address-profiling

name	address-profiling
description	Analyze address behavior patterns from transaction history. Detect anomalies, identify activity patterns, and alert on suspicious changes.

Address Profiling Skill

When to use this skill

Use this skill to:

• Analyze an address's transaction behavior patterns
• Detect unusual activity (frequency spikes, large transfers, new counterparties)
• Profile addresses before interacting (is this a normal user or bot?)
• Monitor saved contacts for behavioral changes
• Get insights: "Is this address actively trading or just holding?"

Analysis Performed

📊 Transaction Pattern Analysis

• Frequency: Transactions per day/week/month
• Volume: Average/median/max transfer amounts
• Direction: Ratio of sent vs received
• Active hours: Time-of-day patterns
• Regularity: Consistent patterns vs sporadic activity

🔍 Anomaly Detection

• Sudden spikes in transaction frequency
• Unusual amounts (outliers from normal range)
• New counterparties (never interacted before)
• Time anomalies (activity at unusual hours)
• Dormant awakening (long inactive then sudden activity)

🏷️ Address Classification

• Exchange deposit: Regular small deposits
• Whale: Large holdings, infrequent large transfers
• Active trader: High-frequency swaps
• Smart contract: Automated patterns
• Normal user: Varied, human-like behavior
• Possible bot: Highly regular patterns

Usage

Basic Profiling

from skills.address_profiling.scripts.analyze_address import profile_address

result = await profile_address("TXXXabc...")
# Or use alias from address book:
result = await profile_address("妈妈")

With Time Range

result = await profile_address(
    address="TXXXabc...",
    max_transactions=1000,  # Last 1000 txs or 1 year
    detect_anomalies=True
)

Output Example

📊 Address Profile: 妈妈 (TXXXabc...abc)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🏷️ Classification: Active Trader
⏱️ Analysis Period: 2025-02-08 to 2026-02-08 (365 days)
📈 Total Transactions: 847

Activity Summary:
  • Daily Average: 2.3 transactions
  • Peak Activity: 15:00-18:00 UTC+8
  • Most Active Token: USDT (67%)
  
Transaction Patterns:
  ✓ Regular activity (no long gaps)
  ✓ Consistent amounts ($50-$500 range)
  ✓ 15 unique counterparties
  
⚠️ Anomalies Detected: 2

  1. 🚨 Large Transfer Spike (2026-02-01)
     Sent 5,000 USDT (10x normal amount)
     Recommendation: Verify this was intentional
     
  2. ⚠️ New Counterparty (2026-02-05)
     First interaction with TYYYnew...
     Recommendation: Check counterparty security

Risk Assessment: LOW
💡 This address shows normal user behavior with occasional
   large transfers. Recent activity aligns with patterns.

Integration with Address Book

Automatically resolves aliases:

User: "分析一下妈妈这个地址的交易情况"
Agent: Looks up "妈妈" → TXXXabc... → Profiles address

Data Sources

• TronScan API: Transaction history
• TronGrid: Block timestamps
• Address Book: Alias resolution

Privacy

• ⚠️ Only analyzes public blockchain data
• 🔒 Analysis results stored locally (optional)
• ✅ No private information required