Aeo

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).