular audience or situation?
• How do two approaches compare for a concrete use case?
• Which options meet a requirement, budget, location, or compatibility need?
• What tradeoffs, costs, proof, or limitations should a buyer check?

Use natural customer language and separate these cohorts:

| Cohort | Buyer knowledge and prompt | Purpose | | --- | --- | --- | | Problem or category discovery | Does not know the target product. Asks how to solve a problem or find a suitable option. | Measure whether answers introduce the product without naming it in the prompt | | Competitor alternatives | Knows an alternative. Asks for alternatives to it without naming the target product. | Measure acquisition from competitor-aware buyers, reported separately from category discovery | | Branded evaluation | Already knows the target product. Names it to ask about price, support, suitability, or comparisons. | Check accuracy and support conversion, excluded from acquisition visibility rates |

Exclude the target brand, aliases, domain, and unique product names from acquisition prompts and any supplied conversation context. Use fresh conversations without target-brand seeding. Competitor names, compatible platforms, and subjects such as a movie title can be natural buying context. Include poor-fit cases to check recommendation accuracy, without rewarding irrelevant recommendations.

Distinguish two acquisition paths: a solution question can lead to a product recommendation, while a contextual question can lead to a useful owned guide where the reader discovers the product. A guide citation is not a product recommendation. Map each acquisition prompt to its intended path and next customer action.

Choose coverage based on distinct buying decisions and collection cost. Deduplicate paraphrases that add no new intent. Keep a stable core for trend comparisons and a separate exploratory set for new questions. Version additions and removals so expanding coverage cannot masquerade as a visibility change.

3. Prioritize channels and collect a baseline

Review available referrals, conversions, customer acquisition surveys, and customer research. Prioritize engines that bring relevant customers or that the audience demonstrably uses. With no channel evidence, run a small discovery sample across plausible engines before committing the budget.

Estimate cost as prompts × engines × repetitions × collection frequency, plus research and maintenance. Start within the user's budget. Use existing exports or a small manual sample before suggesting a paid stack. Automated collection must use supported access and respect provider limits.

For each observation, retain the exact prompt, timestamp, engine and model or mode when visible, locale, relevant account or personalization context, answer evidence, brand mentions, explicit recommendations, and cited URLs. Do not store credent