SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories
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| Format: | Preprint |
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2026
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| _version_ | 1866916071961788416 |
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| author | Yu, Zhuoyun Xie, Xin Yao, Wuguannan Wang, Chenxi Liang, Lei Qi, Xiang Deng, Shumin |
| author_facet | Yu, Zhuoyun Xie, Xin Yao, Wuguannan Wang, Chenxi Liang, Lei Qi, Xiang Deng, Shumin |
| contents | 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 maintenance\footnote{The code will be released at https://github.com/zjunlp/SkillAdaptor.}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01311 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories Yu, Zhuoyun Xie, Xin Yao, Wuguannan Wang, Chenxi Liang, Lei Qi, Xiang Deng, Shumin Computation and Language Artificial Intelligence Machine Learning Multiagent Systems 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 maintenance\footnote{The code will be released at https://github.com/zjunlp/SkillAdaptor.}. |
| title | SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories |
| topic | Computation and Language Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2606.01311 |