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AB send-token

Transfer tokens on Solana or Base (EVM). Use when the user wants to send, transfer, or pay tokens. Supports native coins (SOL, ETH) and tokens (USDC) by name, plus arbitrary tokens by mint/contract address. Covers "send SOL", "transfer USDC", "send tokens", "pay someone", "send ETH on Base", "transfer to address".

modbender/skill-library-mcp Claude Code author: modbender MIT 1 file body ≈ 1 381 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Transfer tokens on Solana or Base (EVM).

As a process B 74/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

Proceduretype 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
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
89
Run on models
none yet
Process rating
B
74/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:88
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      npx @openant-ai/cli@latest wallet send solana 4zMM…cDU 25 7xKa…... --json
      placeholder
    • low Secrets in code secret-high-entropy-token SKILL.md:94
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      npx @openant-ai/cli@latest wallet send base 0x83…913 10 0xAbCdEf... --json
      placeholder

    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 74/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 17 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 22 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1381 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 315: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (8 code blocks)

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