/seo-geo

name: seo-geo
description: Optimize content to be cited inline by AI answer engines — AI Overviews, ChatGPT, Perplexity, and Bing Copilot. Scores passage-level citability, generates llms.txt, audits AI-crawler access, and recommends structured-data and brand-mention signals. Uses DataForSEO for LLM-mention tracking when connected; otherwise scores citability and gives on-page guidance without live mention data. Trigger when the user says "AI Overviews", "GEO", "generative engine optimization", "AI search optimization", "Perplexity citations", "ChatGPT search", "AI visibility optimization", or "llms.txt".

seo-geo

**Family:** seo **Status:** Stable

Purpose

Optimize for AI-powered search, where the win condition is being **cited inline by the AI answer**, not ranking #1. That requires different content patterns: passages that make specific, verifiable, standalone claims; discoverability via llms.txt and AI-crawler access; and structured data (a top-5 GEO citation factor in the GEO study, Aggarwal et al., KDD 2024).

Triggers

• "ai overviews" / "SGE" / "GEO" / "generative engine optimization"
• "AI search" / "LLM optimization" / "AI visibility optimization" / "ai citations"
• "perplexity" / "chatgpt search" / "bing copilot" / "llms.txt"

Inputs

• A page URL and/or its content (file)
• An optional `robots.txt` (file or live) for the AI-crawler-policy verdict
• Target platforms (AI Overviews / Perplexity / ChatGPT / all)

Steps

1. **Run the GEO checker (with the weighted scorecard):**

   python3 "${CLAUDE_PLUGIN_ROOT}/scripts/seo/geo_check.py" --content page.html \
     --robots robots.txt --scorecard --human

It scores each passage's citability (sourced + self-contained + answer-first → 0-100), counts structured-data blocks, reads the robots AI-crawler policy, and rolls those signals into a **weighted 0-100 GEO score** with a per-category breakdown (re-normalized over whatever signals are present, so a content-only offline run still scores). Add `--url https://site.com` to also check `/llms.txt` and fetch live robots. See `references/geo-scorecard.md` for the weight rationale, the passage rubric, and the crawler-verdict semantics. 2. **Passage citability** — rewrite the weak passages it lists so each leads with one specific, sourced claim (number/date/named source) that survives extraction. 3. **llms.txt** — if absent, create a Markdown `/llms.txt` summarizing the site's key pages for LLMs. 4. **AI-crawler access** — read the checker's crawler-policy **verdict** (`citable-training-blocked` is best practice; `retrieval-blocked` is the anti-pattern). Ensure retrieval bots (OAI-SearchBot, PerplexityBot, Claude-SearchBot) are allowed so the site stays citable, even if training crawlers are blocked. 5. **Structured data** — add Article/Organization/Breadcrumb schema via `seo-schema`. 6. **Brand mentions** — recommend earning mentions on sources LLMs trust; if the DataForSEO extension is present, pull LLM-mention tracking, else note it. 7. **Render** platform-specific action items (AI Overviews favors structured, sourced answers; Perplexity favors fresh, citation-dense pages).

Capability routing

This skill follows the plugin's capability-tier cascade (`references/CAPABILITY-TIERS.md`) and always returns a citability report:

1. **Tier 1 — DataForSEO MCP.** When connected, pull live LLM-mention / AI-visibility tracking to ground the brand-mention recommendations in real data. 2. **Tier 2 — built-in (the default).** Otherwise `geo_check.py` scores passage citability, counts structured-data blocks, audits llms.txt + the AI-crawler policy, and emits a **weighted 0-100 GEO scorecard** entirely offline (`--content` / `--robots` / `--scorecard` / `--no-network`) — the free, on-page GEO path is the product. 3. **Tier 4 — guided.** If no mention data is available, deliver the citability score + on-page fixes and name DataForSEO as the way to add hard mention tracking.

capability:   geo-citability
tier1:        DataForSEO MCP (LLM-mention / AI-visibility tracking)
tier1_signal: DATAFORSEO_USERNAME | DATAFORSEO_PASSWORD
tier2:        geo_check.py (weighted GEO scorecard + passage citability + structured-data count + llms.txt / AI-crawler verdict, no key)
tier2_yields: weighted 0-100 GEO score + per-passage citability + weak passages to fix + crawler-policy verdict, zero spend
tier3:        none
tier3_signal: none
tier4:        manual GEO checklist; add a DataForSEO MCP for live LLM-mention tracking
needs_tier1:  LLM-mention count, AI-answer citation share

Always end by stating which tier ran and what mention data a higher tier would add.

Outputs

| Output | What it contains | Format | Quality bar (how it is scored) | |---|---|---|---| | GEO scorecard | weighted 0-100 score + per-category breakdown (passage citability 45 / crawler access 25 / structured data 20 / llms.txt 10), re-normalized over available signals | JSON (`geo_score`) + `--human` ASCII | deterministic; names every excluded category; never weights a signal it did not observe | | Passage citability | per-passage 0-100 (sourced + self-contained + answer-first) + the weak passages to fix | JSON (`citability`) + ASCII | each weak passage carries a specific, actionable reason; `citable` stays sourced+standalone+≤120w | | AI-crawler verdict | retrieval-vs-training stance + verdict (`citable-training-blocked` … `retrieval-blocked`) + recommendation | JSON (`ai_crawler_policy`) | judges against the best practice (stay retrievable, opt out of training); reads real robots grouping | | Structured-data + brand-mention items | per-platform action items; llms.txt status | prose / Info group | every item carries a concrete fix | | Tier line | which tier ran + what a higher tier would add | one sentence | states the tier honestly; LLM-mention count ships as a `needs_tier1` proxy, never a fabricated number |

Filed to: the