Building a Personal Risk-Episode Tracker with OpenClaw: A DeFi Rug-Pull Case Study

A Reddit user who lost a chunk of savings in a DeFi rug pull ("NexaVault") used OpenClaw to build a private risk-episode tracker. The goal wasn't fraud detection or budgeting — it was catching dangerous self-authorized moves: large relative transfers, concentrated destination, obsessive monitoring reminders, social pressure, and creeping debt.

Key Design Decisions

• Real data, not memory: OpenClaw pulled actual numbers from bank records and corrected the user's own writeup (amount and date were wrong).
• Episode grouping: It combined one real-world event scattered across 5 apps (bank withdrawal alert, deposit email, daily "check position" reminder, hype texts) into a single episode, separating primary evidence (transaction + confirmations) from supporting context (reminders, messages, rising card balance).
• Privacy-first: Stored reference summaries, not raw message text — because the screen might be open in front of family.
• Baseline comparison: Explicitly contrasted the rug-pull pattern against normal large payments (mortgage, payroll, childcare) to avoid false alarms on routine transactions.

Unexpected Results

The user was surprised that OpenClaw: corrected their own flawed memory from bank records; grouped messy evidence across apps; and wrote design decisions into memory for iterative refinement. The tracker also learned the difference between "big but normal" and "start of a spiral."

The full thread explores how others are modeling the same distinction — check the source for community discussion.