Can agents improve their own skills without generating any new rollouts? This paper introduces SkillRefiner, which learns entirely from historical agent traces. It turns past mistake patterns into targeted edits to the agent’s existing skill, by basically treat deployment history like a bug report database. Additionally, repeated successful behaviors get reinforced, while repeated failures become new guardrails, with an extra evidence check to avoid learning the wrong lesson. Across all…