Agents can evolve skills more effectively by coupling failure diagnosis with proximal optimization: diagnose what went wrong, test fixes on the same tasks, then systematically audit and remove unhelpful skill components.
SkillProx improves how AI agents learn and refine reusable task skills by combining diagnostic feedback loops with a mathematical optimization approach. Instead of treating skill edits generically, it explicitly diagnoses failures, rolls back unsuccessful changes, breaks skills into auditable components, and removes or demotes unhelpful knowledge—improving task accuracy by 3 percentage points.