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[2602.20867] SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
Summary
This paper explores agentic skills in LLM agents, focusing on reusable procedural capabilities that enhance long-horizon workflows. It presents taxonomies for skill design and representation, analyzes security implications, and discusses evaluation methods for agent success.
Why It Matters
As AI systems become more autonomous, understanding agentic skills is crucial for ensuring their reliability and security. This paper addresses significant challenges in the governance and evaluation of these skills, which are essential for the safe deployment of AI agents in various environments.
Key Takeaways
- Agentic skills enhance the execution of complex workflows in LLM agents.
- Two taxonomies are introduced for skill design and representation.
- Security risks associated with skill-based agents include supply-chain vulnerabilities.
- Curated skills can improve agent performance, while self-generated skills may hinder it.
- Open challenges remain in developing robust and verifiable skills for real-world applications.
Computer Science > Cryptography and Security arXiv:2602.20867 (cs) [Submitted on 24 Feb 2026] Title:SoK: Agentic Skills -- Beyond Tool Use in LLM Agents Authors:Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang, Qin Wang, Guangsheng Yu View a PDF of the paper titled SoK: Agentic Skills -- Beyond Tool Use in LLM Agents, by Yanna Jiang and 6 other authors View PDF HTML (experimental) Abstract:Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably. These capabilities are callable modules that package procedural knowledge with explicit applicability conditions, execution policies, termination criteria, and reusable interfaces. Unlike one-off plans or atomic tool calls, skills operate (and often do well) across tasks. This paper maps the skill layer across the full lifecycle (discovery, practice, distillation, storage, composition, evaluation, and update) and introduces two complementary taxonomies. The first is a system-level set of \textbf{seven design patterns} capturing how skills are packaged and executed in practice, from metadata-driven progressive disclosure and executable code skills to self-evolving libraries and marketplace distribution. The second is an orthogonal \textbf{representation $\times$ scope} taxonomy describing what skills \emph{are} (natural language, code, policy, hybrid) and what environments they operate over (web, OS, software engineering, robotics). We an...