The problem is that agent skills are usually hand-written, made once by an LLM, or revised in loose ways that can easily make them worse.
SkillOpt from Microsoft, argues that agent skills should be trained like small external programs, it teaches AI agents better task habits by editing a reusable skill document, not the model itself.
The paper’s core idea is to treat the skill document like the thing being trained, while the main AI model stays frozen and unchanged.
SkillOpt watches the agent try tasks, studies what worked and failed, then asks a stronger optimizer model to suggest small edits to the skill.
It only accepts an edit when the new skill improves on a held-out check set, so the skill does not drift just because an edit sounds good.
The authors tested this across 6 benchmarks, 7 target models, and 3 agent settings, including direct chat, Codex, and Claude Code.
SkillOpt was best or tied on all 52 tested cases, and on GPT-5.5 it raised average accuracy by 23.5 points in direct chat.
The final result is a small readable skill file that can improve agents across tasks and settings without retraining the model.
The best part is that the optimizer is used during training, but deployment only needs the final skill file.
That makes the artifact inspectable, portable, and cheap to reuse, which is exactly what most prompt-engineering systems lack.
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Link – arxiv. org/abs/2605.23904
Title: "SkillOpt: Executive Strategy for Self-Evolving Agent Skills"
