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BytesAgain β€Ί Skills β€Ί OnChain Analysis
πŸ¦€ ClawHub

OnChain Analysis

by @mzfshark

Interpret blockchain data strategically; identify patterns, anomalies, and flows with data-backed evidence and explicit uncertainty.

Versionv2.0.0
Downloads690
#crypto-defi#legal
TERMINAL
clawhub install onchain-analysis

πŸ“– About This Skill

name: onchain-analysis description: Interpret blockchain data strategically; identify patterns, anomalies, and flows with data-backed evidence and explicit uncertainty. metadata: author: Morpheus version: 2.0.0 owner: Morpheus Agent category: onchain

SKILL: onchain-analysis

Purpose

Interpret blockchain data strategically: identify patterns, detect anomalies, map flows, and surface risk signals β€” data-backed only.

When to Use

  • Wallet/contract behavior seems suspicious or unclear
  • You need to understand fund flows before a decision
  • Investigating market behavior, insider movement, or protocol health

    • Inputs

  • wallet_data (optional): addresses + labels + balances
  • contract_data (optional): contract address + ABI/artifacts + known roles
  • transactions (required): tx list or tx ids/hashes
  • chain (optional): chain + timeframe

    • Steps

    1. Normalize inputs: - ensure chain/time window is explicit - ensure transactions are uniquely identified 2. Identify patterns: - recurring counterparties - periodic deposits/withdrawals - concentration and dispersion patterns 3. Detect anomalies: - sudden large transfers - new counterparties with high volume - unusual contract interactions 4. Map flows: - sources β†’ sinks - intermediate hops - aggregator/bridge interactions (label as such) 5. Evaluate intent as hypotheses: - propose 1–3 plausible explanations - attach confidence and what evidence would change it 6. Produce action-oriented output: - risk signals - what to verify next

    Validation

  • Include tx hashes / block references when possible.
  • Distinguish facts from hypotheses.
  • If data is incomplete, state the missing pieces explicitly.

    • Output

  • insights (facts + patterns)
  • risk_signals
  • opportunities (only if supported by data)
  • hypotheses (with confidence)
  • next_checks

    • Safety Rules

  • Data-backed only; no β€œmind reading” claims.
  • Do not assist illicit activity or evasion.

    • Example

    Input: 200 txs for one wallet over 30 days. Output: β€œHigh concentration into 2 addresses; one new counterparty accounts for 70% volume; verify entity labels + bridge usage.”

    ⚑ When to Use

    TriggerAction
    - You need to understand fund flows before a decision
    - Investigating market behavior, insider movement, or protocol health

    πŸ’‘ Examples

    Input: 200 txs for one wallet over 30 days. Output: β€œHigh concentration into 2 addresses; one new counterparty accounts for 70% volume; verify entity labels + bridge usage.”

    πŸ”’ Constraints

  • Include tx hashes / block references when possible.
  • Distinguish facts from hypotheses.
  • If data is incomplete, state the missing pieces explicitly.