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llm-visibility-audit

Assess brand discoverability using supplied AI-search results, website content, and crawl evidence. Distinguish observed citations from untested recommendations.

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./packages/claude-directory/skills/llm-visibility-audit/SKILL.md

The canonical home for this skill is llm-visibility-audit in Synter-Media-AI/plugin

SKILL.md
Quality
Evals
Security

Quality

Content

75%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.

The body is a lean, well-scoped instruction skill: it adds genuine domain judgment (evidence recording requirements, crawler-preference cautions, the re-measure validation loop) without padding or explaining known concepts. It consistently reaches 'good with minor gaps' — lacking only examples, section headers, and error-recovery guidance to reach the top anchor.

Suggestions

Add one worked example (a sample dated query record and a skeleton of the three-part report) to make the guidance copy-paste ready.

Add short section headers (e.g., "Evidence collection", "Website review", "Report structure") to improve scanability, and drop or trim the mermaid flowchart since the prose already conveys the sequence.

Add a brief fallback for the no-evidence case (e.g., "If no measured evidence is available, list the missing measurements explicitly rather than issuing recommendations").

DimensionReasoningScore

Conciseness

The body is tight and assumes competence — "Record the exact query, engine, date, source URL, and observed citation" and "Never invent a visibility score or treat a page change as proof of ranking" add domain judgment Claude would not infer, with no explanations of known concepts. It is not a 5 because the mermaid flowchart ("Define brand and queries" → "Collect dated evidence" → ... → "Repeat measurements") restates the prose sequence and is a minor instance of trimmable padding; not a 3 because nothing explains concepts Claude already knows.

4 / 5

Actionability

For an instruction-only skill the guidance is concrete and executable: exact fields to record ("query, engine, date, source URL, and observed citation"), specific checks ("robots directives, accessible documentation, and structured data"), an explicit report structure ("observations supported by evidence, missing measurements, and proposed fixes"), and a concrete re-measurement step. Not a 5 because no examples cover common cases (e.g., a sample dated query record or a report skeleton); not a 3 because the guidance is specific and immediately executable rather than pseudocode-level.

4 / 5

Workflow Clarity

The sequence — identify brand/queries, collect dated evidence, review the site, separate findings from gaps, propose fixes, repeat measurements — is coherent across the paragraphs and the flowchart, with an explicit validation checkpoint: "Re-run the same dated queries after changes before describing an improvement." Not a 5 because steps are unnumbered, there is no checklist, and no error-recovery loop (e.g., what to do when no evidence is available is only partially addressed); not a 3 because the re-measure-before-claiming-improvement checkpoint is explicit, and this read-only audit involves no destructive or batch operations that would trigger the cap.

4 / 5

Progressive Disclosure

The skill is under 50 lines, needs no external references (none exist in the bundle), and inlines nothing that belongs in a separate file, matching good structure with minor gaps. Not a 5 because "well-organized sections" is not literally met — there are no section headers delineating evidence collection, website review, and report structure, and the flowchart is appended without introduction; not a 3 because the content is short, logically ordered, and fully navigable as-is.

4 / 5

Total

16

/

20

Passed

Description

53%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.

The description is concise and names a distinct niche with concrete inputs, but it has no explicit trigger guidance and misses the natural phrases users would say when they need this skill. It reads as a clear "what" with the "when" left entirely implicit, placing it at the middle of the scale.

Suggestions

Add an explicit trigger clause, e.g., "Use when the user asks how their brand appears in AI assistants (ChatGPT, Gemini, Perplexity) or requests an AI search visibility audit."

Include natural synonyms users would actually say: "AI visibility", "LLM visibility", "mentions in AI answers", "how does ChatGPT describe my brand".

State the deliverable to sharpen the "what": "Produces a report separating evidenced observations from untested recommendations and missing measurements."

DimensionReasoningScore

Specificity

"Assess brand discoverability" names the domain, and "using supplied AI-search results, website content, and crawl evidence" plus "Distinguish observed citations from untested recommendations" give 1-2 concrete actions with concrete inputs, matching the anchor for domain plus 1-2 actions. It is not a 4 because it does not list several specific actions (e.g., report generation, website review, re-measurement steps that the body actually contains), and not a 2 because the inputs and the citation-vs-recommendation distinction are concrete rather than generic.

3 / 5

Completeness

The "what" is clear ("Assess brand discoverability using supplied AI-search results, website content, and crawl evidence") but the "when" is entirely absent — there is no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. Not a 2 because the "what" is specific, not vague; not a 4 because zero usage triggers are given.

3 / 5

Trigger Term Quality

Terms like "brand discoverability", "AI-search results", and "citations" are relevant, but common user phrasings are missing: "AI visibility", "LLM visibility", "how does ChatGPT/Gemini/Perplexity describe my brand", "mentions in AI answers". This matches the anchor for some relevant keywords while missing common variations, and falls short of good coverage (4).

3 / 5

Distinctiveness Conflict Risk

The AI-search citation auditing niche ("AI-search results", "observed citation", "crawl evidence") is mostly distinct with only minor overlap risk against general SEO or website-audit skills. Not a 5 because "website content, and crawl evidence" could still trigger for broader SEO/technical-audit requests; not a 3 because the evidence-vs-recommendation framing and AI-citation focus are specific and unlikely to fire for unrelated skills.

4 / 5

Total

13

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

Synter-Media-AI/plugin

Path

packages/claude-directory/skills/llm-visibility-audit/SKILL.md

Reviewed

Table of Contents

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