The description names the domain (AI-driven search optimization) and some actions like 'optimize content and site structure,' 'build llms.txt,' and 'structure content for AI extraction,' but the actions are somewhat high-level and not as concrete as listing multiple distinct operations (e.g., it doesn't specify deliverables like audits, schema markup, content rewrites).

Clearly answers both 'what' (optimize content and site structure for AI-driven search experiences) and 'when' with an explicit 'Use this skill whenever...' clause followed by detailed trigger scenarios and a comprehensive trigger term list.

Excellent coverage of natural trigger terms including acronyms (AEO, GEO), product names (ChatGPT search, Perplexity, Gemini, Claude search), technical terms (llms.txt, LLM optimization), and natural language concerns ('AI eating their organic traffic,' 'remain visible as search shifts'). These are terms users would naturally use.

This skill occupies a very clear niche—AI search optimization/AEO—with highly specific trigger terms like 'AEO,' 'GEO,' 'llms.txt,' 'AI citation,' and named AI search products. It is unlikely to conflict with general SEO skills or other content skills.

The content is generally well-structured and avoids explaining basic concepts Claude would know, but it's somewhat verbose in places—the 5-layer framework could be tighter, and some bullet points include explanatory context that Claude doesn't need (e.g., 'AI struggles when claims are spread across paragraphs'). Overall mostly efficient but could be trimmed by ~20%.

The skill provides a clear framework and workflow with specific, concrete guidance (e.g., 'Open major sections with a definitive 1 to 3 sentence answer'), but lacks executable code examples—no sample llms.txt content, no schema markup snippets, no example audit output. For a skill that references schema implementation and llms.txt creation, concrete examples would significantly improve actionability.

The 6-step workflow is clearly sequenced with logical progression from audit to implementation to re-testing. It includes validation checkpoints (test current AI visibility, identify gaps, re-test quarterly) and a feedback loop. The failure patterns section adds guardrails. The output format section specifies exactly what the deliverable should contain.

The skill references two supporting files (llms-txt-guide.md and extraction-friendly-patterns.md) with clear navigation links, which is good structure. However, no bundle files were provided, meaning these references are broken. Additionally, some inline content (like the full 5-layer framework details) could potentially be split into reference files to keep the main skill leaner, though this is borderline given the content's coherence as a single document.