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AC drain-mcp

MCP server for the Handshake58 AI marketplace. Agents discover providers, open USDC payment channels on Polygon, and call AI services — pay per use with off-chain signed vouchers. No API keys, no subscriptions.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 365 tokens Open the sourcegithub.com↗ analyzed 2 d ago

MCP server for the Handshake58 AI marketplace.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

IntegrationGitHubAI 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
92/100
safety, quality, tests
Safety 60%
96
Quality 40%
87
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token SKILL.md:137
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      await usdc.approve('0x1C…e64', amount);
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:179
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      to: '0x1C…e64',
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:230
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - **Handshake58 Channel**: `0x1C…e64`
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:231
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - **USDC**: `0x3c…359`
      quoted

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 57/100

    • 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. 7 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 28 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2365 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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)
    • +2Single-language instructions
    • +3Description length 210: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 28 items
    • +3Output format is stated explicitly
    • +4Has examples (9 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.