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AC handling-transactions

Token transactions and wallet integration for XMTP agents. Use when sending USDC, creating transaction requests, or handling transaction confirmations. Triggers on USDC transfer, wallet calls, or transaction reference.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 860 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Token transactions and wallet integration for XMTP agents.

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

IntegrationAI 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
A
93/100
safety, quality, tests
Safety 60%
96
Quality 40%
89
Run on models
none yet
Process rating
C
57/100
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token SKILL.md:47
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Base Sepolia | 84532 | `0x03…F7e` |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:48
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Base Mainnet | 8453 | `0x83…913` |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:74
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "base-sepolia": { address: "0x03…F7e", decimals: 6 },
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:75
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "base-mainnet": { address: "0x83…913", decimals: 6 },
      quoted

    Files scanned: 4. 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 57/100

    • 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
    • 30Running it twice. 10 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 860 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
    • +2Single-language instructions
    • +3Description length 218: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.