mlsanityData to 7 Oct 2026

Agent Skill Framework: Perspectives on the Potential of Small to Medium Language Models in Industrial Environments

arXiv 2602.16653 ↗First posted 18 Feb 2026cs.AIcited by 3

On the map: LLM agents and tool use → Agent skill learning and evolution

Abstract (arXiv)

Agent skills are widely supported by major agentic frameworks and perform well with proprietary models, yet their effectiveness for small and medium-sized open source language models (270 M-80B) remains underexplored. We systematically study the Skill paradigm in resource-constrained industrial settings, where reliance on proprietary APIs is impractical due to data security and budget constraints. Across two open-source tasks and a real-world insurance claims classification task, we find that very small models struggle with reliable skill selection, while models around 30B-80B benefit substantially. Thinking variants do not show major levels of improvement from skills, also considering GPU usage increases due to overthinking. These findings reveal a trade-off between GPU cost and agent performance, and provide actionable insights for effective Skill configuration and SLM deployment in real world settings.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use. Builds on 20 of its 32 references that are in the map. First posted on arXiv 18 Feb 2026.

Builds on

20 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks13 Feb

Other references · 19

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  4. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,000
  5. 2022A Survey on In-context Learningcited by 717
  6. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  7. 2024CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challengescited by 278
  8. 2025Small Language Models are the Future of Agentic AIcited by 254
  9. 2024A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthinesscited by 125
  10. 2025Context Length Alone Hurts LLM Performance Despite Perfect Retrievalcited by 118
  11. 2026Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scalecited by 94
  12. 2025MemInsight: Autonomous Memory Augmentation for LLM Agentscited by 84
7 more
  1. 2026Meta Context Engineering via Agentic Skill Evolutioncited by 42
  2. 2025Exploring the Impact of Temperature on Large Language Models:Hot or Cold?cited by 40
  3. 2026When Single-Agent with Skills Replace Multi-Agent Systems and When They Failcited by 34
  4. 2026CUA-Skill: Develop Skills for Computer Using Agentcited by 32
  5. 2023Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?cited by 13
  6. 2024DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilitiescited by 3
  7. 2025Small Language Models in the Real World: Insights from Industrial Text Classificationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Modelscited by 2
  2. 2026SkillEval: Decomposing Agent Skill Quality into Interpretable Signalscited by 1
  3. 2026Compression, structure, and executor capability: a controlled real-cost decomposition of language-model agent skill optimisationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality
  2. 2026Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
  3. 2025COALESCE: Economic and Security Dynamics of Skill-Based Task Outsourcing Among Team of Autonomous LLM Agents
  4. 2026A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
  5. 2026A Framework for Evaluating Agentic Skills at Scale
  6. 2026OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents
  7. 2026SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents
  8. 2026How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings