SKILLEMALL.ai

AD mercator-entity-evidence

Company facts with the receipts attached. Ask for legal name, headquarters, founded year, employee count, executives or funding for a domain and get each field back with its source URL, a verbatim excerpt from that page, when it was retrieved, and n-of-m source agreement. Conflicting sources are returned, not hidden. A field that cannot be evidenced comes back UNKNOWN rather than guessed, and a company that cannot be resolved is not charged for. Paid per call over x402 on Base ($0.10 USDC), with a free preflight. You pay from your own wallet — see "Who pays" below. Triggers: "what is the legal name of X", "verify this company", "enrich this domain with sources", "who runs X and where is it based".

ClawHub Agent Skills author: Arzuo v1.0.0 MIT-0 2 files body ≈ 862 tokens Open the sourceclawhub.ai analyzed 12 h ago

Company facts with the receipts attached.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
89
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:112
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | asset | `0x83…913` (USDC on Base) |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:113
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | payTo | `0xEF…7A8` |
      table

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "credentials"

    Process rating: all ten parameters 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 862 tokens

    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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 708: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a disclosed paid company-verification API helper, with the main caution that payment headers must only be used with the trusted service endpoint.
    LLM: benign (high) · VirusTotal: · 13 Sept 2026