Structuring
'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
Install / Use
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-ransomware-payment-walletsInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
Our assessment of Structuring
Structuring scores 98/100 on our quality scale, 27th of 461 Security skills we index (top 6%).
Its SKILL.md is 7.5 KB long, well organised into 13 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.
With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 25 days ago, so Structuring is actively maintained.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
Structuring compared with similar skills
All 4 of these similar skills score higher than Structuring; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| Structuring (this skill)by mukul975 | 98 | 33.3k | 25d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.4k | 9d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.8k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 83.5k | today | MCP Server |
| LocalAIby mudler | 100 | 49.3k | today | MCP Server |
Frequently asked questions
- How do I install Structuring?
- Run
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill Structuring. The install tabs above show the steps for each supported agent. - Which AI agents does Structuring work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is Structuring safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is Structuring still maintained?
- The repository was last updated 25 days ago, so Structuring is actively maintained.
Skill content
View source on GitHubname: analyzing-ransomware-payment-wallets description: '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.
' domain: cybersecurity subdomain: ransomware-defense tags:
- ransomware
- blockchain
- cryptocurrency
- forensics
- threat-intelligence
- bitcoin version: 1.0.0 author: mahipal license: Apache-2.0 nist_csf:
- PR.DS-11
- RS.MA-01
- RC.RP-01
- PR.IR-01 mitre_attack:
- T1657
- T1486
mitre_f3:
version: '1.1'
tactics:
- monetization
- stealth techniques:
- id: F1018 name: Convert to Cryptocurrency tactic: monetization source: f3
- id: F1017 name: Conversion to Physical Monetary Instruments tactic: monetization source: f3
- id: F1017.001 name: 'Conversion to Physical Monetary Instruments: Cash' tactic: monetization source: f3
- id: F1047 name: Transfer of funds tactic: monetization source: f3
- id: F1045 name: Structuring tactic: stealth source: f3
Analyzing Ransomware Payment Wallets
When to Use
- An organization has been hit by ransomware and the ransom note contains a Bitcoin or cryptocurrency wallet address that needs investigation
- Law enforcement or incident responders need to trace where ransom payments flowed after the victim paid
- Threat intelligence analysts are attributing ransomware campaigns by clustering payment infrastructure across incidents
- Investigators need to determine if a ransomware group is reusing wallet infrastructure across multiple victims
- Compliance or legal teams need evidence of fund flows for prosecution, sanctions enforcement, or insurance claims
Do not use this skill for live payment interception or to interact directly with ransomware operators. All analysis should be passive and read-only against public blockchain data.
Prerequisites
- Python 3.8+ with
requests,json, andhashliblibraries - Access to blockchain explorer APIs (blockchain.com, WalletExplorer.com, Blockstream.info)
- Familiarity with Bitcoin transaction model (UTXOs, inputs, outputs, change addresses)
- Understanding of common obfuscation techniques (mixers, tumblers, peel chains, cross-chain swaps)
- Optional: Chainalysis Reactor license for enterprise-grade cluster analysis
- Optional: OXT.me for advanced transaction graph visualization
Workflow
Step 1: Extract Wallet Address from Ransom Note
Parse the ransom note to identify the payment address(es):
Common address formats:
Bitcoin (P2PKH): 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa (starts with 1)
Bitcoin (P2SH): 3J98t1WpEZ73CNmQviecrnyiWrnqRhWNLy (starts with 3)
Bitcoin (Bech32): bc1qar0srrr7xfkvy5l643lydnw9re59gtzzwf5mdq (starts with bc1)
Monero: 4... (95 characters, much harder to trace)
Ethereum: 0x... (40 hex chars)
Step 2: Query Blockchain Explorer for Transaction History
Retrieve all transactions associated with the wallet:
import requests
def get_wallet_transactions(address):
"""Query blockchain.com API for address transactions."""
url = f"https://blockchain.info/rawaddr/{address}"
resp = requests.get(url, timeout=30)
resp.raise_for_status()
data = resp.json()
return {
"address": address,
"n_tx": data.get("n_tx", 0),
"total_received_satoshi": data.get("total_received", 0),
"total_sent_satoshi": data.get("total_sent", 0),
"final_balance_satoshi": data.get("final_balance", 0),
"transactions": data.get("txs", []),
}
Step 3: Map Fund Flow and Identify Clusters
Trace outputs from the ransom wallet to downstream addresses:
Fund Flow Analysis:
━━━━━━━━━━━━━━━━━━
Victim Payment ──► Ransom Wallet ──► Consolidation Wallet
├─► Mixer/Tumbler Service
├─► Exchange Deposit Address
└─► Peel Chain (sequential small outputs)
Key indicators:
- Consolidation: Multiple ransom payments aggregated into one wallet
- Peel chains: Sequential transactions with diminishing outputs
- Mixer usage: Funds sent to known mixer addresses (Wasabi, Samourai, ChipMixer)
- Exchange cashout: Deposits to known exchange wallets (Binance, Kraken hot wallets)
Step 4: Cross-Reference with Known Wallet Databases
Check addresses against known ransomware infrastructure:
# Check WalletExplorer for entity identification
def check_wallet_explorer(address):
url = f"https://www.walletexplorer.com/api/1/address?address={address}&caller=research"
resp = requests.get(url, timeout=30)
data = resp.json()
return {
"wallet_id": data.get("wallet_id"),
"label": data.get("label", "Unknown"),
"is_exchange": data.get("is_exchange", False),
}
Step 5: Generate Attribution Report
Compile findings into a structured intelligence report:
RANSOMWARE WALLET ANALYSIS REPORT
====================================
Ransom Address: bc1q...xyz
Family Attribution: LockBit 3.0 (based on ransom note format)
Total Received: 4.25 BTC ($178,500 at time of payment)
Total Sent: 4.25 BTC (wallet fully drained)
Number of Payments: 3 (likely 3 separate victims)
FUND FLOW:
Payment 1: 1.5 BTC → Consolidation wallet → Binance deposit
Payment 2: 1.0 BTC → Wasabi Mixer → Unknown
Payment 3: 1.75 BTC → Peel chain (12 hops) → OKX deposit
CLUSTER ANALYSIS:
Related wallets: 47 addresses identified in same cluster
Total cluster volume: 156.3 BTC ($6.5M USD)
First activity: 2024-01-15
Last activity: 2024-09-22
Verification
- Confirm wallet address format is valid before querying APIs
- Cross-reference transaction timestamps with known incident timelines
- Validate cluster associations by checking common-input-ownership heuristic
- Compare findings against OFAC SDN list for sanctioned addresses
- Verify exchange attribution against multiple sources (WalletExplorer, OXT, Chainalysis)
Key Concepts
| Term | Definition | |------|------------| | UTXO | Unspent Transaction Output; the fundamental unit of Bitcoin that tracks ownership through a chain of transactions | | Cluster Analysis | Grouping multiple Bitcoin addresses believed to be controlled by the same entity using common-input-ownership and change-address heuristics | | Peel Chain | A laundering pattern where funds are sent through many sequential transactions, each peeling off a small amount to a new address | | CoinJoin/Mixer | Privacy techniques that combine multiple users' transactions to obscure the link between sender and receiver | | Common Input Ownership | Heuristic that assumes all inputs to a single transaction are controlled by the same entity |
Tools & Systems
- Chainalysis Reactor: Enterprise blockchain investigation platform with entity attribution and cross-chain tracing
- WalletExplorer: Free tool that clusters Bitcoin addresses and labels known services (exchanges, mixers, markets)
- OXT.me: Advanced Bitcoin transaction visualization with UTXO graph analysis
- Blockstream.info: Open-source Bitcoin block explorer with full API access
- blockchain.com API: Free API for querying Bitcoin address balances and transaction histories
- OFAC SDN List: U.S. Treasury sanctioned address list for compliance checking
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
