/aeo

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 which URL, which LLM, which query, what date
• Compute per-page citation count, citation velocity, LLM coverage
• Export to CSV for reporting

Stores in `~/.aeo-data/citations.json` (local, no telemetry).

References

• `references/aeo_eeat_canon.md` — E-E-A-T methodology, industry thresholds, anti-patterns
• `references/llm_citation_patterns.md` — per-LLM citation selection heuristics (Perplexity, ChatGPT, Claude, Gemini, Mistral)
• `references/aeo_vs_seo.md` — when to invest in AEO vs SEO vs both
• `references/bot_access_and_monitoring.md` — AI crawler robots.txt matrix (the prerequisite check: a blocked bot zeroes that platform), Google Search Console AI Overviews monitoring, manual testing protocols, citation-drop diagnostic (merged from the former `ai-seo` skill)
• `references/extractable_content_patterns.md` — 7 copy-ready block templates (definition, steps, table, FAQ, attributed stat, expert quote, summary box) that answer engines reliably extract (merged from the former `ai-seo` skill)

Workflow

0. Pre-flight: bot access
   Check robots.txt against the crawler matrix in references/bot_access_and_monitoring.md
   → a blocked GPTBot/PerplexityBot/ClaudeBot/Google-Extended is the first fix, always

1. Audit existing content
   $ python3 scripts/aeo_audit.py --url https://example.com/blog/post
   → markdown report with composite score + 4-dimension breakdown

2. Apply optimization recommendations
   $ python3 scripts/aeo_optimizer.py --input post.md --mode balanced --output post-aeo.md
   → optimized variant with citations + schema + structural fixes

3. Publish + monitor
   $ python3 scripts/citation_tracker.py --action add --url https://example.com/blog/post \
       --llm perplexity --query "what is AEO" --date 2026-05-17
   → adds entry to local citations.json ledger

4. Report
   $ python3 scripts/citation_tracker.py --action report --url https://example.com/blog/post
   → per-page citation stats: count, LLMs, queries, velocity

Configuratio