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AB trade

Swap or trade tokens on Base network. Use when you or the user want to trade, swap, exchange, buy, sell, or convert between tokens like USDC, ETH, and WETH. Covers phrases like "buy ETH", "sell ETH for USDC", "convert USDC to ETH", "get some ETH".

modbender/skill-library-mcp Claude Code author: modbender MIT 1 file body ≈ 988 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Swap or trade tokens on Base network.

As a process B 66/100 · Nearly there — weak spots: result and completion, when it triggers, progress reporting

Generatortype 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%
97
Quality 40%
89
Run on models
none yet
Process rating
B
66/100
Nearly there
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

    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 SKILL.md:62
      High-entropy token-like string (may be an id, hash or a credential)
      | usdc  | USDC  | 6        | 0x83…913 |
    • low Secrets in code secret-high-entropy-token SKILL.md:63
      High-entropy token-like string (may be an id, hash or a credential)
      | eth   | ETH   | 18       | 0xEe…EeE |
    • low Secrets in code secret-high-entropy-token SKILL.md:87
      High-entropy token-like string (may be an id, hash or a credential)
      npx awal@latest trade 100 0x83…913 0x42…006

    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 66/100

    • 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
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 988 tokens
    • 100Running it twice. No mutating operations

    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 4 example trigger phrases
    • +3Description length 247: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (3 code blocks)

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