Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirrors how human experts author skills: by holistically analyzing broad execution experience before distilling it into a single, comprehensive guide. Instead of reacting sequentially to individual trajectories, Trace2Skill dispatches a parallel fleet of sub-agents to analyze a diverse pool of executions. It extracts trajectory-specific lessons and hierarchically consolidates them into a unified, conflict-free skill directory via inductive reasoning. Trace2Skill supports both deepening existing human-written skills and creating new ones from scratch. Experiments in challenging domains, such as spreadsheet, VisionQA and math reasoning, show that Trace2Skill significantly improves upon strong baselines, including Anthropic's official xlsx skills. Crucially, this trajectory-grounded evolution does not merely memorize task instances or model-specific quirks: evolved skills transfer across LLM scales and generalize to OOD settings. For example, skills evolved by Qwen3.5-35B on its own trajectories improved a Qwen3.5-122B agent by up to 57.65 absolute percentage points on WikiTableQuestions. Ultimately, our results demonstrate that complex agent experience can be packaged into highly transferable, declarative skills -- requiring no parameter updates, no external retrieval modules, and utilizing open-source models as small as 35B parameters.
69c90a754c8e8981b7fa7a88
Jingwei Ni
admin_assigned
2026-03-30T10:21:18.017Z
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/avatars/4047cf8549f162994f44101b4e7c27fa.svg
Jingwei Ni
JingweiNi
user
JingweiNi
69c90a754c8e8981b7fa7a89
Yihao Liu
69c90a754c8e8981b7fa7a8a
Xinpeng Liu
admin_assigned
2026-03-30T10:21:29.031Z
64fffeb3cc88b84c06af5387
/avatars/a671bc9e8cd1138c4bb7128f1f83827c.svg
Xinpeng Liu (SII)
XinpengLiu
user
XinpengLiu
69c90a754c8e8981b7fa7a8b
Yutao Sun
admin_assigned
2026-03-30T10:21:35.994Z
6300ef4779c5ddbc6cf83e1a
https://cdn-avatars.huggingface.co/v1/production/uploads/1661005591657-noauth.jpeg
Yutao Sun
sunyt32
user
sunyt32
69c90a754c8e8981b7fa7a8c
Mengyu Zhou
69c90a754c8e8981b7fa7a8d
Pengyu Cheng
69c90a754c8e8981b7fa7a8e
Dexin Wang
69c90a754c8e8981b7fa7a8f
Xiaoxi Jiang
69c90a754c8e8981b7fa7a90
Guanjun Jiang
Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirrors how human experts author skills: by holistically analyzing broad execution experience before distilling it into a single, comprehensive guide. Instead of reacting sequentially to individual trajectories, Trace2Skill dispatches a parallel fleet of sub-agents to analyze a diverse pool of executions. It extracts trajectory-specific lessons and hierarchically consolidates them into a unified, conflict-free skill directory via inductive reasoning. Trace2Skill supports both deepening existing human-written skills and creating new ones from scratch. Experiments in challenging domains, such as spreadsheet, VisionQA and math reasoning, show that Trace2Skill significantly improves upon strong baselines, including Anthropic's official xlsx skills. Crucially, this trajectory-grounded evolution does not merely memorize task instances or model-specific quirks: evolved skills transfer across LLM scales and generalize to OOD settings. For example, skills evolved by Qwen3.5-35B on its own trajectories improved a Qwen3.5-122B agent by up to 57.65 absolute percentage points on WikiTableQuestions. Ultimately, our results demonstrate that complex agent experience can be packaged into highly transferable, declarative skills -- requiring no parameter updates, no external retrieval modules, and utilizing open-source models as small as 35B parameters.
Trace2Skill enables scalable skill generation for LLM agents by analyzing diverse execution traces in parallel and consolidating them into transferable, declarative skills without parameter updates or external modules.
Large Language Model agents skill generation trajectory analysis parallel sub-agents inductive reasoning declarative skills transferability parameter-efficient fine-tuning