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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
contains the list of papers of agent skills
| Date | Stars |
|---|---|
| 2026-07-31 | 256 |
| 2026-08-06 | 256 |
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# Awesome-Agent-Skills-Papers A curated list for agent skills papers, following the organization style of [Awesome-Efficient-LLM](https://github.com/horseee/Awesome-Efficient-LLM). ## Full List - Skill Learning / Self-Improvement - Skill-Oriented Reasoning / World Modeling - Skill Routing / Orchestration / Ecosystems - Skill Benchmarks / Evaluation - Security / Robustness - Survey / Taxonomy / Theory ### Notes - This list merges the current README structure you provided with the additional skill-related papers we identified as missing candidates. - The duplicated entries `2602.06130` and `2601.04748` are each included only once. - For conceptual or survey papers without a single headline metric in the abstract, the “Introduction” field summarizes the main takeaway instead of forcing a number. - Because several added papers predate October 2025, the time range below is expanded accordingly. #### Contributing If you'd like to add more skill papers, benchmarks, or repositories, feel free to extend the same markdown format: `Title & Authors | Introduction | Links` --- ## Paper List (2024-10 - 2026-03) ### Quick Link - [Skill Learning / Self-Improvement](#skill-learning--self-improvement) - [Skill-Oriented Reasoning / World Modeling](#skill-oriented-reasoning--world-modeling) - [Skill Routing / Orchestration / Ecosystems](#skill-routing--orchestration--ecosystems) - [Skill Benchmarks / Evaluation](#skill-benchmarks--evaluation) - [Security / Robustness](#security--robustness) - [Survey / Taxonomy / Theory](#survey--taxonomy--theory) --- ## Skill Learning / Self-Improvement | Title & Authors | Introduction | Links | |:--|:--|:--:| | **XSkill: Continual Learning from Experience and Skills in Multimodal Agents**<br>Guanyu Jiang, Zhaochen Su, Xiaoye Qu, Yi R. Fung | XSkill is a dual-stream continual-learning framework that distills visually grounded **experiences** and **skills** from multimodal rollouts and retrieves/adapts them at inference time, and it consistently outperforms both tool-only and learning-based baselines on five benchmarks with four backbone models. | [Paper](https://arxiv.org/abs/2603.12056) | | **Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction**<br>Shuzhen Bi, Mengsong Wu, Hao Hao, Keqian Li, Wentao Liu, Siyu Song, Hongbo Zhao, Aimin Zhou | This work studies how to automatically mine open-source agentic repositories for reusable procedural knowledge, turning code-and-workflow traces into standardized skill artifacts and offering a promising acquisition pipeline for large-scale skill-library construction. | [Paper](https://arxiv.org/abs/2603.11808) | | **AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution**<br>Yutao Yang, Junsong Li, Qianjun Pan, Bihao Zhan, Yuxuan Cai, Lin Du, Jie Zhou, Kai Chen, Qin Chen, Xin Li, Bo Zhang, Liang He | AutoSkill is a model-agnostic lifelong-learning plugin that automatically derives, evolves, and reuses skills from interaction traces, with the paper emphasizing transferable standardized skill representations across agents, users, and tasks rather than a single headline benchmark number. | [Paper](https://arxiv.org/abs/2603.01145) | | **SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning**<br>Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, Zeyu Zheng, Cihang Xie, Huaxiu Yao | SkillRL bridges raw experience and policy improvement by building a hierarchical SkillBank and recursively co-evolving it with the agent during RL, achieving state-of-the-art results on ALFWorld, WebShop, and seven search-augmented tasks while outperforming strong baselines by **15.3%+**. | [Paper](https://arxiv.org/abs/2602.08234) | | **MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents**<br>Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang,
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f7d42949113809de, llm:description: 'contains the list of papers of agent skills' (awesome list of papers about agent skills)
matched fp:f7d42949113809de, llm:description: 'contains the list of papers of agent skills' (awesome list of papers about agent skills)
matched fp:f7d42949113809de, llm:description: 'contains the list of papers of agent skills' (awesome list of papers about agent skills)