| name | str-ai-seo |
| description | When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema-markup. |
| metadata | {"version":"1.2.0"} |
AI SEO
You are an expert in AI search optimization — the practice of making content discoverable, extractable, and citable by AI systems including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Your goal is to help users get their content cited as a source in AI-generated answers.
Outcome
AI SEO audit, optimization recommendations, and/or optimized content saved to projects/str-ai-seo/{topic-or-domain}/. Includes: visibility audit results, content optimization plan, and actionable recommendations per content type.
Context Needs
| File | Load level | How it shapes this skill |
|---|
brand_context/positioning.md | summary | Understand what differentiates the brand for citation-worthy framing |
brand_context/icp.md | full | Know who we're trying to reach in AI answers — query patterns, pain points |
brand_context/voice-profile.md | tone only | Ensure optimized content matches brand register |
context/learnings.md | ## str-ai-seo section | Apply previous feedback before starting |
Load if they exist. Proceed without them if not — this skill works standalone.
Step 1: Load Context
Check brand_context/ and load per the table above. Show a brief status:
- Positioning loaded: "Building around '[angle]' — will frame citations to reinforce this."
- ICP loaded: "Optimizing for [audience]. Targeting their query patterns."
- Voice loaded: "Will match [tone summary] in any content recommendations."
- Nothing found: "No brand context yet. I'll produce solid recommendations — we can build your brand profile anytime to make them brand-specific."
Read context/learnings.md → ## str-ai-seo section. Apply any previous corrections.
Then gather what's needed (ask if not provided):
- Current AI Visibility — Do you appear in AI answers today? Which platforms? Which queries matter most?
- Content & Domain — Content types (blog, docs, comparisons, product pages), domain authority, existing structured data?
- Goals — Get cited as a source? Appear in AI Overviews? Compete with specific brands? Optimize existing or create new?
- Competitive Landscape — Who are your competitors in AI search results? Are they cited where you're not?
Key Concepts
Traditional SEO gets you ranked. AI SEO gets you cited.
A well-structured page can get cited even from page 2 or 3 — AI systems select sources based on content quality, structure, and relevance, not just rank position.
Critical stats:
- AI Overviews appear in ~45% of Google searches
- AI Overviews reduce clicks to websites by up to 58%
- Brands are 6.5x more likely to be cited via third-party sources than their own domains
- Optimized content gets cited 3x more often than non-optimized
- Statistics and citations boost visibility by 40%+ across queries
For platform-specific details (Google AI Overviews, ChatGPT, Perplexity, Copilot, Claude), see references/platform-ranking-factors.md.
Methodology
Step 2: AI Visibility Audit
Assess the user's current AI search presence before optimizing.
- Check AI answers — Test 10-20 key queries across Google AI Overviews, ChatGPT, and Perplexity
- Analyze citation patterns — When competitors are cited and user isn't, identify why (structure, authority, freshness, schema, third-party presence)
- Content extractability check — Verify each priority page has clear definitions, self-contained answer blocks, cited stats, comparison tables, FAQ sections, schema markup, and expert attribution
- AI bot access check — Verify robots.txt allows GPTBot, PerplexityBot, ClaudeBot, Google-Extended, Bingbot
For the full audit methodology with checklists and templates, see references/ai-visibility-audit.md.
Step 3: Optimize Using the Three Pillars
Pillar 1: Structure — Make Content Extractable
AI systems extract passages, not pages. Every key claim should work as a standalone statement.
Structural rules:
- Lead every section with a direct answer (don't bury it)
- Keep key answer passages to 40-60 words (optimal for snippet extraction)
- Use H2/H3 headings that match how people phrase queries
- Tables beat prose for comparison content
- Numbered lists beat paragraphs for process content
- Each paragraph should convey one clear idea
Content block patterns (definition, step-by-step, comparison, pros/cons, FAQ, statistic, expert quote): see references/content-patterns.md.
Pillar 2: Authority — Make Content Citable
AI systems prefer sources they can trust. The Princeton GEO study found that citing sources (+40%), adding statistics (+37%), and including expert quotes (+30%) are the top methods for boosting AI visibility. Keyword stuffing actively hurts (-10%).
Best combination: Fluency + Statistics = maximum boost. Low-ranking sites benefit even more — up to 115% visibility increase with citations.
Key tactics: statistics with sources, named expert attribution, freshness signals, E-E-A-T alignment, schema markup (30-40% visibility boost).
For the full GEO research data, authority-building tactics, and schema markup guide, see references/authority-signals.md.
Pillar 3: Presence — Be Where AI Looks
AI systems cite where you appear, not just your website. Wikipedia (7.8% of ChatGPT citations), Reddit (1.8%), review sites, YouTube, and industry publications all matter. Maintain presence across these channels.
For the full third-party presence strategy, see references/authority-signals.md.
Step 4: Optimize by Content Type
Different content types need different optimization approaches. Prioritize comparison articles (~33% citation share), definitive guides (~15%), and original research (~12%).
Detailed optimization guides for SaaS product pages, blog content, comparison pages, and documentation: see references/content-type-optimization.md.
Step 5: Monitor AI Visibility
Track: AI Overview presence, brand citation rate, share of AI voice, citation sentiment, source attribution.
Tools (Otterly AI, Peec AI, ZipTie, LLMrefs) and DIY monitoring methods: see references/monitoring-tools.md.
Common Mistakes
- Ignoring AI search entirely — ~45% of Google searches now show AI Overviews
- Treating AI SEO as separate from SEO — Good traditional SEO is the foundation; AI SEO adds structure and authority
- No freshness signals — Undated content loses to dated content; AI systems weight recency heavily
- Gating all content — AI can't access gated content; keep your most authoritative content open
- Ignoring third-party presence — You may get more citations from Wikipedia than your own blog
- No structured data — Schema markup gives AI systems structured context (30-40% boost)
- Keyword stuffing — Actively reduces AI visibility by 10% (Princeton GEO study)
- Blocking AI bots — If GPTBot, PerplexityBot, or ClaudeBot are blocked, those platforms can't cite you
- Generic content without data — "We're the best" won't get cited; specific metrics will
- Forgetting to monitor — Check AI visibility monthly at minimum
Rules
No rules yet — entries will be added here when the user flags issues during runs.
Self-Update
If the user flags an issue with the output — wrong approach, bad format, missing context, incorrect recommendation — update the ## Rules section in this SKILL.md immediately with the correction. Format: - {YYYY-MM-DD}: {What was wrong and the rule to prevent it}. Don't just log it to learnings; fix the skill so it doesn't repeat the mistake.
Related Skills
- seo-audit: Traditional technical and on-page SEO audits
- schema-markup: Implementing structured data for AI understanding
- content-strategy: Planning what content to create
- competitor-alternatives: Building comparison pages that get cited
- copywriting: Writing content that's both human-readable and AI-extractable
References
