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BC misttrack-skills

Cryptocurrency address risk analysis, AML compliance checks, and on-chain transaction tracing using the MistTrack OpenAPI. MistTrack is an anti-money laundering tracking tool developed by SlowMist, supporting risk scoring, label lookup, and transaction investigation for BTC, ETH, TRX, BNB, and other major chains.

ClawHub Agent Skills author: MistTrack v0.2.12 MIT-0 16 files body ≈ 1 242 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerSecurityInfrastructuretype 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
81/100
safety, quality, tests
Safety 60%
88
Quality 40%
70
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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.
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 · 12

✓ No critical or high findings

Medium and low: 12
  • low Secrets in code secret-high-entropy-token README.md:57
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Check the risk score for ETH address `0x64…42f`
    quoted
  • low Secrets in code secret-high-entropy-token README.md:58
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Is TRX address `TNfK…3Yh` safe? Any money laundering history?
    quoted
  • low Secrets in code secret-high-entropy-token README.md:63
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Run a complete on-chain investigation on `0x64…42f` - labels, balance, risk score, platform interactions, and counterparties
    quoted
  • low Secrets in code secret-high-entropy-token README.md:64
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Where did the funds in BTC address `1A1z…ivf` come from and go to?
    quoted
  • low Secrets in code secret-high-entropy-token README.md:69
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Help me check whether this ETH address `0x84…39d` is a multisig address
    quoted
  • low Secrets in code secret-high-entropy-token scripts/multisig_analysis.py:35
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python3 scripts/multisig_analysis.py --address 3J98…NLy --chain btc
    quoted
  • low Secrets in code secret-high-entropy-token scripts/multisig_analysis.py:38
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python3 scripts/multisig_analysis.py --address 0x84…39d --chain eth
    quoted
  • low Secrets in code secret-high-entropy-token scripts/multisig_analysis.py:41
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    python3 scripts/multisig_analysis.py --address TJCn…xMW --chain trx
    quoted
  • low Secrets in code secret-high-entropy-token scripts/pay.py:32
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    --token 0x83…913 \
    quoted
  • low Secrets in code secret-high-entropy-token scripts/pay.py:34
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    --to 0x20…87C \
    quoted
  • low Secrets in code secret-high-entropy-token skills/payment.md:67
    High-entropy token-like string (may be an id, hash or a credential)
    --token 0x83…913 \
  • low Secrets in code secret-high-entropy-token skills/payment.md:69
    High-entropy token-like string (may be an id, hash or a credential)
    --to 0x20…87C \

Files scanned: 16. 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")
  • note frontmatter-key unknown frontmatter key "optional_env_vars"

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Consistency. Frontmatter name (misttrack-skills) differs from the folder (misttrack-aml-skills)
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 8 steps
  • 100Execution cost. Instruction body is 1242 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 314: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (4 code blocks)
  • +3All 6 scripts are documented

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

External checks

ClawHub: suspicious
The AML features are mostly coherent, but the skill also bundles wallet-key handling and crypto payment signing that users should review carefully.
LLM: suspicious (high) · VirusTotal: · 29 May 2026