Agent-Skills.md

Agent Skills: Answer Engine Optimization (AEO)

>

[aeoanswer-engine-optimizationllm-citationgenerative-searchai-contentschema-qageollm-seo]
marketingID: borghei/claude-skills/aeo

Repository

borghei/claude-skills
borgheiLicense: NOASSERTION
34669

Install this agent skill to your local

pnpm dlx add-skill https://github.com/borghei/Claude-Skills/tree/HEAD/marketing/aeo

Skill Files

Browse the full folder contents for aeo.

Download Skill

Loading file tree…

marketing/aeo/SKILL.md

Skill Metadata

Name
aeo
Description
>

Answer Engine Optimization (AEO)

End-to-end practice of optimizing content to be cited by LLMs when they generate answers. Covers the technical foundations (how LLMs select sources), content structuring patterns (Q&A schema, citation-worthy patterns), measurement (which content gets cited, by which LLM, how often), and the strategic positioning that differentiates AEO from traditional SEO and from AI-SEO.

This skill is provider-aware but provider-agnostic: works for content optimized for ChatGPT, Claude, Perplexity, Gemini, Copilot, and emerging AI surfaces.

When to use this skill

| Situation | Skill applies | |-----------|---------------| | Designing content strategy that targets LLM citation | Yes — start with AEO fundamentals | | Auditing existing content for LLM citability | Yes — scripts/aeo_content_auditor.py | | Adding Q&A schema to content | Yes — scripts/schema_qa_generator.py | | Tracking which content gets cited by LLMs | Yes — scripts/citation_extractor.py | | Choosing between AEO and traditional SEO investment | Yes — see AEO vs SEO vs AI-SEO | | Ranking in Perplexity / Google AI Overviews | Use marketing/ai-seo | | Traditional SEO (rank in Google search results) | Use marketing/seo-specialist |

AEO vs SEO vs AI-SEO

Three distinct (but overlapping) practices. Confusing them leads to wasted investment.

| Practice | Optimizes for | Surface | Success metric | |----------|---------------|---------|----------------| | Traditional SEO | Google / Bing rankings | SERPs (organic blue links) | Position, clicks | | AI-SEO | AI search engines | Perplexity, Google AI Overviews, You.com | Position in AI search results, traffic from citations | | AEO (this skill) | LLM citation in answers | ChatGPT, Claude, Gemini, Copilot answers | Citation rate, brand mention in LLM outputs |

Strategic positioning

For most B2B brands:

  • Traditional SEO: still 50-70% of organic traffic. Don't abandon.
  • AI-SEO: emerging 10-20% of search-driven engagement. Growing fast.
  • AEO: 5-15% of LLM-mediated user discovery. Largest growth potential.

Optimize content for all three simultaneously; the techniques substantially overlap.

The AEO funnel

Users find brands through LLMs in a different funnel than search:

Traditional search:           AEO funnel:
1. User types query           1. User asks LLM a question
2. SERPs show ~10 results     2. LLM generates answer
3. User clicks one            3. LLM cites N sources (1-10)
4. User reads page            4. User reads answer; may click cited source
5. User converts              5. User attributes answer to LLM (less so to cited brand)

Key implications:

  • Citation is the new click. When LLM cites your content, you don't always get a visit — but you get attribution.
  • Brand-as-source becomes the goal. Even without click, being cited builds brand association.
  • Quality > volume. LLMs cite a small number of sources; quality of citation matters more than ranking position.
  • Trust signals matter more. LLMs avoid citing low-authority sources.

See references/aeo-fundamentals.md for the deep mechanics of how LLMs select sources, the citation models per provider, and the trust signals that drive selection.

The 5 content patterns that get cited

After analysis of LLM citation behavior, five content patterns dominate:

Pattern 1: Definitional content with clear claims

LLMs cite sources for definitions, facts, and short claims. Pages that answer "What is X?" with a clean 2-3 sentence definition followed by elaboration get cited often.

Structure:

[Term] is [crisp definition in 1-2 sentences].

[Elaboration with context and nuance — 1-3 paragraphs].

[Related concepts / scope / boundaries — optional].

Pattern 2: Comparative tables

LLMs use tables to extract comparisons. Markdown tables in published content (or HTML equivalents) get cited when users ask "X vs Y."

| Feature | Product A | Product B |
|---------|-----------|-----------|
| Price | $X | $Y |
| Speed | Z ms | W ms |
| Support | 24/7 | Business hours |

Pattern 3: Step-by-step procedural content

"How to [task]" content with explicit numbered steps. LLMs reproduce procedural steps; the cited source becomes the authoritative reference.

Pattern 4: Statistics + data with sources

LLMs cite content that provides numerical facts with attribution. "According to [your study], X% of [thing] does Y" is repeatable and citable.

Pattern 5: Lists with explanations

"Top N approaches to X" with each item explained gets cited when users ask comparative or enumeration questions.

See references/llm-content-structuring.md for deep patterns including FAQ schema, citation hooks, voice-search optimization, and LLM-readable structure markers.

Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • [ ] Target queries — the actual questions customers ask LLMs about your category (drives which content to audit and restructure)
  • [ ] Your brand name — exact wording to track in answers vs competitors (drives citation extraction)
  • [ ] Target LLM surface — ChatGPT / Claude / Perplexity / Gemini (citation behavior and trust signals differ per provider)
  • [ ] Canonical page/content — the high-value page to be the authoritative source (drives schema generation + pattern restructuring)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick start

  1. Audit existing content: python3 scripts/aeo_content_auditor.py --path ./content
  2. Add Q&A schema to high-value pages: python3 scripts/schema_qa_generator.py --content article.md
  3. Track citations from competitors: python3 scripts/citation_extractor.py --query "What is X?" --brand "Your Brand"
  4. Iterate: monthly content review with AEO scoring

End-to-end workflows

Workflow: AEO content strategy from scratch

  1. Identify target queries — what questions do potential customers ask LLMs about your category?
  2. Audit competitor citations — which brands get cited for those queries? scripts/citation_extractor.py
  3. Audit your existing content — score current content for AEO patterns: scripts/aeo_content_auditor.py
  4. Prioritize 10-20 high-value pages — those that should be the canonical source
  5. Restructure per AEO patterns — definitional content, tables, step-by-step, statistics
  6. Add structured data — scripts/schema_qa_generator.py generates FAQ schema
  7. Build authority signals — backlinks, citations, mentions
  8. Monitor monthly — track citation rate trend

Workflow: Audit individual content piece

  1. Run scripts/aeo_content_auditor.py --path article.md --format markdown
  2. Review per-pattern scoring (5 patterns above)
  3. Identify gaps: missing definition, no table, no clear steps, no stats, no list
  4. Restructure to add 2-3 missing patterns
  5. Add FAQ schema with scripts/schema_qa_generator.py
  6. Re-audit to confirm improvements

Workflow: Competitive citation analysis

  1. Identify 10-20 key queries in your category
  2. Query each LLM (ChatGPT, Claude, Perplexity, Gemini) with those questions
  3. Record citations + brands mentioned
  4. Analyze: which brands dominate? what content do they have?
  5. Identify white-space queries (no clear dominant source yet)
  6. Prioritize content creation for white-space queries

Workflow: Measure AEO performance

  1. Citation rate: % of queries where your brand is cited (target: 30%+ for category leaders)
  2. Brand mention rate: % of queries where your brand is mentioned (cited or not)
  3. Source quality: are you cited as primary source or supporting?
  4. Click-through from citations: traffic attributable to LLM citations (requires source tracking)
  5. Voice tracking: how is your brand characterized (positive / neutral / negative attributes)

See references/citation-tracking-and-measurement.md for measurement methodologies, attribution challenges, and competitive benchmarking.

Common AEO failures

  • Optimizing only for Google SERP: misses the LLM citation surface entirely
  • Generic content without specific claims: LLMs prefer specific, factual content over generic explanation
  • No structure markers (headings, lists, tables): LLMs can't extract specific information
  • No FAQ schema: missed opportunity for Q&A surfacing in AI Overviews
  • Stuffed keyword content: LLMs prefer natural language with clear meaning
  • No authority signals: LLMs avoid citing low-trust sources
  • Outdated content: LLMs prefer recent, current content
  • Hidden behind paywalls: LLMs can't cite what they can't access
  • No structured data: missed opportunity for richer extraction
  • Brand-first content: LLMs prefer informational content over promotional

LLM-by-LLM citation behavior

Different LLMs have different citation behaviors:

| LLM | Citation style | What gets cited | |-----|----------------|-----------------| | ChatGPT | Inline citations (when web-enabled); fewer otherwise | Recent, authoritative sources | | Claude | Citations when grounding enabled (tools); generally avoids unsupported claims | High-quality sources, evidence-based | | Perplexity | Always cites sources prominently | Recent + authoritative sources | | Google Gemini / AI Overviews | Cites in AI Overviews + Gemini responses | High-ranking pages + structured data | | Copilot (Microsoft) | Cites sources prominently | Sources varied | | Meta AI | Lighter citation | Limited transparency |

Optimize content with structure markers (headings, lists, tables) and authority signals (links, citations, expert attribution) — works across all of these.

Tooling

| Script | Purpose | |--------|---------| | scripts/aeo_content_auditor.py | Score content for AEO patterns (definition, table, steps, stats, list, structure markers) | | scripts/citation_extractor.py | Parse LLM responses (saved transcripts) for brand citations + competitive analysis | | scripts/schema_qa_generator.py | Generate JSON-LD FAQ schema from content (FAQPage / QAPage / HowTo) |

References

Related skills

  • marketing/ai-seo — AI search engine ranking (Perplexity, Google AI Overviews); complementary to AEO
  • marketing/seo-specialist — traditional SEO (Google rankings); foundational; still 50-70% of organic
  • marketing/seo-audit — technical SEO audit
  • marketing/programmatic-seo — scaled content production with SEO patterns
  • c-level-advisor/cs-cmo-advisor — strategic AEO investment decisions