Machine-Maintained Failure Taxonomy Improves ML Eval Feedback Loops

A developer built a machine-maintained failure taxonomy using Claude Code skills to track classifier errors across eval runs. The system stores structured JSON data on runs, observations, and failure classes, enabling automated analysis of recurring issues. This approach replaces manual note-taking that becomes stale after just two eval iterations.