aeo

Answer Engine Optimization (AEO)

A source-disciplined implementation methodology for making useful, attributable answers discoverable and reusable by AI search and answer systems without treating vendor folklore as a ranking guarantee.

When to use

Load this skill when the work involves:

• planning or implementing AEO, GEO, LLMO, AI-search visibility, or citation visibility;
• turning a topic into question clusters, answer-first pages, evidence blocks, or reusable knowledge assets;
• implementing or validating answer-oriented HTML, JSON-LD, llms.txt, crawler controls, sitemaps, freshness signals, or Markdown delivery;
• designing prompt sets, citation observation logs, share-of-voice experiments, or AI-answer measurement;
• translating an AEO audit into a bounded implementation plan.

Do not load this skill alone for broad technical SEO, keyword research, page-speed work, CMS administration, or ordinary copy-editing. Route those concerns to seo-audit, a platform skill, or the appropriate writing skill. AEO cannot guarantee inclusion, ranking, citation, or traffic.

When not to use

Do not use this skill alone for broad technical SEO, keyword research, page-speed work, CMS administration, or ordinary copy-editing. Route those concerns to seo-audit, a platform skill, or the appropriate writing skill.

Operating model

AEO is an implementation loop, not a bag of hacks:

1. Scope the audience, entities, questions, answer surfaces, business outcome, and content boundary.
2. Research primary platform guidance and the subject's authoritative evidence.
3. Map question clusters to pages and record the intended answer, evidence, entity, freshness, and owner.
4. Implement human-useful content first, then machine-readable structure and discovery controls that the target systems actually support.
5. Measure with a frozen prompt set and platform-native data where available. Record the full answer, citations, URL, date, model/surface, and failure mode.
6. Learn from changes with a bounded experiment. Keep SEO fundamentals and AEO-specific hypotheses separate.

Read references/implementation-playbook.md for the full workflow and completion gate. Read references/evidence-boundaries.md before adopting a tactic or making a performance claim.

Decision rules

• Answer first, not AI-first: Put a concise, accurate answer near the relevant heading, followed by qualifications, evidence, and useful detail. Do not distort prose into fragments or keyword variants.
• One question, one canonical answer location: Consolidate duplicate answers, link related questions, and make ownership and update responsibility explicit.
• Evidence beats assertion: Cite primary sources, state scope and date, preserve uncertainty, and distinguish observation, vendor claim, inference, and experiment result.
• Visible content is the contract: JSON-LD, llms.txt, metadata, and summaries must agree with the rendered page. Structured data does not create facts.
• Access is a choice: Robots directives control access only where the relevant crawler honors them. Separate search access, training access, user-triggered fetching, and commercial permissions.
• No universal AEO signal: A tactic supported by one provider, experiment, or tool is not a cross-engine law. Label provider scope and confidence.
• Freshness must be earned: Change dates, lastmod, and update notices only when the underlying content changed. Never manufacture freshness.
• Measure citation quality, not only count: A citation that is irrelevant, stale, misattributed, or contradicted is a defect even when the count rises.

Reference routing

Need	Read
What AEO is, what it is not, and evidence confidence	references/evidence-boundaries.md
End-to-end implementation sequence and acceptance gate	references/implementation-playbook.md
Question/topic maps, answer blocks, entity and evidence design	references/content-and-entity-architecture.md
JSON-LD, visible parity, schema selection, validation	references/structured-data.md
Crawlers, robots controls, sitemaps, IndexNow, freshness	references/discovery-and-freshness.md
llms.txt, Markdown delivery, and provider-specific support	references/agent-readable-content.md
Prompt sets, citation logs, metrics, experiments, and limits	references/measurement-and-experimentation.md
Platform-specific primary guidance and refresh points	references/platform-guidance.md
Provenance, authority tier, access date, and claim ledger	references/source-index.md

Reusable assets

• templates/aeo-implementation-plan.md — scope, hypotheses, changes, owners, risks, and verification.
• templates/question-cluster.md — question-to-page and evidence mapping.
• templates/citation-observation-log.md — reproducible answer/citation observations.
• templates/llms.txt.template — optional proposal-format llms.txt, clearly marked non-universal.
• templates/robots-ai-crawlers.txt — decision-oriented crawler policy template; do not copy without reviewing provider semantics.
• scripts/aeo_audit.py — read-only HTML/URL structural audit with JSON output.
• scripts/build_prompt_matrix.py — deterministic prompt matrix generator from a topic YAML/JSON file.
• scripts/test_aeo_scripts.py — offline tests for the bundled scripts.

Run bundled scripts non-interactively from the skill directory. They never publish, edit, submit URLs, call an LLM, or change robots policy.

Routing to and from SEO

Use seo-audit for broad crawlability, indexability, on-page SEO, schema eligibility, and site-level search audits. When its work reaches AI-answer structure, citation measurement, provider-specific crawler semantics, or llms.txt, load this skill. Conversely, use this skill to identify AEO changes, then route platform mutations and general SEO remediation to the relevant existing skill.

Common failure modes

• Treating Google AI Overviews guidance as proof of how ChatGPT, Perplexity, or every answer engine works.
• Calling llms.txt, FAQ schema, headings, short paragraphs, or “mentions” guaranteed ranking or citation levers.
• Inventing question pages at scale, duplicating near-identical answers, or adding boilerplate that helps neither people nor retrieval.
• Publishing schema or summaries that say more than the visible content proves.
• Using third-party citation counts without the prompt set, date, surface, model, URL, and retrieval method.
• Confusing being mentioned, being cited, ranking, receiving a click, and producing a conversion.
• Disallowing a crawler without separating search visibility from training or user-triggered fetch behavior.
• Calling a script or validator “AEO complete” without checking the rendered page and an answer surface.

Verification checklist

• Scope names target entities, questions, surfaces, outcomes, and exclusions.
• Primary provider guidance is current and its limits are recorded.
• Every target question maps to one canonical page/section and a responsible owner.
• Opening answers, evidence, caveats, links, and update dates are visible and accurate.
• Structured data parses and matches visible content; unsupported types are not added for decoration.
• Robots, sitemap, and freshness changes were previewed and verified at the public boundary.
• Optional files such as llms.txt are labeled as proposals or provider-specific aids, not universal requirements.
• Prompt observations preserve exact answers and citations, with a frozen test set and access dates.
• Completion distinguishes implemented, verified, observed, inferred, and unresolved claims.