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CC analyzing-ransomware-payment-wallets

Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs, identifying wallet clusters and tracking fund movement through mixers and exchanges to support law enforcement attribution. Use when tracing ransomware bitcoin payments, performing cryptocurrency wallet forensics, or gathering blockchain threat intelligence on extortion payments.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 1 595 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs…

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

AnalyzerAI and agentsPeople and hiringtype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
82/100
safety, quality, tests
Safety 60%
84
Quality 40%
79
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 16

    ✓ No critical or high findings

    Medium and low: 16
    • low Risky intent intent-offensive-security references/api-reference.md:1
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # API Reference: Ransomware Payment Wallet Analysis
    • low Secrets in code secret-high-entropy-token references/api-reference.md:75
      High-entropy token-like string (may be an id, hash or a credential)
      | P2PKH (Legacy) | 1 | 1A1z…fNa | Original format |
    • low Secrets in code secret-high-entropy-token references/api-reference.md:76
      High-entropy token-like string (may be an id, hash or a credential)
      | P2SH (SegWit compatible) | 3 | 3J98…NLy | Script hash |
    • low Risky intent intent-offensive-security references/api-reference.md:80
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## Common Ransomware Wallet Indicators
    • low Secrets in code secret-high-entropy-token scripts/agent.py:27
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "12t9…SMw": "WannaCry",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/agent.py:28
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "13AM…b94": "WannaCry",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/agent.py:29
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "115p…rLn": "WannaCry",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/agent.py:30
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "1Mz7…BWX": "DarkSide (Colonial Pipeline)",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/agent.py:184
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      print("  python agent.py 12t9…SMw")
      quoted
    • low Risky intent intent-offensive-security SKILL.md:2
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      name: analyzing-ransomware-payment-wallets
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      description: 'Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs, identifying wallet clusters and track
      quoted
    • low Risky intent intent-offensive-security SKILL.md:7
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      subdomain: ransomware-defense
    • low Risky intent intent-offensive-security SKILL.md:9
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - ransomware
    • low Risky intent intent-offensive-security SKILL.md:54
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # Analyzing Ransomware Payment Wallets
    • low Secrets in code secret-high-entropy-token SKILL.md:83
      High-entropy token-like string (may be an id, hash or a credential)
      Bitcoin (P2PKH):   1A1z…fNa  (starts with 1)
    • low Secrets in code secret-high-entropy-token SKILL.md:84
      High-entropy token-like string (may be an id, hash or a credential)
      Bitcoin (P2SH):    3J98…NLy  (starts with 3)

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"
    • note frontmatter-key unknown frontmatter key "mitre_f3"

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1595 tokens

    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
    • +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
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 429: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.