---
name: geo-strategy
description: Build AI search visibility strategies by extracting decision criteria and closing retrieval gaps on high-value pages
mode: subagent
tools:
  read: true
  write: false
  edit: false
  bash: true
  glob: true
  grep: true
  webfetch: true
  task: true
---

<!-- SPDX-License-Identifier: MIT -->
<!-- SPDX-FileCopyrightText: 2025-2026 Marcus Quinn -->

# GEO Strategy

Increase citation likelihood in AI search by matching decision criteria with verifiable page content on pages that already rank. Ranking is prerequisite — unranked pages cannot be consistently cited. Optimize for deterministic retrieval signals, not daily answer volatility.

**Inputs:** core query set, top landing pages, page types, competitor set, proof assets (certifications, policies, prices, case evidence)
**Outputs:** criteria matrix, source-ID evidence ledger, page-type gap map, weighted implementation plan, per-engine report lines

## Workflow

### 1) Scope high-value intents

- Select 5-20 intents that influence revenue or lead quality; map each to an existing target page
- Exclude intents without a realistic ranking path
- Classify by grounding likelihood to avoid optimizing non-retrieval prompts
- Classify each target as PDP, category, homepage, article, local, SaaS feature, pricing, comparison, glossary, use-case, or research/report before scoring tactics

### 2) Extract decision criteria

- Probe multiple models with targeted buying-decision prompts
- Normalize into concrete criteria (not vague advice); cluster by: trust, expertise, fit, cost, delivery, risk

### 3) Score coverage per page

- Mark each criterion: strong, partial, missing, or not applicable
- Require source IDs for every material recommendation; each source ID must map to a URL section, data source, policy, certification, third-party profile, log, or engine run
- Flag unsupported marketing claims immediately
- Score business value, page-type applicability, retrieval eligibility, evidence strength, effort, confidence, freshness, third-party breadth, and engine-specific mention/citation behavior with `ai-search-scoring.md`

### 4) Build retrieval-ready summaries

- Add a concise criteria-matching block near top of page
- Keep claims specific, self-contained, and fact-backed — not broad brand language

### 5) Validate and iterate

- Re-check retrieval fitness after edits; evaluate coverage before citation counts
- Monitor citations directionally, not as the only success metric
- Report AIO, Gemini, ChatGPT, AI Mode, and Perplexity on separate per-engine lines; never aggregate AI Share of Voice without the underlying engine-level evidence
- Re-run criteria extraction monthly or after major model shifts

## Anti-Patterns

- Prompt-rank dashboards without content remediation
- Large batches of AI-generated pages with weak evidence
- Generic "best" claims without supporting proof
- Treating one model's output snapshot as durable ground truth
- Recommendations without source IDs or with source IDs that do not support the claimed fix
- Treating `FAQPage` schema as a primary GEO tactic; visible FAQ content can help where page type and query fan-out justify it, but schema is hygiene

## Implementation Rules

- First 200-300 words must be criteria-dense and informative
- Use explicit headings for key buyer concerns; align terminology with user query vocabulary
- Single canonical value for every critical fact across the site
- Prefer additive edits to existing pages before creating net-new pages
- Weight tactics with `seo-audit-skill/aeo-geo-patterns/04-page-type-tactic-matrix.md` before proposing copy, schema, FAQ, comparison, or proof work
- Keep key pages accessible to major AI/search crawlers
- One topic per URL; titles, H1s, and headings must include category terms, feature type, year, and pricing where applicable
- Keep pricing, feature lists, and comparison data in crawlable HTML — not behind JS rendering or gated forms
- AI models use `site:yourdomain.com [category] features [year]` patterns to extract detail from known-relevant domains

### Review platform parity

AI models query G2, Capterra, and TrustRadius as a validation stage after extracting brand-site claims:

- Maintain complete profiles with the same canonical facts (pricing, features, integrations) as the primary site
- Consistent product naming across platforms; wrong category = invisible to model queries
- Respond to reviews — AI models may extract vendor responses as support quality evidence
- Monitor profiles quarterly; add TrustRadius, PeerSpot, or vertical-specific sites where G2/Capterra coverage is thin

## Report Output

- Use `ai-search-report-template.md` for executive summaries, method,
  weighted scorecards, page-type findings, evidence ledgers, roadmap,
  verification, and custom-agent/routine handoff.
- Use stable `source_id` values in the evidence ledger and repeat those IDs in
  every roadmap item, scorecard row, and material recommendation.
- Map each finding to a taxonomy component: grounding eligibility, fan-out
  coverage, criteria alignment, snippet survivability, fact integrity,
  autonomous discoverability, citation monitoring, or page-type tactic fit.

## Related Subagents

- `sro-grounding.md` for snippet selection and grounding optimization
- `query-fanout-research.md` for sub-query and theme decomposition
- `ai-hallucination-defense.md` for contradiction and claim-evidence audits
- `keyword-research.md` for demand and intent validation
- `video-seo.md` — video as a GEO content atom; LLM surface retrieval via transcript
- `transcript-seo.md` — transcript paragraphs as GEO snippet candidates
- `ai-search-scoring.md` for weighted AI-search prioritisation
- `ai-search-report-template.md` for report-ready SEO/GEO outputs
