---
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
