SkillEvo is a self-renewing evolution gradient method based on multi-turn interaction feedback. Traditional intelligent agent skills are either hand-authored or generated in a single pass, with no closed loop to improve from the interaction failures they cause. Recent work has closed this loop, but uses single-turn question-answering evaluation to derive feedback. This results in a sharp asymmetry: once the first round has patched the gaps that a single exchange can reveal, the evolution gradient decays, and defects that surface only across multiple turns remain invisible. SkillEvo addresses this by recasting multi-turn user simulation from an evaluation endpoint into a feedback generator, and replacing the passive rejection of a scalar gate with an independent governance layer that actively repairs factual degradation and structural bloat. Across six categories of cloud services, 9 production skills, and 98 skill-reference files, SkillEvo surpasses self-reflection-based evolution by 23.0 points and single-turn-QA-driven evolution by 15.4 points. Blogger's Review: SkillEvo is a promising method that updates evolution gradients through multi-turn interaction feedback, effectively improving intelligent agent skills. The key to this method is transforming multi-turn user simulation into a feedback generator and using an independent governance layer to repair factual degradation and structural bloat. This method can be applied to multiple fields, including cloud services and natural language processing.