Conciseness

The body is dense with genuinely non-obvious domain knowledge (crawler token purposes, format-volatility data, engine-specific citation behavior) and pushes detail to references, but it includes unnecessary repetition and time-sensitive material: Google's official stance is restated three times ("Google's Official Stance vs. Multi-Platform Reality", the "Machine-Readable Files" blockquote, and "What NOT to Do"), and dated claims ("ChatGPT 5.6 (Aug 2026) demoted the exploited formats", "BrightEdge, Feb 2026", "Ahrefs, Dec 2025") are sprinkled inline rather than isolated in a dated/deprecated section, which the guidelines penalize. This fits anchor 3 ('mostly efficient but includes some unnecessary explanation or could be tightened'); it is not 2 because most sections carry unique, high-value instruction rather than padding, and not 4 because the redundancy and undated-section statistics are more than minor.

Actionability

Most guidance is concrete and directly usable: a copy-paste-ready "/pricing.md" template, exact user-agent tokens ("OAI-SearchBot", "Claude-SearchBot", "Google-Extended"), fill-in audit tables ("Clear definition in first paragraph? ... Pass/Fail"), query templates per awareness stage, and specific numbers ("40-60 words", "10-20 of your most important queries", "Run each query 3–5 times"). This matches anchor 4 ('mostly executable guidance ... with minor gaps'); it is not 5 because a meaningful share of the Presence/actions guidance remains directional ("Ensure your Wikipedia page is accurate and current", "Get featured in industry roundups") without steps, and not 3 because the core workflows are executable as written rather than pseudocode-level.

Workflow Clarity

A coherent sequence is present — context gathering ("Before Starting"), a 4-step "AI Visibility Audit" with explicit checks, the three-pillar "Optimization Strategy", then "Monitoring AI Visibility" with a monthly protocol — and there are checklists plus a measurement feedback loop ("Run each query 3–5 times per platform and track the mention rate with its sample size ... comparing rates over time"). This fits anchor 4 ('clear sequence with most checkpoints present; minor validation gaps'); it is not 5 because the end-to-end flow is organized topically rather than as one explicit ordered process, and re-verifying optimization changes is only implicit in the monthly monitoring cadence; it is not 3 because checkpoints and error-handling guidance (mis-statements → "fix that page first", non-determinism → sample-size rule) are explicit.

Progressive Disclosure

The body opens with a question-to-reference routing table covering all 15 bundled files (verified: every path in the table exists in references/, and reference files cross-link only laterally to siblings, not to second-level files), instructs "read additional references only when their topics are relevant", and includes a fallback ("If a file is missing, say which reference is unavailable"). This matches anchor 5 ('clear overview with well-signaled one-level-deep references; content appropriately split; easy navigation'); the core workflow stays inline while specialized topics are split out, so neither the anchor-4 'minor organization gaps' nor inlining critique applies.

Specificity

The description names the domain clearly ("optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers") but these are outcome statements rather than a list of concrete capabilities — it never states what the skill actually does (audit visibility, restructure content, configure bot access). This matches anchor 3 ('names domain and 1-2 concrete actions, but not comprehensive'); it is not 4 because no several specific actions are enumerated, and not 2 because the domain and purpose are far from generic.

Completeness

Both 'what' and 'when' are answered explicitly: the what is "optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers" and "wants their content to be cited or surfaced by AI assistants and AI search engines", and the when is an explicit 'Also use when the user mentions...' list plus 'Use this whenever...'. This matches anchor 5 (both answered clearly with concrete trigger phrases); it exceeds anchor 4 because the 'when' is maximally explicit rather than needing more specificity.

Trigger Term Quality

Trigger coverage is exhaustive and natural: synonyms and acronyms ("'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization'"), platform-specific phrasings ("'optimize for ChatGPT,' 'optimize for Perplexity'"), file-format terms ("'llms.txt,' 'llms-full.txt'"), and verbatim user questions ("'how do I show up in AI answers,' 'is my site agent-ready'"), plus emerging terms like "'WebMCP,' 'OKF'". This exceeds the anchor-5 example's coverage of natural terms including synonyms; nothing common is missing.

Distinctiveness Conflict Risk

The niche (AI search / answer-engine citation) is distinct with dedicated triggers, and adjacent skills are explicitly de-conflicted: "For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema." This matches anchor 5 (clear niche with distinct triggers; minimal conflict risk) — the only adjacent overlap (traditional SEO) is actively disambiguated rather than left to chance.