SKILLEMALL.ai

AD early_setup_finder

Pre-pump fingerprint scanner. Identifies assets showing accumulation signals before a price move using the 8-signal framework.

ClawHub Agent Skills author: Zahrah v0.1.0 MIT-0 2 files body ≈ 634 tokens Open the sourceclawhub.ai analyzed 2 d ago

Pre-pump fingerprint scanner.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
D
49/100
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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 49/100

    • 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
    • 40Consistency. Frontmatter name (early_setup_finder) differs from the folder (early-setup-finder)
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100Execution cost. Instruction body is 634 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 126: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 25 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

    External checks

    ClawHub: suspicious
    The skill text is a simple crypto market-analysis checklist, but its metadata advertises wallet, transaction-signing, and sensitive-credential capabilities that do not fit the instructions.
    LLM: suspicious (medium) · VirusTotal: · 5 Jun 2026