chain-forensics
On-chain analysis and transaction forensics for blockchain security investigations. Provides capabilities for tracing fund flows, identifying suspicious patterns, MEV analysis, and generating forensic reports for incident response.
Best use case
chain-forensics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
On-chain analysis and transaction forensics for blockchain security investigations. Provides capabilities for tracing fund flows, identifying suspicious patterns, MEV analysis, and generating forensic reports for incident response.
Teams using chain-forensics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/chain-forensics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How chain-forensics Compares
| Feature / Agent | chain-forensics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
On-chain analysis and transaction forensics for blockchain security investigations. Provides capabilities for tracing fund flows, identifying suspicious patterns, MEV analysis, and generating forensic reports for incident response.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Chain Analysis/Forensics Skill
Expert on-chain analysis and transaction forensics for security investigations and incident response.
## Capabilities
- **Transaction Tracing**: Follow fund flows across addresses and protocols
- **Pattern Detection**: Identify suspicious patterns (wash trading, rugpulls, sandwich attacks)
- **MEV Analysis**: Analyze MEV activity and flashbots bundles
- **Address Clustering**: Group related addresses and identify ownership
- **Cross-Chain Tracking**: Track bridged assets across chains
- **Forensic Reports**: Generate detailed investigation reports
## MCP/Tool Integration
| Tool | Purpose | Reference |
|------|---------|-----------|
| **Phalcon MCP** | Transaction analysis, exploit detection | [phalcon-mcp](https://github.com/mark3labs/phalcon-mcp) |
| **whale-tracker-mcp** | Large transaction monitoring | [whale-tracker](https://github.com/kukapay/whale-tracker-mcp) |
| **bicscan-mcp** | Address risk scoring | [bicscan](https://github.com/ahnlabio/bicscan-mcp) |
| **dune-analytics-mcp** | Custom queries, analytics | [dune](https://github.com/kukapay/dune-analytics-mcp) |
| **Etherscan MCP** | Block explorer data | [etherscan](https://github.com/haomingdev/etherscan-mcp) |
## Transaction Tracing
### Basic Flow Analysis
```bash
# Get transaction details
cast tx 0xTxHash --rpc-url $RPC
# Decode transaction input
cast 4byte-decode $(cast tx 0xTxHash --rpc-url $RPC | grep input)
# Get internal transactions via Etherscan API
curl "https://api.etherscan.io/api?module=account&action=txlistinternal&txhash=0xTxHash&apikey=$KEY"
```
### Tracing with Tenderly/Phalcon
```javascript
// Phalcon trace analysis
const trace = await phalcon.analyzeTransaction(txHash);
// Identify key flows
const flows = {
valueTransfers: trace.transfers.filter(t => t.value > 0),
tokenTransfers: trace.erc20Transfers,
internalCalls: trace.calls.filter(c => c.type === 'CALL'),
delegateCalls: trace.calls.filter(c => c.type === 'DELEGATECALL')
};
```
## Address Analysis
### Profile Building
```javascript
const addressProfile = {
address: '0x...',
// Basic metrics
metrics: {
firstTransaction: '2022-01-15',
transactionCount: 1234,
uniqueInteractions: 56,
totalValueTransferred: '1000 ETH'
},
// Activity patterns
patterns: {
activeHours: [14, 15, 16], // UTC hours
frequentProtocols: ['Uniswap', 'Aave'],
averageTxFrequency: '5/day'
},
// Risk indicators
riskFlags: {
tornadoCashInteraction: false,
sanctionedAddressInteraction: false,
knownExploitPattern: false,
highFrequencyTrading: true
},
// Related addresses
clusters: [
{ address: '0x...', confidence: 0.95, reason: 'Funding source' },
{ address: '0x...', confidence: 0.8, reason: 'Common recipient' }
]
};
```
### Clustering Heuristics
1. **Deposit Address Reuse**: Same deposit addresses across exchanges
2. **Multi-Input Transactions**: Addresses used together in single tx
3. **Timing Analysis**: Coordinated transaction timing
4. **Amount Patterns**: Matching amounts minus fees
5. **Contract Interactions**: Shared smart contract usage patterns
## MEV Analysis
### Sandwich Attack Detection
```sql
-- Dune Analytics query for sandwich detection
WITH potential_sandwiches AS (
SELECT
block_number,
transaction_index,
"from",
"to",
value,
LAG("from") OVER (PARTITION BY block_number ORDER BY transaction_index) as prev_from,
LEAD("from") OVER (PARTITION BY block_number ORDER BY transaction_index) as next_from
FROM ethereum.transactions
WHERE block_number > {{start_block}}
)
SELECT *
FROM potential_sandwiches
WHERE prev_from = next_from
AND prev_from != "from"
-- Additional filters for DEX interactions
```
### Flashbots Bundle Analysis
```javascript
// Analyze flashbots bundles
const bundleAnalysis = {
bundleHash: '0x...',
transactions: [
{ index: 0, type: 'frontrun', profit: '0.5 ETH' },
{ index: 1, type: 'victim', loss: '0.3 ETH' },
{ index: 2, type: 'backrun', profit: '0.4 ETH' }
],
totalMEV: '0.9 ETH',
miner: '0x...',
minerPayment: '0.45 ETH'
};
```
## Suspicious Pattern Detection
### Rugpull Indicators
```javascript
const rugpullIndicators = {
// Contract analysis
contract: {
hasHiddenMint: true, // Owner can mint unlimited
hasDisableTrading: true, // Can disable selling
hasBlacklist: true, // Can block addresses
highOwnershipConcentration: true, // >50% in few wallets
unverifiedContract: true,
recentDeployment: true // <7 days old
},
// Token metrics
tokenMetrics: {
liquidityLocked: false,
lockDuration: 0,
holderCount: 50,
top10HoldersPercent: 85
},
// Trading patterns
tradingPatterns: {
artificialVolume: true, // Wash trading detected
sellPressure: 'high',
buyWallsArtificial: true
},
riskScore: 95 // 0-100
};
```
### Wash Trading Detection
```sql
-- Identify circular trading
WITH transfers AS (
SELECT
"from",
"to",
contract_address,
value,
block_time
FROM erc20_ethereum.evt_Transfer
WHERE contract_address = {{token_address}}
AND block_time > NOW() - INTERVAL '7 days'
)
SELECT
a."from" as trader,
COUNT(DISTINCT b."to") as counterparties,
SUM(a.value) as total_volume,
COUNT(*) as trade_count
FROM transfers a
JOIN transfers b ON a."to" = b."from" AND a."from" = b."to"
WHERE a.block_time < b.block_time
AND b.block_time < a.block_time + INTERVAL '1 hour'
GROUP BY a."from"
HAVING COUNT(*) > 10
ORDER BY total_volume DESC
```
## Cross-Chain Tracking
### Bridge Transaction Mapping
```javascript
const crossChainTrace = {
originChain: 'ethereum',
originTx: '0x...',
originAddress: '0x...',
bridge: 'Wormhole',
bridgeMessage: '0x...',
destinationChain: 'arbitrum',
destinationTx: '0x...',
destinationAddress: '0x...',
amount: '100 USDC',
timestamp: {
origin: '2024-01-15T10:00:00Z',
destination: '2024-01-15T10:15:00Z'
}
};
```
### Multi-Chain Address Mapping
```javascript
// Track address across chains
const multiChainProfile = {
primaryAddress: '0x...',
chainPresence: {
ethereum: { address: '0x...', balance: '10 ETH', txCount: 500 },
arbitrum: { address: '0x...', balance: '5 ETH', txCount: 200 },
optimism: { address: '0x...', balance: '3 ETH', txCount: 100 },
polygon: { address: '0x...', balance: '1000 MATIC', txCount: 50 }
},
bridgeHistory: [
{ from: 'ethereum', to: 'arbitrum', amount: '5 ETH', date: '2024-01-10' },
{ from: 'ethereum', to: 'optimism', amount: '3 ETH', date: '2024-01-12' }
]
};
```
## Forensic Report Template
```markdown
# Blockchain Forensic Investigation Report
## Executive Summary
- **Investigation ID**: INV-2024-XXX
- **Date Range**: 2024-01-01 to 2024-01-15
- **Subject**: [Address/Protocol/Incident]
- **Conclusion**: [Brief finding]
## Key Findings
### 1. Fund Flow Analysis
[Diagram and description of fund movements]
### 2. Address Attribution
| Address | Attribution | Confidence | Evidence |
|---------|-------------|------------|----------|
| 0x... | Attacker | High | Funding pattern |
| 0x... | Mixer | Medium | Tornado Cash |
| 0x... | Exchange | High | Known deposit |
### 3. Timeline
| Timestamp | Event | Addresses | Amount |
|-----------|-------|-----------|--------|
| T+0 | Initial exploit | 0x... | 1000 ETH |
| T+1h | Consolidation | 0x... | 1000 ETH |
| T+2h | Mixer deposit | Tornado | 100 ETH |
### 4. Attack Vector
[Technical description of how the incident occurred]
### 5. Total Impact
- Funds Lost: $X
- Users Affected: Y
- Contracts Exploited: Z
## Appendix
- Full transaction list
- Address clustering data
- Supporting evidence
```
## Process Integration
This skill integrates with:
- `incident-response-exploits.js` - Exploit investigation
- `economic-simulation.js` - Market impact analysis
- `smart-contract-security-audit.js` - Post-audit monitoring
## Tools Reference
| Tool | Purpose | URL |
|------|---------|-----|
| **Etherscan** | Explorer, API | [etherscan.io](https://etherscan.io) |
| **Dune Analytics** | Custom queries | [dune.com](https://dune.com) |
| **Nansen** | Wallet labels, flows | [nansen.ai](https://nansen.ai) |
| **Arkham Intelligence** | Entity attribution | [arkhamintelligence.com](https://www.arkhamintelligence.com) |
| **Chainalysis Reactor** | Investigation platform | [chainalysis.com](https://www.chainalysis.com) |
| **TRM Labs** | Risk scoring | [trmlabs.com](https://www.trmlabs.com) |
| **Phalcon** | Tx analysis | [phalcon.blocksec.com](https://phalcon.blocksec.com) |
## See Also
- `agents/incident-response/AGENT.md` - Incident commander agent
- `skills/bug-bounty/SKILL.md` - Disclosure coordination
- `incident-response-exploits.js` - Full incident processRelated Skills
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