Skills

GEO & AI Visibility Skill

Purpose

This Skill specialises in Generative Engine Optimization (GEO):

• Makes content more likely to be cited, quoted, or referenced by AI systems.
• Aligns structures with conversational queries and decision-making use cases.
• Designs and reviews llms.txt and GEO scorecards.

It operationalises concepts detailed in docs/geo-optimization-guide.md.

When to Use

Call this Skill when the user asks about:

• AI Overviews, ChatGPT visibility, Perplexity, Claude, Gemini, or Copilot.
• “Will AI find or cite our content?” or “How do we appear in AI answers?”.
• Conversational queries and voice-style questions.
• llms.txt, AI crawler optimisation, or AI visibility testing and measurement.

Expected Inputs

Any combination of:

• Existing or drafted content (pages, articles, comparison guides, FAQs).
• Target conversational queries (e.g. “Should I…”, “Compare X vs Y”, “Why does…”).
• Business and brand context (what expertise should be visible, what topics matter).
• Optional:
  • Existing or planned /llms.txt file.
  • Outputs from the SERP & Gap Analysis and AEO Writer Skills.

Typical Outputs

• GEO-optimised page structures and answer patterns for key queries.
• llms.txt drafts or improvements.
• AI visibility test plans and interpretation of results.
• GEO scorecards and ongoing measurement frameworks.

Core Behaviours

1. GEO vs SEO vs AEO Framing

Keep the relationship clear:

• SEO: Ranking in traditional SERPs.
• AEO: Structuring content for featured snippets and voice answers.
• GEO: Ensuring content is cited in AI-generated answers.

Many patterns overlap, but GEO emphasises:

• Citation-worthiness (data, sources, expertise).
• Conversational queries and nuanced decision frameworks.
• AI visibility testing and tracking.

2. Citation-Worthy Content Design

Use the 3C framework (see docs/geo-optimization-guide.md):

• Credible
  • Highlight author expertise, domain authority, and transparent sourcing.
  • Use specific data (stats, dates, research) with clear attribution.
• Clear
  • Lead with direct answers.
  • Use headings that mirror natural questions.
  • Structure content with lists and tables where helpful.
• Current
  • Indicate publication and update dates.
  • Reflect current best practices and up-to-date data.

When rewriting or planning content:

• Turn vague claims into data-backed statements.
• Add comparison tables, stepwise processes, and decision frameworks where relevant.

3. Conversational Query Patterns

Optimise headings and sections for how people talk to AI:

• “Should I…” decision queries.
• “Compare X vs Y…” comparison queries.
• “How do I…” with constraints (on a budget, for small teams, etc.).
• “Why does…” diagnostic queries.

Translate this into concrete structures (see docs/geo-optimization-guide.md for full templates), for example:

• Decision query:
  • Quick answer.
  • “When to do it” vs “When not to do it”.
  • Decision framework bullets.
• Comparison query:
  • Quick comparison summary.
  • Table with core criteria.
  • “When to choose X” vs “When to choose Y”.
• Diagnostic “Why does…” query:
  • Primary cause.
  • Other common causes.
  • Diagnostic workflow.
  • Solutions by cause.

4. llms.txt Design

When asked about llms.txt:

• Use the template from docs/geo-optimization-guide.md to:
  • Summarise what the organisation does.
  • Highlight 5–10 key resources.
  • Describe content focus and expertise.
  • Include a clear last updated date.
• Ensure the file is:
  • Concise (well under ~10 KB).
  • Focused on the best, most authoritative content.
  • Written in natural language, not keyword spam.

Outputs should be ready to paste into /llms.txt at the site root.

5. AI Visibility Testing & Scorecards

When testing AI visibility:

• Select a small set (10–20) of target conversational queries.
• For each query, design a test grid across platforms:
  • Google AI Overviews.
  • ChatGPT (with browsing).
  • Perplexity (Quick + Pro).
  • Claude (with web search).
  • Gemini.
  • Copilot.
• Use the test and scorecard templates from docs/geo-optimization-guide.md and the root SKILL.md to:
  • Capture whether the brand is cited.
  • Note position, whether it’s a quote or paraphrase.
  • Track which competitors are cited instead.

Summarise patterns:

• Content types that get cited vs ignored.
• Platforms where visibility is strong vs weak.
• Query clusters that need new or improved content.

6. Platform-Specific Tactics (High-Level)

Use docs/geo-optimization-guide.md for deeper detail, but keep these headlines in mind:

• Google AI Overviews
  • Optimise for question-based headings and FAQ schema.
  • Provide concise intro answers and comprehensive coverage.
• ChatGPT with browsing
  • Conversational headings and data-backed claims.
  • Clear attribution (“According to…” statements).
  • Recency signals (update dates, 2026 context).
• Perplexity
  • Research-grade content and transparent sources.
  • Tables, lists, and structured analysis.
• Claude / Gemini / Copilot
  • Nuanced, balanced explanations.
  • Clear separation of fact vs judgment.
  • E-E-A-T signals (experience, expertise, authoritativeness, trust).

Dependencies & Related Files

This Skill relies on:

• docs/geo-optimization-guide.md – the deep-dive GEO playbook.
• skills/seo-os.SKILL.md – for orchestration and overall strategy.
• skills/aeo-snippet-writer.SKILL.md – to execute GEO-aligned answer patterns.
• skills/serp-gap-analysis.SKILL.md – for identifying conversational query opportunities.

Where appropriate, explicitly reference sections or templates from the GEO guide rather than duplicating full content.