aeo

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---
name: aeo
description: "Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools."
---

# Answer Engine Optimization (AEO)

**Get your content cited by ChatGPT, Perplexity, Claude, Gemini, and Mistral as the authoritative source.**

AEO is the practice of optimizing content for **citation** in LLM-generated responses — distinct from SEO, which optimizes for search rankings. This skill audits, optimizes, and tracks AEO performance.

## Distinct From SEO

| | SEO | AEO |
|---|---|---|
| **Optimizes for** | Click-through rankings | Being cited as authoritative source |
| **Audience** | Humans browsing search results | LLMs answering questions |
| **Success metric** | Position 1-10, organic traffic | Citation count across LLMs |
| **Key signals** | Backlinks, keywords, page speed | E-E-A-T, structured data, factual density |
| **Update cadence** | Weeks-to-months | Days-to-weeks (LLM training cycles) |

Both can coexist — the same content can rank #1 on Google AND get cited by Perplexity. But the techniques differ: SEO rewards keyword density + backlinks; AEO rewards primary-source signals + structured facts.

## When To Use

- Planning a new content piece for an AI-first audience
- Auditing existing content for E-E-A-T gaps before AI Overview rollout
- Tracking which pages get cited by which LLM (citation ledger)
- Researching what queries LLMs cite sources for (vs. what they answer from training)
- Benchmarking against competitors' citation rates
- Building a long-term AEO strategy aligned with traditional SEO

## When NOT To Use

- Pure click-through SEO without LLM-citation intent — use `marketing-skill/skills/seo-audit` instead
- Brand-voice content with no factual claims — citations require facts to cite
- Content for a topic where LLMs already have strong training signal (e.g., elementary math) — citation upside is minimal
- Time-sensitive content (breaking news) — LLM training lag means citations come months later

## Core Capabilities

### 1. Content audit + E-E-A-T scoring

The auditor (`aeo_audit.py`) scores content across 4 dimensions:

- **Experience**: First-person evidence, dated examples, case studies, "We ran X in 2026" claims
- **Expertise**: Author bio, credentials, citations to peer-reviewed sources, technical depth
- **Authoritativeness**: External backlinks from authority domains, schema.org markup, structured data
- **Trustworthiness**: HTTPS, contact info, transparent corrections, factual density (number of verifiable claims per 1000 words)

Composite score 0-100 with per-dimension breakdown. Output: markdown report with specific fix recommendations.

### 2. Content optimization

The optimizer (`aeo_optimizer.py`) generates AEO-improved variants:

- **Structure rewrite** — H2/H3 hierarchy optimized for LLM parsing
- **Citation density boost** — adds `[1]`-style references with sources
- **Schema injection** — generates JSON-LD for FAQ, HowTo, Article schemas
- **Fact-first lede** — moves verifiable claims into the first 200 words

Three modes: `conservative` (touch <10% of words), `balanced` (touch <30%), `aggressive` (rewrite for maximum AEO).

### 3. Citation tracking

The tracker (`citation_tracker.py`) maintains a local ledger of citations:

- Manual entry: paste a citation found in ChatGPT/Perplexity/Claude/Gemini output
- Track whi