SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while o

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ethics.ai (31 May 2026), “SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision,” evidence record 3350, https://ethics.ai/record/3350 (originally published by arXiv).

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