aeo

name aeo description Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools. 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 SEO AEO Optimizes for Click-through rankings Being cited as authoritative source Audience Humans browsing search results LLMs answering questions Success metric Position 1-10, organic traffic Citation count across LLMs Key signals Backlinks, keywords, page speed E-E-A-T, structured data, factual density Update cadence Weeks-to-months Days-to-weeks (LLM training cycles) 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). References references/aeo_eeat_canon.md — E-E-A-T methodology, industry thresholds, anti-patterns references/llm_citation_patterns.md — per-LLM citation selection heuristics (Perplexity, ChatGPT, Claude, Gemini, Mistral) references/aeo_vs_seo.md — when to invest in AEO vs SEO vs both references/bot_access_and_monitoring.md — AI crawler robots.txt matrix (the prerequisite check: a blocked bot zeroes that platform), Google Search Console AI Overviews monitoring, manual testing protocols, citation-drop diagnostic (merged from the former ai-seo skill) references/extractable_content_patterns.md — 7 copy-ready block templates (definition, steps, table, FAQ, attributed stat, expert quote, summary box) that answer engines reliably extract (merged from the former ai-seo skill) Workflow 0. Pre-flight: bot access Check robots.txt against the crawler matrix in references/bot_access_and_monitoring.md → a blocked GPTBot/PerplexityBot/ClaudeBot/Google-Extended is the first fix, always 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 Industry Min Composite Critical Signals Healthcare 85 Medical reviewer byline, peer-reviewed citations, FDA disclosure Finance 85 Author CFA/CPA credentials, "not investment advice" disclaimer, dated examples Legal 85 Jurisdiction disclosed, attorney bio, "not legal advice" disclaimer SaaS 70 Product manager byline, case study with metrics, ROI calculator E-commerce 65 Product reviews aggregated, return policy, schema.org Product B2B 70 Industry analyst quotes, customer logos, ROI data Media 70 Editorial policy, fact-check link, original reporting Education 75 Instructor bio, learning outcomes, accreditation if applicable 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).