SKILLEMALL.ai

AC ai-visibility

Check whether AI assistants (ChatGPT, Perplexity, Gemini, Google AI Overviews) recommend a brand, product, or crypto token in its category — the AI-era version of search ranking (GEO). The only x402 source for AI-recommendation data. USE FOR: - "Does AI recommend [brand]?" — visibility score 0-100 + who AI names instead - Ranking a whole category by how often AI names each brand (share of voice) - Whether ChatGPT/Perplexity recommend a crypto token, protocol, or chain - Qualifying a sales prospect by its AI-visibility gap (HOT/WARM/COLD) - Monitoring which brands/categories are being checked right now TRIGGERS: - "does ChatGPT recommend", "does AI recommend", "AI visibility", "GEO", "AEO" - "share of voice in AI", "brand in AI answers", "who does AI suggest for" - "is [token] recommended by AI", "ai visibility score" - "qualify this lead", "geo audit", "ai search ranking" Use x402 GET calls. Never guess paths — use the exact URLs below or GET /samples first.

ClawHub Agent Skills author: rccola990-cloud v1.0.0 MIT-0 2 files body ≈ 387 tokens Open the sourceclawhub.ai analyzed 2 d ago

Check whether AI assistants (ChatGPT, Perplexity, Gemini, Google AI Overviews) recommend a brand, product, or crypto token in its category — the AI-era…

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "mcp"

    Process rating: all ten parameters 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (ai-visibility) differs from the folder (riley-ai-visibility)
    • 50Failures and branches. 0 branches, has a failure section
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 387 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 975: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +4Structure: 3 headings
    • +3Step-by-step instructions: 3 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    This skill is a small, disclosed AI-visibility lookup helper that uses paid external x402 GET endpoints, with no executable code or hidden persistence found.
    LLM: benign (high) · VirusTotal: · 19 Jun 2026