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+---
+title: "A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications"
+created: 2026-06-19
+updated: 2026-06-19
+type: paper-raw
+source: https://arxiv.org/abs/2605.07358
+arxiv_id: 2605.07358
+version: v3
+---
+
+# A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
+
+**Authors**: Yingli Zhou, Shu Wang, Yaodong Su, Wenchuan Du, Yixiang Fang, Xuemin Lin
+**Affiliation**: The Chinese University of Hong Kong, Shenzhen
+**Published**: 2026-05-08 (v3: 2026-05-26)
+**Venue**: arXiv:2605.07358 (cs.IR)
+**Resources**: https://github.com/JayLZhou/Awesome-Agent-Skills
+
+## Abstract
+
+LLM-based agents that reason, plan, and act through tools, memory, and structured interaction are emerging as a promising paradigm for automating complex workflows. This survey examines the challenge through the lens of **agent skills**, defined as reusable procedural artifacts that coordinate tools, memory, and runtime context under task-specific constraints. Agents handle high-level reasoning and planning, while skills form the operational layer that enables reliable, reusable, and composable execution.
+
+The literature is organized around four stages of the agent skill lifecycle: **representation**, **acquisition**, **retrieval**, and **evolution**. The paper also discusses open challenges in quality control, interoperability, safe updating, and long-term capability management.
+
+## Key Contributions
+
+1. Identifies agent skills as a foundational component of LLM agent ecosystems, characterizing their role in bridging the **procedural gap** between raw tool access and robust task execution.
+2. Organizes research around four lifecycle stages with representative methods in each.
+3. Summarizes agent skills platforms (SkillNet, ClawHub, SkillHub, SkillsMP, Skills.sh), application scenarios, and open challenges.
+
+## Formal Definition
+
+A skill is a tuple **S = (M, R, C)**:
+- **M**: root instruction document
+- **R**: auxiliary resources (references, templates, scripts)
+- **C**: applicability conditions (metadata, descriptions, embeddings)
+
+## Taxonomy at a Glance
+
+| Stage | Categories |
+|-------|-----------|
+| Representation | Text-Based, Code-Backed, Hybrid-Based |
+| Acquisition | Human-Derived, Experience-Derived, Task-Derived, Corpus-Derived |
+| Retrieval | Dense Embedding, Sparse/Keyword, Generative, Structure-Aware (Hierarchical + Dependency Graph) |
+| Selection | Context-Aware, Skill Composition, Cost/Utility-Aware, Feedback-Driven |
+| Evolution | Skill Revision, Skill Validation, Policy Coupling, Repository Evolution, Runtime Governance |
+
+## Open Challenges
+
+- **Acquisition**: Abstraction quality, weak trigger specification, resource drift, admission quality at scale
+- **Retrieval**: Scalable skill libraries, constraint-aware composition, multi-objective selection, execution-centric evaluation
+- **Evolution**: Coarse artifact-level evaluation, asymmetric revision (add > rewrite/retire), weakly specified repository governance, confounded gains
+- **Future**: Unified skill schema, resource-aware joint optimization, lifecycle-level robustness, causality-driven skill diagnosis