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AB paegents-pay

Use Paegents through the published SDK and API surface to register services, create usage agreements, activate bilateral escrow, route metered usage, and settle with the recommended execution mode.

ClawHub Agent Skills author: Mark McDaniels v2.9.1 MIT-0 9 files body ≈ 1 365 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 73/100 · Nearly there — weak spots: result and completion, inputs and preconditions, consistency

IntegrationInfrastructuretype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
B
73/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
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 references/SDK_USAGE.md:52
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      | ERC-2612 permit funding | `buil…ion(...)` | `buil…ion(...)` from `paegents/escrow` |
      placeholder

    Files scanned: 9. 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 73/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 3 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (paegents-pay) differs from the folder (agent-payment-processor)
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 55 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 1365 tokens
    • 100Progress reporting. Reports progress

    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 197: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 55 items
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

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

    External checks

    ClawHub: clean
    This is a coherent payment-integration skill, but it handles real payment actions, API keys, seller credentials, proxy traffic, and webhooks, so users should install it only with scoped credentials and supervision.
    LLM: benign (high) · VirusTotal: · 29 May 2026