Agent skills -- structured, reusable knowledge artifacts

The gap

Evidence profile

Sourced from the stated research gap and abstract and future-work section of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 4 representative gaps

• Breaking Barriers: Multi-Agent Prompt Fusion for Automated LLM Jailbreaks (2026) · Cognitive Computation · doi

  Current studies mostly focus on isolated attack strategies, lacking multi-agent coordination mechanisms. There is a need to explore attack methods capable of handling cross-agent behaviors and model-specific vulnerabilities. The existing methods have limited attack effectiveness and fail to fully exploit the potential security vulnerabilities of LLMs.

  generalstated research gap
  Keywords: current studies mostly focus isolated attack strategies lacking
• A Framework for Evaluating Agentic Skills at Scale (2026) · arXiv

  Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.

  generalabstractevidence 5/5
  Keywords: agent reusable skills structured knowledge artifacts augment capabilities rapidly adopted industry cross domain impact across
• Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi

  The paper identifies a gap in the current research on LLM-based agents, particularly in terms of their potential applications and challenges. It notes that current research is still far from achieving artificial general intelligence. The paper identifies a need for further research on the development of more sophisticated and effective LLM-based agents.

  generalstated research gapevidence 5/5
  Keywords: paper identifies gap current research llm-based agents particularly
• Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi

  The paper suggests that future research should focus on addressing the challenges faced by LLM-based agents, such as LLM's inherent limitations, dynamic expansion of MAS, and security and trust issues. It proposes that future research should explore the potential applications of LLM-based agents in various fields. The paper suggests that future research should aim to develop more sophisticated and effective LLM-based agents.

  generalfuture-work sectionevidence 5/5
  Keywords: paper suggests future research focus addressing challenges faced

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