onchain flow attribution

The problem

Attributing blockchain transfer data manually leads to double-counting and false positives. Most tools fail to distinguish between internal entity reshuffling and genuine external demand, skewing risk and volume metrics.

What it does

• Normalizes multi-chain transfer logs using chain-qualified address keys and event-level deduplication.
• Separates internal transfers within the same reviewed entity from external inflows and outflows.
• Aggregates flows per chain and token while maintaining strict unit scale isolation.
• Identifies anomalies by comparing current flow snapshots against historical baselines and market context.
• Flags attribution gaps and uncertain labels for manual review instead of forcing inaccurate classifications.

Frameworks & tools

Python 3 for analysis scripts, JSON for data input/output, and chain-specific transaction logs.

Why this beats prompting it yourself

General LLMs often hallucinate entity relationships or fail to handle the precision required for log index deduplication. This skill enforces a strict normalization and attribution logic that prevents common accounting errors like merging cross-chain address strings or misinterpreting bridge legs.

Use cases

• Auditing exchange inflow vs. internal wallet management.
• Monitoring for anomalous whale movements against a historical baseline.
• Separating bridge mint/burn semantics from market-driven transfers.

Known limitations

Does not include node indexers, automated wallet clustering, or cross-chain bridge matching. Unknown attributions are flagged but excluded from reviewed totals.