Skills help LLM agents reuse procedural knowledge in recurring workflows, but broad transferability alone does not guarantee workflow fit: a reusable skill may be semantically relevant yet fail under local tools, files, formats, and failure modes. We propose SkillMe, a workflow-situated skill evolution framework that adapts agent skills through repeated use in matched workflow recurrences. A Publisher induces and validates an initial skill from a successful execution, while Executors use the skill in agent-native sessions and provide trace-grounded feedback. SkillMe activates the matched skill on demand as workspace files, allowing agents to inspect and adopt guidance progressively without permanently installing it as a local skill. On the 71-task skill-needed subset of PinchBench with OpenClaw, SkillMe improves the overall mean score of all eight Executors over their paired no-skill baselines, with mean gains of +25.7 points. Comparisons show that evolved task-scoped skills are more stable than static skills and more reliable than broadly reusable skills. SkillMe thus provides an agent-native path to evolve reusable skills into effective workflow guidance through situated use.