ai-seo

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' 'WebMCP,' 'do listicles still work for AI,' 'ChatGPT stopped citing comparison pages,' or 'AI citation format shift.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

Quality

83%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

71%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, actionable skill body that uses progressive disclosure effectively and gives concrete audit/optimization guidance. Its main weakness is redundancy between the 'What NOT to Do' and 'Common Mistakes' sections and between the two question-gathering blocks, which inflates token cost.

Suggestions

Merge 'What NOT to Do' and 'Common Mistakes' into a single section; they repeat the same points (blocking AI bots, gating content, keyword stuffing, writing separate AI content) and roughly halve that token cost.

Consolidate 'Before Starting' context-gathering and 'Task-Specific Questions' into one question list; they overlap on top queries, schema markup, competitors, and Wikipedia/review presence.

Move the Princeton GEO method table and the platform source-selection table into a reference file (e.g. platform-ranking-factors.md) and summarize the top findings inline, shortening the main SKILL.md.

Dimension	Reasoning	Score
Conciseness	The body is information-dense with tables and stats rather than padding about basics Claude knows, but at ~480 lines it carries real redundancy: 'What NOT to Do' and 'Common Mistakes' overlap heavily (AI-bot blocking, gating, keyword stuffing, writing for AI) and 'Before Starting' questions overlap 'Task-Specific Questions'.	3 / 5
Actionability	Provides concrete executable guidance — a copy-paste /pricing.md block, named AI bots to check in robots.txt, schema types per content type, the Princeton GEO boost table, and a 4-step DIY monitoring procedure — with only minor abstract guidance remaining.	4 / 5
Workflow Clarity	The AI Visibility Audit is clearly sequenced (check answers → analyze citations → extractability check → bot access check) and is not a destructive/batch operation, so the validation cap does not apply; each audit step is itself a verification check, leaving only minor explicit-checkpoint gaps.	4 / 5
Progressive Disclosure	Eight reference files exist in references/ and all eight are linked one level deep with clear markdown links and brief content descriptions (platform-ranking-factors, content-patterns, agent-readiness, format-volatility, youtube-ai-citations, okf, citations-vs-recommendations, content-types), keeping SKILL.md as an overview with deep material split out.	5 / 5
	Total	16 / 20 Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exceptionally trigger-rich description that explicitly states both capability and activation conditions while actively disambiguating from neighboring skills. Its only weakness is that the named actions are variants of a single capability rather than a broad feature set.

Dimension	Reasoning	Score
Specificity	Names the AI-citation domain and several concrete actions ('optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers') plus skill disambiguation, but the actions are variants of one core capability rather than a comprehensive list of distinct operations.	4 / 5
Completeness	Explicitly answers both what ('optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers') and when ('Also use when the user mentions…' / 'Use this whenever someone wants their content to be cited or surfaced…') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Comprehensive coverage of natural user phrases including acronyms and synonyms ('AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'optimize for ChatGPT/Perplexity/Claude/Gemini,' 'how do I show up in AI answers,' 'llms.txt,' 'WebMCP'), matching the anchor's expectation of synonyms and file/extension-style terms.	5 / 5
Distinctiveness Conflict Risk	Clear AI-citation/AEO niche with distinct triggers and explicit conflict avoidance via hand-offs ('For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.').	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 1 suspicious	Warning

	Total	15 / 16 Passed

Repository: coreyhaines31/marketingskills
Path: skills/ai-seo/SKILL.md
Commit: 5b2c000

Reviewed: ~5 hours ago

Table of Contents

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