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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

73%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/llm-visibility-audit/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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-structured instruction skill with concrete evidence-recording requirements, guardrails against fabricating visibility claims, and a clear sequenced workflow ending in re-measurement. Its main room for improvement is per-step validation checkpoints and small concrete examples (e.g., sample queries or a report skeleton), plus a slightly redundant flowchart.

Suggestions

Add per-step validation checkpoints in the prose workflow (e.g., what to confirm before moving from evidence collection to fix proposal), rather than relying on the flowchart's implicit sequence.

Include one or two example audit queries and a minimal report skeleton to make the common case fully copy-paste ready.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence, spending tokens only on non-obvious constraints like 'A crawler allowance or an index file does not establish that an AI service will ingest or cite the site'. The mermaid flowchart partially duplicates the sequence already traceable in the prose, a minor instance that could be trimmed, keeping it just below the every-token-earns-its-place anchor.

4 / 5

Actionability

Guidance is concrete and executable for an instruction-only skill: 'Record the exact query, engine, date, source URL, and observed citation', 'check robots directives, accessible documentation, and structured data', and an explicit three-part report structure. Minor gaps (no example queries or report skeleton) keep it below fully-executable-with-common-cases coverage.

4 / 5

Workflow Clarity

The flowchart lays out a clear five-step sequence (Define → Collect → Separate → Propose → Repeat) and 'Re-run the same dated queries after changes before describing an improvement' is an explicit validation loop; this is not a destructive/batch skill so no cap applies. It falls short of 5 because intermediate checkpoints per step are implicit rather than built into the sequence.

4 / 5

Progressive Disclosure

The skill is under 50 lines, single-purpose, needs no external references, and has no bundle files; the body is well organized into three focused phase paragraphs plus a summary flowchart. Per the scoring notes for simple skills, this warrants full marks with well-organized content alone.

5 / 5

Total

17

/

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 specific about its domain and inputs and occupies a fairly distinct niche, but it lacks any explicit 'when to use' trigger guidance and misses the natural phrases users would actually say when needing this skill. These gaps leave it at the midpoint of quality despite the otherwise precise wording.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks whether AI assistants (ChatGPT, Perplexity, Gemini) mention or cite their brand, or wants to improve their AI search visibility.'

Include natural user phrasings and synonyms such as 'AI search visibility', 'LLM mentions', 'how does my brand show up in AI answers' alongside the current terminology.

Name a couple more concrete audit actions (e.g., checking robots directives and structured data, producing a findings-vs-gaps report) to broaden capability coverage.

DimensionReasoningScore

Specificity

The description names the domain and its inputs ('supplied AI-search results, website content, and crawl evidence') plus one concrete analytical action ('Distinguish observed citations from untested recommendations'), but does not list several distinct actions, matching the 1-2 concrete actions anchor. It falls short of 4 because broader coverage of the audit's actions is absent.

3 / 5

Completeness

It has a clear 'what' (assess brand discoverability from supplied evidence) but no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines. It is not 4 because the 'when' is entirely absent rather than merely implicit.

3 / 5

Trigger Term Quality

Terms like 'brand discoverability', 'AI-search results', and 'crawl evidence' are relevant, but common natural phrasings users would say ('AI search visibility', 'does ChatGPT/Perplexity mention my brand', 'LLM visibility') are missing. This matches 'some relevant keywords but missing common variations or synonyms', not the good-coverage anchor.

3 / 5

Distinctiveness Conflict Risk

The niche is mostly distinct — 'AI-search results', 'crawl evidence', and 'observed citations' target AI search visibility auditing specifically, with only minor overlap risk against general SEO/brand-marketing analysis skills. It is not 5 because 'brand discoverability' and 'website content' alone could still overlap with broader marketing-audit 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

skills/llm-visibility-audit/SKILL.md

Reviewed

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