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BC nevermined-payments

Integrates Nevermined payment infrastructure into AI agents, MCP servers, Google A2A agents, and REST APIs. Handles x402 protocol, credit billing, payment plans, and SDK integration for TypeScript (@nevermined-io/payments) and Python (payments-py).

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 10 files body ≈ 4 380 tokens Open the sourcegithub.com↗ analyzed 25 h ago

Integrates Nevermined payment infrastructure into AI agents, MCP servers, Google A2A agents, and REST APIs.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

IntegrationDiscordAI and agentsSoftware developmenttype 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
B
88/100
safety, quality, tests
Safety 60%
97
Quality 40%
74
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 references/payment-plans.md:22
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const USDC_ADDRESS = '0x03…F7e'
    quoted
  • low Secrets in code secret-high-entropy-token references/payment-plans.md:72
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    USDC_ADDRESS = '0x03…F7e'
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:462
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    --price-config '{"tokenAddress":"0x03…F7e","price":10000000,"amountOfCredits":100}' \
    quoted

Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4380 tokens
  • 100Steps. 44 steps
  • 100Consistency. Name and required fields are in place
  • low 13 top-level sections: this looks like several domains in one skill

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 248: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (20 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.