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BD misttrack-skills

使用 MistTrack OpenAPI 进行加密货币地址风险分析、AML 合规检测和交易追踪。MistTrack 是由 SlowMist 开发的反洗钱追踪工具，支持 BTC、ETH、TRX、BNB 等主流链上地址与交易的风险评分、标签查询、交易调查等功能。

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 5 733 tokens Open the sourcegithub.com↗ analyzed 2 d ago

使用 MistTrack OpenAPI 进行加密货币地址风险分析、AML 合规检测和交易追踪。MistTrack 是由 SlowMist 开发的反洗钱追踪工具，支持 BTC、ETH、TRX、BNB 等主流链上地址与交易的风险评分、标签查询、交易调查等功能。

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI 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
B
79/100
safety, quality, tests
Safety 60%
90
Quality 40%
63
Run on models
none yet
Process rating
D
42/100
Unfinished process
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 10

✓ No critical or high findings

Medium and low: 10
  • low Secrets in code secret-high-entropy-token README.md:30
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - 帮我检测 `0x64…42f` 这个 ETH 地址的风险评分
    quoted
  • low Secrets in code secret-high-entropy-token README.md:31
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - 这个 TRX 地址 `TNfK…3Yh` 安全吗？有没有洗钱记录？
    quoted
  • low Secrets in code secret-high-entropy-token README.md:36
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - 对 `0x64…42f` 做一个完整的链上调查，包括标签、余额概览、风险评分、平台交互历史和主要交易对手
    quoted
  • low Secrets in code secret-high-entropy-token README.md:37
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - 这个 BTC 地址 `1A1z…ivf` 的资金来源和流向是什么？
    quoted
  • low Secrets in code secret-high-entropy-token README.md:44
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - 将我的 0.1 ETH 兑换成 USDT 到 `0x64…42f`（会自动检测目标地址风险）
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:339
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "address": "TNXo…32G",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:726
    High-entropy token-like string (may be an id, hash or a credential)
    --address 0xd9…31b \
  • low Secrets in code secret-high-entropy-token SKILL.md:731
    High-entropy token-like string (may be an id, hash or a credential)
    --address 5tzF…FwN \
  • low Secrets in code secret-high-entropy-token SKILL.md:736
    High-entropy token-like string (may be an id, hash or a credential)
    --address 0x28…d60 \
  • low Secrets in code secret-high-entropy-token SKILL.md:751
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "address": "0x28…d60",
    quoted

Files scanned: 7. 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 body-long SKILL.md body ≈ 5733 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 42/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5733 tokens
  • 100Steps. 53 steps
  • 100Consistency. Name and required fields are in place
  • low 10 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)
  • +3Output format is not stated: the model decides each time
  • -240 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 129: enough signal without eating the budget
  • +4Structure: 49 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (29 code blocks)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.