Answer Engine Optimization (AEO)

Distinct From SEO

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

aeo_audit.py

• 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)

2. Content optimization

aeo_optimizer.py

• 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

conservative

balanced

aggressive

3. Citation tracking

citation_tracker.py

• 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

~/.aeo-data/citations.json

Workflow

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

Configuration

--industry

saas

healthcare

finance

legal

ecommerce

b2b

media

education

• Authority signal requirements — healthcare/finance need stricter source citations
• Fact-checking rigor — legal/healthcare flag unverifiable claims as critical
• Citation style — academic vs. trade-journal vs. blog conventions

python3 scripts/aeo_audit.py --url <url> --industry healthcare
# → stricter E-E-A-T thresholds; flags any health claim without primary citation

Output Format

Markdown audit report (default)

# AEO Audit Report — [Page Title]

**URL:** https://example.com/blog/post
**Date:** 2026-05-17
**Industry:** saas
**Composite Score:** 72/100 (B+)

## Dimension Breakdown

| Dimension | Score | Verdict |
|---|---|---|
| Experience | 80/100 | Strong — first-person case study present |
| Expertise | 65/100 | Author bio missing credentials |
| Authoritativeness | 75/100 | 4 backlinks from authority domains |
| Trustworthiness | 68/100 | No corrections policy linked |

## Top 3 Fixes

1. Add author bio with credentials (Expertise +15)
2. Link to corrections policy from footer (Trustworthiness +12)
3. Inject FAQ schema for the 5 questions implicit in H2s (Authoritativeness +8)

## All Recommendations
[...]

## Audit Trail
[3-count of analysis steps, sources cited, time taken]

JSON for pipelines

python3 scripts/aeo_audit.py --url <url> --output json

Industry-Specific E-E-A-T Thresholds

Anti-Patterns Rejected

• Keyword stuffing for AI — LLMs already extract topic from semantics; keyword density doesn't boost citation likelihood
• Pure AI-generated content with no human review — generic LLM output gets de-prioritized by RAG retrieval algorithms looking for distinctive signal
• Citation farms / link wheels — modern LLM RAG penalizes low-authority linked networks
• Schema spam — false or unverifiable schema.org claims get filtered; only mark up real, verifiable claims
• Optimizing for one LLM at expense of others — citation distributions are highly correlated across major LLMs because they share training data sources; optimize for the shared signals (E-E-A-T) not per-LLM hacks
• Ignoring SEO entirely — AEO citations often originate from sources that already rank well organically; AEO and SEO are complements, not substitutes

Dependencies

• stdlib-only for all 3 scripts — no

  pip install

  required
• Optional:

  requests

  +

  beautifulsoup4

  if

  --url

  mode used (otherwise pass markdown via

  --input

  for file-based audits)
• Optional: any LLM API key for

  query_research

  mode (currently scaffold-only — full LLM-driven query research is roadmap)

Storage

• ~/.aeo-data/citations.json

  — citation ledger

• ~/.aeo-data/patterns.json

  — success patterns library

• ~/.aeo-data/audits/<hash>.md

  — saved audit reports

citation_tracker.py --action export

Trigger Phrases

• "AEO audit", "AEO check"
• "optimize for ChatGPT / Perplexity / Claude / Gemini"
• "get cited by [LLM]"
• "LLM citation strategy"
• "answer engine optimization"
• "content for AI search"
• "E-E-A-T audit"
• "track AI citations"
• "schema for AI"

Related Skills

• marketing-skill/skills/seo-audit

  — traditional click-through SEO

• marketing-skill/skills/programmatic-seo

  — template-driven SEO at scale

• marketing-skill/skills/content-strategy

  — broader content planning

• marketing-skill/skills/copywriting

  — voice + tone

• marketing-skill/skills/schema-markup

  — structured data implementation

alirezarezvani/aeo-box

answer-engine-optimization/