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

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).

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

The skill is industry-aware via per-run --industry flag. Supported: saas, healthcare, finance, legal, ecommerce, b2b, media, education.

Industry affects:

• 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

Example:

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

Returns full structured data for integration with content management workflows.

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

All data is local-first:

• ~/.aeo-data/citations.json — citation ledger
• ~/.aeo-data/patterns.json — success patterns library
• ~/.aeo-data/audits/<hash>.md — saved audit reports

No telemetry. No cloud sync. Export to CSV anytime via 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

Version: 2.7.3 Source: Ported from alirezarezvani/aeo-box (answer-engine-optimization/ skill, 2,464 LOC across 9 modules). This port distills the 9-module Python toolkit into 3 stdlib CLI tools per the claude-skills convention; preserves the E-E-A-T scoring methodology, citation-tracking schema, and industry-aware thresholds verbatim. License: MIT (matches upstream + this repo).