Smart-contract risk monitor, 16 chains.

The original linux install was lost when that machine was wiped, so we reconstructed everything from the surviving session logs of two production cron runs. The key insight those logs proved: cursor persistence was decoupled from analysis. The cursor advanced and was written to disk *before* the slow Slither/ML stage ran. A crash or timeout mid-analysis therefore never caused re-fetching — run 1 timed out at 64/110, run 2 timed out at 65/110 but still finished 110 fetched / 30 saved / 104 findings in the background.

The other key property: per-chain isolation. One chain failing — optimism 301, base 500 — degraded only that chain. The other 15 still completed and the run still exited 0.

The rewritten pipeline loads the chain registry from a single source of truth, fetches new verified contracts past the cursor, persists the cursor immediately, then runs Slither and the ML model over the new contracts in parallel. Each per-contract output (`[n/total] on (0xADDR...)`) is logged with its Slither finding count and ML vector set + severity. The dashboard exposes a contracts table, a findings browser, and an analysis console where operators can paste raw source or a contract address for a one-off run.

The cursor-and-analysis separation was preserved end-to-end. The ML model itself is multi-label over the vector vocabulary (selfdestruct, access_control, uninitialized_storage, unchecked_call, frontrunning, integer_overflow, dos) — slimmer than a regression, simpler to retrain. PostgreSQL schema versioning via Alembic, FastAPI backend with Pydantic v2 validation, and a Next.js 15 dashboard with middleware-protected routes behind Google OAuth round out the rebuilt platform.