Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimodal, heterogeneous, noisy, and implicitly assumes human executors, making it difficult to use directly as the skills required by agents. To bridge the gap between human-oriented guides and agent-executable skills, we formalize this problem as guide-to-skill learning: converting in-the-wild guides into executable skills and continuously improving them from trajectories observable to the agent. To evaluate the capability of existing agents on this task, we introduce MMG2Skill-Bench, the first benchmark designed for this problem. We further propose MMG2Skill, a closed-loop framework that compiles guides into editable skills, conditions a fixed vision-language model (VLM) agent on these skills during execution, and revises the skills from trajectory-level root-cause feedback without using benchmark scores. Across GUI control, open-ended gameplay, and strategic card play with six VLM backbones, MMG2Skill consistently outperforms vanilla baseline agents in every model-domain setting, achieving macro-average gains of +12.8 to +25.3 percentage points across backbones. Ablation studies show that directly prompting agents with raw guides can degrade performance, while both structured skill construction and trajectory-driven revision are necessary for the observed improvements. On success-inferable tasks, analyzer-based early stopping further prevents late-stage performance regressions and saves 25%-53% of attempts when the success signal is properly calibrated.
Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；The Chinese University of Hong Kong（香港中文大学）；University of Science and Technology of China（中国科学技术大学）；University of Chinese Academy of Sciences（中国科学院大学）；vivo AI Lab（vivo AI实验室）
Skill-Conditioned Gated Self-Distillation for LLM Reasoning
Tsinghua University（清华大学）；Fudan University（复旦大学）；City University of Hong Kong（香港城市大学）；Huazhong University of Science and Technology（华中科技大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
"Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild
Griffith University（格里菲斯大学）；Nanyang Technological University（南洋理工大学）；University of New South Wales（新南威尔士大学）；Zhejiang Key Laboratory of Digital Fashion and Data Governance, Zhejiang Sci-Tech University（浙江数字时尚与数据治理重点实验室，浙江科技大学）
SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills
The Ohio State University（俄亥俄州立大学）；The University of Chicago（芝加哥大学）；University College London（伦敦大学学院）；University of Michigan（密歇根大学）；The Chinese University of Hong Kong（香港中文大学）；Case Western Reserve University（凯斯西储大学）；Amazon（亚马逊）
SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology（东部技术研究所）；University of Science and Technology of China（中国科学技术大学）；Southeast University（东南大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究所）
COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Tianjin University（天津大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；University of Science and Technology of China（中国科学技术大学）；Renmin University of China（中国人民大学）；Hong Kong Polytechnic University（香港理工大学）
Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale
Tianjin University（天津大学）；Southern Cross University（南方十字大学）；School of Cybersecurity, Tianjin University（安全学院，天津大学）；Nanyang Technological University（南洋理工大学）；University of New South Wales（新南威尔士大学）；Griffith University（格里菲斯大学）
When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills
The University of Sydney（悉尼大学）；University of Queensland（昆士兰大学）；Southeast University（东南大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
