AI SEO
Optimize content to get cited by AI search engines and AI Overviews.
$skillfish add ~skills/ai-seo Real Conversations
See how the AI SEO skill responds to actual requests. These are representative examples of what your AI agent will produce.
Our blog post on sprint planning tips ranks #4 on Google but we're not getting cited in AI Overviews. Competitors who rank #7 and #8 are being cited instead. What are we doing wrong and how do we fix it?
AI Overviews don't just pull from top-ranked pages — they pull from content formatted for extraction. Here's likely what's happening and how to fix it:
Why lower-ranked pages get cited:
- Their content has direct, extractable answers — clear question followed by a direct 1–2 sentence answer
- They use definition-first writing — the first sentence after an H2 answers the question completely
- They have structured lists and tables that AI can parse and quote cleanly
Fix 1 — Add a direct-answer intro block:
What is sprint planning? Sprint planning is a Scrum ceremony where engineering teams select backlog items for the upcoming sprint and estimate the work required. A typical session for a 5–10 person team should take 60–90 minutes.
AI Overviews heavily favor definitions in the first 100 words after a heading.
Fix 2 — Restructure sections as question-format headers:
- Change: "Tips for Better Sprint Planning"
- To: "How long should sprint planning take?"
- To: "How do you run an effective sprint planning meeting?"
Fix 3 — Add a structured summary table:
| Sprint length | Recommended planning duration |
|---|---|
| 1-week sprint | 1–2 hours |
| 2-week sprint | 2–4 hours |
Fix 4 — Add FAQ schema markup — AI Overviews weight FAQ structured data heavily.
Fix 5 — Author E-E-A-T signals: Add a bio with real credentials ("Engineering manager for 8 years") — AI Overviews favor demonstrated expertise over anonymous content.
Implement these and check for AI Overview inclusion within 2–3 weeks.
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Skills are designed for AI coding agents (Claude Code, Cursor, Windsurf) and IDE-based workflows where the agent can read files, run scripts, and act on your codebase. Web-based AI can use the knowledge and frameworks, but won't have tool access.
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Good to Know
Advanced guide and reference material for AI SEO. Background, edge cases, and patterns worth understanding.
Contents
AI Search vs Traditional Search
Classic SEO optimizes for ranking signals — PageRank, backlinks, keyword density — to win a position on a results page. AI search works differently: the model retrieves candidate sources, extracts content, synthesizes an answer, and selects citations from that extraction. A page ranked #7 can be cited over a page ranked #1 if its content is more extractable.
Key differences practitioners need to internalize:
- Ranking is not citation. AI Overviews and Perplexity pull from their own retrieval and indexing pipelines, not directly from the live SERP position order.
- Entity understanding matters more. AI systems recognize named entities, relationships, and topical authority — thin keyword-matching content is increasingly invisible.
- Freshness thresholds vary. Perplexity indexes actively; Google AI Overviews pull from pages already in the search index, so indexation lag affects AI visibility.
E-E-A-T Signals for AI Retrieval
Google's E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) applies to AI retrieval with some platform-specific weighting:
| Signal | What it means | How to implement |
|---|---|---|
| Experience | First-hand demonstration, not just description | Case studies, specific outcomes ("reduced churn by 22%"), dated examples |
| Expertise | Demonstrated domain knowledge | Author bios with credentials, linked profiles (LinkedIn, personal site) |
| Authoritativeness | Third-party recognition | Backlinks from domain-relevant sites, citations in other authoritative content |
| Trustworthiness | Factual accuracy, transparency | Citations with links, correction notes, clear publication and update dates |
Perplexity and ChatGPT Browse weigh backlink profiles and external citations heavily — a page that authoritative domains reference is a stronger citation candidate. Google AI Overviews additionally weight Schema markup (especially FAQ and Article type) as a structured signal of content intent.
Content Structure for AI Retrieval
AI extraction favors content that is pre-digested — the answer is complete in the first 1–2 sentences after a heading, not buried three paragraphs in.
Definition-first writing: Place the direct answer in the opening sentence of each section. "Sprint planning is a ceremony where..." beats an intro paragraph building up to the definition.
Question-format headings: H2s phrased as questions ("How long should sprint planning take?") match query intent and are frequently pulled verbatim into AI answers.
Short, self-contained paragraphs: Each paragraph should make one complete point. AI extraction windows are typically 100–200 tokens; paragraphs that sprawl across multiple ideas lose coherence when extracted.
Structured lists and tables: Numbered lists and tables are among the most reliably extracted formats. If your content has comparative or sequential information, use them.
FAQ sections with Schema: A dedicated FAQ section with FAQPage JSON-LD markup gives AI systems an explicit, machine-readable signal of Q&A structure.
Platform Comparison
| Platform | Source preference | Citation style | Freshness requirement | Schema influence |
|---|---|---|---|---|
| Google AI Overviews | Google index (existing rankings inform but don't determine) | Inline source cards, 3–5 sources | Standard crawl cycle | FAQ, Article, HowTo weighted |
| Perplexity | Live web retrieval + Bing index | Numbered footnotes, typically 5–8 sources | Near real-time | Minimal direct schema use |
| ChatGPT Browse | Bing index, real-time retrieval when enabled | Inline links | Days to weeks | Minimal |
| Claude (claude.ai) | No live web in base mode; citations when tools enabled | Tool-dependent | N/A for base model | N/A |
Tracking AI Visibility
Current tooling for AI search monitoring is early-stage with significant gaps:
Google Search Console: Shows AI Overviews impressions as a filter in the Performance report (Search type → Web, filter by "AI Overviews"). Available for GSC-verified properties only.
Semrush / Ahrefs AI Overview tracking: Both tools added AI Overview appearance data to keyword rank trackers in 2024. Useful for monitoring which keywords trigger AI Overviews and whether your domain appears.
Perplexity source tracking: No official API or analytics tool. Manual monitoring via searching target queries and checking Sources panel. Third-party tools like Brandwatch have added Perplexity monitoring but coverage is partial.
Key limitation: No platform exposes click data from AI citations — impressions and mentions are measurable but CTR from AI panels is largely unmeasurable as of early 2025. Attribution models for AI-driven traffic remain unsolved.
Ready to try AI SEO?
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$skillfish add ~skills/ai-seo