Developer builds Molt to catch silent scraper failures through data distribution checks

A developer has created an open-source tool called Molt to address a critical blind spot in web scraper monitoring, where scrapers return HTTP 200 success codes even while silently delivering corrupted or zeroed-out data. Traditional monitoring checks — such as endpoint reachability, row counts, and schema validation — fail to detect cases where numeric fields collapse in value without triggering any alerts. Molt works by snapshotting scraper output and comparing statistical distributions against a trusted baseline, flagging anomalies like a median dropping from 20,251 to 0. When drift is detected, the tool diagnoses affected fields, generates a targeted repair instruction, and routes it through a mandatory human approval gate before any changes are applied. The system then re-runs the scraper to verify that the data has genuinely recovered, closing the loop from detection to confirmed resolution.