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

• URL: http://arxiv.org/abs/2606.01139v2
• Date: Tue, 02 Jun 2026 08:41:20 GMT
• Title: SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
• Abstract summary: We propose SkillRevise, an execution-grounded framework designed to iteratively refine agent skills.<n>SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits.<n>It substantially outperforms one-shot baselines, improving the base agent's success rate on SkillsBench from 36.05% to 61.63%.
• Score: 41.562357872885826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates and measuring empirical utility, it systematically retains the optimal skill version. Evaluated across three benchmarks and five LLMs, SkillRevise substantially outperforms one-shot baselines, improving the base agent's success rate on SkillsBench from 36.05% to 61.63%. Furthermore, the revised skills exhibit strong cross-model transferability, capturing generalized procedural knowledge over model-specific artifacts.

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