[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82172-en":3,"doc-seo-82172-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82172,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills","Software engineering (SE) has advanced through stronger reuse mechanisms, and AI agents introduce a new reusable artifact: agent skills. Public skill repositories and marketplaces package SE expertise as reusable skills, prompting the core question of which SE activities become encapsulated. Prior work emphasizes general skill acquisition and evaluation rather than a systematic SE-specific view. This study conducts the first large-scale empirical analysis of 11,497 SE skills, examining encapsulated activities, lifecycle coverage, evolution patterns, and evaluation mechanisms, enabling research directions for skill recommendation and engineering structuring.","Inside the Skill Market: From Software Engineering Activities to Reusable Agent Skills  \nJialun Cao∗ [jcaoap@cse.ust.hk](jcaoap@cse.ust.hk)[ ](jcaoap@cse.ust.hk)The Hong Kong University of Science and Technology Hong Kong, China  \nXinru Yan∗ [yanxinru24@mails.ucas.ac.cn](yanxinru24@mails.ucas.ac.cn)[ ](yanxinru24@mails.ucas.ac.cn)University of Chinese Academy of Sciences Beijing, China  \nSongqiang Chen  \n[i9s.chen@connect.ust.hk](i9s.chen@connect.ust.hk)[ ](i9s.chen@connect.ust.hk)The Hong Kong University of Science and Technology Hong Kong, China  \nYaojie Lu  \n[luyaojie@iscas.ac.cn](luyaojie@iscas.ac.cn)[ ](luyaojie@iscas.ac.cn)Institute of Software, Chinese Academy of Sciences Beijing, China  \nZhongxin Liu  \nliu [zx@zju.edu.cn](zx@zju.edu.cn)  \nZhejiang University Hangzhou, China  \nShing-Chi Cheung [scc@cse.ust.hk](scc@cse.ust.hk)  \nThe Hong Kong University of Science and Technology Hong Kong, China  \narXiv :2607 .09065v 1 [ cs . SE] 10 Jul 2026  \nAbstract—Software engineering (abbrev. SE) has continuously evolved through increasingly powerful forms of reuse, from source code and libraries to components and services. Recent advances in AI agents have introduced a potentially new reusable artifact: skills. Emerging agent skill repositories and marketplaces enable developers to package, share, and reuse SE expertise as reusable skills. This trend raises a fundamental question: what SE activities are being encapsulated into reusable skills? Existing studies primarily focus on a broad range of skills acquisition, safety, or benchmarking, while lacking a systematic understanding of SE-specific skills and their coverage across the software development lifecycle. To address this gap, we conduct the first large-scale empirical study of SE skills in public repositories and marketplaces. We collect and analyze a large corpus of SE skills, examining the activities they encapsulate, lifecycle coverage, evolution characteristics, and evaluation mechanisms. Our findings reveal that SE activities are increasingly becoming reusable artifacts via skills and suggest promising research opportunities for skill recommendation and engineering-oriented structuring, as well as the need for mechanisms to encapsulate high-context SE activities into reusable skills. Overall, our study provides the first activity-centric characterization of SE skills and reveals how SE activities are increasingly being transformed into reusable skills. These findings offer new insights into skill reuse, ecosystem development, and the future of agent-centric SE.  \nIndex Terms—agent skills, software engineering, software reuse  \nI. INTRODUCTION  \nSoftware engineering (abbrev. SE) has long advanced through increasingly powerful forms of reuse [1]–[4] . Over the past decades, developers have moved from reusing source code fragments to libraries, frameworks, services, and cloud-based infrastructures [3], [4] . Each shift has changed not only how software is constructed, but also what constitutes a reusable software artifact [1], [3] . Reuse has therefore remained a central mechanism for improving productivity, scalability, and knowledge transfer throughout the history of SE.  \n∗Both authors contributed equally to this research.  \nRecent advances in foundation models and AI agents are introducing a potentially new form of reuse [5] . Early work, such as Voyager [6], demonstrated that the agent skills (abbrev. skills), which describe specific functionality of agents, can be accumulated, retrieved, and reused across tasks through persistent skill libraries [7] . Subsequently, agent frameworks such as MetaGPT [8] and ChatDev [9] further showed that SE processes can be structured and decomposed through reusable workflows.  \nMore recently, an emerging ecosystem of public skill repositories [10], [11] and marketplaces [12]–[16] has enabled developers to publish, discover, and share skills for a wide range of SE tasks such as code writing [17] and testing [18] . 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