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AC nexus-ap2-batched-settle

Atomically settle up to 20 AI agent payments in a single XRPL Batch transaction. Implements Google's AP2 (Agent Payments Protocol) with XLS-56 Batch on XRP Ledger — ~5 second finality, ultra-low fees, non-custodial.

ClawHub Agent Skills author: CyberForexBlockchain v1.0.0 MIT-0 3 files body ≈ 1 667 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
97
Quality 40%
81
Run on models
none yet
Process rating
C
51/100
Has gaps
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token README.md:75
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - **RLUSD** — requires one-time trust line to Ripple's mainnet issuer (`rMxC…5De`)
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:154
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "recipient_address": "rM86…2XA",
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:206
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      Then use `"currency": "RLUSD"` in your mandates. RLUSD issuer is `rMxC…5De`.
      quoted

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "capabilities"
    • note frontmatter-key unknown frontmatter key "permissions"
    • note frontmatter-key unknown frontmatter key "inputs"
    • note frontmatter-key unknown frontmatter key "outputs"
    • note frontmatter-key unknown frontmatter key "requires"
    • note frontmatter-key unknown frontmatter key "protocols"
    • note frontmatter-key unknown frontmatter key "payment"

    Process rating: all ten parameters 51/100

    • 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. 3 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1667 tokens
    • 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

    • +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
    • +1No license
    • +2Single-language instructions
    • +3Description length 215: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (8 code blocks)

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

    External checks

    ClawHub: suspicious
    This is a coherent payment-settlement skill, but it deserves Review because it can create payment mandates and prepare wallet transactions through a hosted service with limited adjacent safety warnings.
    LLM: suspicious (high) · 28 May 2026