# SkillAdaptor：一种面向LLM智能体的自适应技能方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-31 08:00
- AIHOT 分数：59
- AIHOT 链接：https://aihot.virxact.com/items/cmpw3ayof03zfsluknxpyfyrg
- 原文链接：https://arxiv.org/abs/2606.01311

## AI 摘要

SkillAdaptor 是一种无需训练的步骤级技能自适应框架，能进行显式故障归因。它针对智能体失败的执行轨迹，识别首个可操作的故障步骤，并将责任归因于候选技能，随后在骨干模型冻结的前提下进行针对性更新与验证。在 WebShop、PinchBench 和 Claw-Eval 上使用 Kimi-K2.5、GLM-5 和 GPT-5.2 的评估表明，该框架在所有三项基准测试中均优于无技能和现有技能适应基线。

## 正文

Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. Existing training-free skill adaptation pipelines usually update skills from full trajectories or session-level feedback, which makes failure attribution coarse and often produces unstable or overly broad revisions. We propose SkillAdaptor, a training-free step-level skill adaptation framework with explicit failure attribution, and it can plug into OpenClaw-class agent harnesses. Given a failed trajectory, SkillAdaptor identifies a first actionable fault step, links responsibility to candidate skills, and applies targeted updates under explicit acceptance checks while keeping the backbone frozen. We evaluate on WebShop, PinchBench, and Claw-Eval with Kimi-K2.5, GLM-5, and GPT-5.2. SkillAdaptor improves over no-skill and skill-adaptation baselines on all three suites, with the largest single-metric improvements of +1.5 points on PinchBench Avg Score%, +1.8 on Claw-Eval Avg Score, and +1.7 on WebShop success rate. These results indicate that step-level attribution supports more stable and auditable training-free skill maintenanceThe code will be released at https://github.com/zjunlp/SkillAdaptor..