54 lines
3.1 KiB
Markdown
54 lines
3.1 KiB
Markdown
---
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title: "A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications"
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created: 2026-06-19
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updated: 2026-06-19
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type: paper-raw
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source: https://arxiv.org/abs/2605.07358
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arxiv_id: 2605.07358
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version: v3
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---
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# A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
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**Authors**: Yingli Zhou, Shu Wang, Yaodong Su, Wenchuan Du, Yixiang Fang, Xuemin Lin
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**Affiliation**: The Chinese University of Hong Kong, Shenzhen
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**Published**: 2026-05-08 (v3: 2026-05-26)
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**Venue**: arXiv:2605.07358 (cs.IR)
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**Resources**: https://github.com/JayLZhou/Awesome-Agent-Skills
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## Abstract
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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.
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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.
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## Key Contributions
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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.
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2. Organizes research around four lifecycle stages with representative methods in each.
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3. Summarizes agent skills platforms (SkillNet, ClawHub, SkillHub, SkillsMP, Skills.sh), application scenarios, and open challenges.
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## Formal Definition
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A skill is a tuple **S = (M, R, C)**:
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- **M**: root instruction document
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- **R**: auxiliary resources (references, templates, scripts)
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- **C**: applicability conditions (metadata, descriptions, embeddings)
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## Taxonomy at a Glance
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| Stage | Categories |
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|-------|-----------|
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| Representation | Text-Based, Code-Backed, Hybrid-Based |
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| Acquisition | Human-Derived, Experience-Derived, Task-Derived, Corpus-Derived |
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| Retrieval | Dense Embedding, Sparse/Keyword, Generative, Structure-Aware (Hierarchical + Dependency Graph) |
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| Selection | Context-Aware, Skill Composition, Cost/Utility-Aware, Feedback-Driven |
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| Evolution | Skill Revision, Skill Validation, Policy Coupling, Repository Evolution, Runtime Governance |
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## Open Challenges
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- **Acquisition**: Abstraction quality, weak trigger specification, resource drift, admission quality at scale
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- **Retrieval**: Scalable skill libraries, constraint-aware composition, multi-objective selection, execution-centric evaluation
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- **Evolution**: Coarse artifact-level evaluation, asymmetric revision (add > rewrite/retire), weakly specified repository governance, confounded gains
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- **Future**: Unified skill schema, resource-aware joint optimization, lifecycle-level robustness, causality-driven skill diagnosis
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