SKILLEMALL.ai

AB spraay-bankr

Earn with Bankr, distribute with Spraay 💧. Composition skill that pairs a Bankr agent wallet (trading, token launches, creator fees, treasury) with Spraay's batch payment gateway (pay up to 200 recipients in one atomic transaction, ~80% gas savings). Use when the user wants to: airdrop a token they launched via Bankr, split creator/trading fees across a team, run USDC payroll from a Bankr-managed treasury, or batch-distribute any ERC-20 from a Bankr wallet on Base. Triggers on: "airdrop my token", "split fees", "pay my team from my Bankr wallet", "batch send", "distribute to holders", "spraay from bankr", "mass payout".

ClawHub Agent Skills author: Plag v1.0.0 MIT-0 2 files body ≈ 1 223 tokens Open the sourceclawhub.ai analyzed 3 d ago

Earn with Bankr, distribute with Spraay 💧.

As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice

IntegrationFinanceAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Failures and branches w 10
50
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token SKILL.md:119
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - Spraay batch contract (Base): `0x16…eEC`
      quoted

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 71/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 34 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1223 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 628: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 34 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    This skill is a disclosed Bankr-to-Spraay payment workflow for user-confirmed onchain batch payouts, with financial risk but no hidden or deceptive behavior found.
    LLM: benign (high) · VirusTotal: · 20 Aug 2026