--- 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). ## 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: ```bash python3 scripts/aeo_audit.py --url --industry healthcare # → stricter E-E-A-T thresholds; flags any health claim without primary citation ``` ## Output Format ### Markdown audit report (default) ```markdown # 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 ```bash python3 scripts/aeo_audit.py --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/.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`](https://github.com/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).