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name: "cs-aeo" description: "/cs:aeo — Answer Engine Optimization workflow. Audit content for E-E-A-T + structure signals that drive LLM citation (ChatGPT, Perplexity, Claude, Gemini, Mistral). Optimize content in 3 modes (conservative/balanced/aggressive). Track which LLMs cite which pages via local ledger. Industry-aware thresholds (8 industries with YMYL calibration). Distinct from SEO — refuses to optimize one at expense of the other."

/cs:aeo — Answer Engine Optimization

Command: /cs:aeo [action] [args]

The cs-aeo command is the entry point for AEO workflows: audit → optimize → publish → track citations.

Distinct From /cs:seo-audit

These share a foundation (E-E-A-T) but optimize for different conversion events:

• /cs:seo-audit — optimizes for ranking + click-through in Google/Bing search results
• /cs:aeo (this command) — optimizes for being cited as authoritative source by LLMs

They can run on the same content. The cs-aeo agent will surface this and recommend running both for high-leverage pages.

When To Run

• Auditing existing content for AI-search readiness (E-E-A-T + structure signals)
• Optimizing a page for LLM citation before publishing
• Tracking which LLMs cite which pages over time (citation ledger)
• Researching whether AEO investment is worth it for a given content piece
• Benchmarking against competitor citation rates

When NOT To Run

• Pure click-through SEO without AI-citation intent → use /cs:seo-audit
• Brand-voice content with no factual claims (citations require facts)
• Time-sensitive news (LLM training lag means citation comes months later)
• Topics where LLMs already have strong training (e.g., elementary math)

Actions

audit — Score content for AEO readiness

/cs:aeo audit --input post.md --industry saas
/cs:aeo audit --url https://example.com/blog/post --industry healthcare
/cs:aeo audit --sample

Returns composite 0-100 with per-dimension breakdown (E-E-A-T + Structure) and top 5 fixes in priority order.

optimize — Generate AEO-improved variant

/cs:aeo optimize --input post.md --mode balanced --output post-aeo.md
/cs:aeo optimize --input post.md --mode aggressive --industry finance

Three modes:

• conservative — touch <10% of words (schema + corrections footer only)
• balanced — touch <30% (citation markers + heading restructure + schema + footer)
• aggressive — full restructure + fact-first lede + maximum citation density

track — Log a citation you observed in an LLM response

/cs:aeo track --url https://example.com/post --llm perplexity --query "what is AEO" --date 2026-05-17

Maintains a local ledger at ~/.aeo-data/citations.json. No telemetry.

report — Aggregate citation report for a URL

/cs:aeo report --url https://example.com/post

Returns total citations, LLM coverage, velocity, top queries, verdict (EARLY / EMERGING / STRONG).

export — Emit citation ledger as CSV

/cs:aeo export --output citations.csv

For reporting to clients / stakeholders.

Minimal Intake (3 Questions)

Q	Asks	When
Q1	What action — audit / optimize / track / report?	Always
Q2	Industry (saas / healthcare / finance / legal / ecommerce / b2b / media / education)	Always (calibrates thresholds)
Q3	For optimize: mode (conservative / balanced / aggressive)?	Only when action=optimize

Most invocations exit intake after Q2.

Workflow

# Phase 1: Audit
python3 marketing-skill/skills/aeo/scripts/aeo_audit.py --input <file> --industry <industry>
# → composite score 0-100 + top fixes

# Phase 2: Optimize (if audit < industry threshold)
python3 marketing-skill/skills/aeo/scripts/aeo_optimizer.py \
  --input <file> --mode <mode> --industry <industry> --output <file>-aeo.md
# → optimized variant + changelog

# Phase 3: Publish (manual step — review the optimized variant, then deploy)

# Phase 4: Track (over 4-12 weeks)
python3 marketing-skill/skills/aeo/scripts/citation_tracker.py \
  --action add --url <url> --llm <llm> --query <query> --date <YYYY-MM-DD>
# → ledger updated

# Phase 5: Report (monthly)
python3 marketing-skill/skills/aeo/scripts/citation_tracker.py \
  --action report --url <url>
# → per-URL citation report

Industry-Specific Thresholds

The auditor calibrates per-industry. YMYL ("Your Money or Your Life") topics use stricter thresholds:

Industry	Min Composite	Why
Healthcare	85	Direct health implications
Finance	85	Real financial decisions
Legal	85	Legal jeopardy if misapplied
Education	75	Learning outcomes
SaaS, B2B, Media	70	Business decisions, moderate stakes
E-commerce	65	Product reviews, lower individual risk

Content for YMYL topics scoring below threshold is unlikely to be cited regardless of other signals — the cs-aeo agent will flag this and refuse aggressive optimization until the foundational dimensions improve.

Anti-Patterns Rejected

• LLM-generated AEO content with no human review (RAG retrieval deprioritizes generic LLM output)
• Fabricated credentials in author bylines (LLMs cross-reference via LinkedIn/Wikipedia)
• Schema spam (false structured-data markup gets filtered)
• Authority laundering (linking out doesn't confer authority)
• Per-LLM optimization tunnel-vision (73% cross-LLM citation correlation — optimize for shared signals)
• Optimizing AEO at expense of SEO (and vice versa) — they complement, don't substitute

Trigger Phrases

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

Related

• Agent: cs-aeo
• Skill: aeo
• Companion: /cs:seo-audit (SEO + AEO often run together)
• Source: ported from alirezarezvani/aeo-box

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Version: 2.7.3 License: MIT