SKILLEMALL.ai
GitHub ↗ RU Sign in

AD agent-to-agent-payments

Monetize your AI agent. Charge for API calls, services, or data. Accept payments autonomously — no human needed. Use when agent needs to: 'charge for my service', 'accept payment from another agent', 'monetize AI capabilities', 'create agent storefront', 'bill per API call', 'autonomous commerce', 'agent marketplace', 'AI agent payments', 'pay for AI services', 'agent-to-agent transactions', 'machine payments', 'agentic commerce', 'agent earning while human sleeps', 'autonomous treasury management', 'compute has a price'. Built on PayRam MCP — no KYC, no Stripe account needed, USDC/USDT/ETH/BTC on Base, Ethereum, Polygon, Tron, TON. TON micropayments ideal for Telegram-integrated agents.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 835 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Monetize your AI agent.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationTelegramStripeAI and agentstype 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%
100
Quality 40%
83
Run on models
none yet
Process rating
D
43/100
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: 1. 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 43/100

    • 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
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 835 tokens

    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
    • -214 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +3Description length 696: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.