/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

Version: 2.7.3 License: MIT