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BF defi-yield

DeFi 收益聚合器（Yield Aggregator）助手。帮助用户找到最佳收益策略、 分析各协议 APY、执行自动复投、追踪仓位收益。 当用户提到以下内容时激活： - "收益聚合"、"DeFi 收益"、"撸收益" - "Yearn Finance"、"Beefy Finance"、"Pendle"、"Gamma" - "APY 查询"、"哪个池子收益最高"、"收益策略" - "自动复投"、"流动性挖矿"、"收益农场" - "质押收益"、"LP 收益"、"稳定币收益" - 查询 APY、对比收益、分析风险、执行操作

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 3 513 tokens Open the sourcegithub.com↗ analyzed 2 d ago

DeFi 收益聚合器（Yield Aggregator）助手。帮助用户找到最佳收益策略、 分析各协议 APY、执行自动复投、追踪仓位收益。 当用户提到以下内容时激活： - "收益聚合"、"DeFi 收益"、"撸收益" - "Yearn Finance"、"Beefy…

As a process F 31/100 · Will not run — References files that are not bundled: scripts/position_tracker.py

ProcedureSoftware 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
B
88/100
safety, quality, tests
Safety 60%
97
Quality 40%
74
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: scripts/position_tracker.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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:321
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "0x5f…892": "yUSDC",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:322
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "0xa3…2D9": "yUSDT",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:323
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "0x8d…0E1": "yDAI",
    quoted

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/position_tracker.py

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: scripts/position_tracker.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/position_tracker.py
  • 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
  • 40Consistency. Frontmatter name (defi-yield) differs from the folder (shenmeng-defi-yield)
  • 100Steps. 22 steps
  • 100Execution cost. Instruction body is 3513 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 6 example trigger phrases
  • +3Description length 262: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (8 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.