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 （The code will be released at <a href=\"https://github.com/zjunlp/SkillAdaptor\" rel=\"nofollow\">https://github.com/zjunlp/SkillAdaptor</a>).</p>\n","updatedAt":"2026-06-02T02:56:52.018Z","author":{"_id":"6441f1d2603214724ec0c1c2","avatarUrl":"/avatars/d3c4b759e6a5635e37ff715fae52e5ba.svg","fullname":"Shumin Deng","name":"231sm","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":2,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8235855102539062},"editors":["231sm"],"editorAvatarUrls":["/avatars/d3c4b759e6a5635e37ff715fae52e5ba.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.01311","authors":[{"_id":"6a1e45e0808ddbc3c7d43c5d","name":"Zhuoyun Yu","hidden":false},{"_id":"6a1e45e0808ddbc3c7d43c5e","name":"Xin Xie","hidden":false},{"_id":"6a1e45e0808ddbc3c7d43c5f","name":"Wuguannan Yao","hidden":false},{"_id":"6a1e45e0808ddbc3c7d43c60","name":"Chenxi Wang","hidden":false},{"_id":"6a1e45e0808ddbc3c7d43c61","name":"Lei Liang","hidden":false},{"_id":"6a1e45e0808ddbc3c7d43c62","name":"Xiang Qi","hidden":false},{"_id":"6a1e45e0808ddbc3c7d43c63","name":"Shumin Deng","hidden":false}],"publishedAt":"2026-05-31T00:00:00.000Z","submittedOnDailyAt":"2026-06-02T00:00:00.000Z","title":"SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories","submittedOnDailyBy":{"_id":"6441f1d2603214724ec0c1c2","avatarUrl":"/avatars/d3c4b759e6a5635e37ff715fae52e5ba.svg","isPro":false,"fullname":"Shumin Deng","user":"231sm","type":"user","name":"231sm"},"summary":"Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. 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SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories
Abstract
Step-level skill adaptation framework with explicit failure attribution improves training-free skill maintenance for LLM agents in interactive tasks.
AI-generated summary
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..
Community
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 （The code will be released at https://github.com/zjunlp/SkillAdaptor).
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