AI 中文总结
本研究通过对Claude Code插件市场的实证分析，揭示智能体插件的维护与协同演化规律，发现其功能驱动开发特征及新型组件耦合依赖关系。
AI 中文摘要
AI编码智能体是通过推理和工具使用实现开发任务自动化的软件工具，正越来越多地通过插件市场进行扩展，但这些新兴仓库的结构、维护及协同演化动态仍未得到实证探索。与通过源代码提供功能的传统软件包不同，智能体插件通过自然语言指令文件、脚本和配置文件的组合提供功能，这引发了一个问题：这些插件是跨组件协同演化的维护型制品，还是开发者编写一次后无需再修改的一次性制品？为研究智能体插件的维护与协同演化，我们对托管Claude Code插件市场的1926个仓库开展实证研究，分析了2018个市场中的8351个插件及77773次提交。研究发现，该市场正在快速扩张，自2025年10月推出后，涉及插件的提交活动在六个月内增长了8.8倍，针对软件工程任务的插件占所有插件的61.3%。插件开发主要以功能为驱动，功能提交的频率是传统开源软件（OSS）的两倍以上（39.6% vs. 17.2%）。Claude参与了34.9%的所有提交，且四种提交类型（文档、性能、样式和重构）在插件仓库中的含义与传统软件存在显著差异。大多数组件类型独立演化，但在技能目录内，自然语言指令文件与实现脚本以高于随机的速率协同演化，其中78%的共同变更存在功能耦合，代表了软件工程中未观察到的一类新型维护依赖关系。
英文摘要
AI coding agents, software tools that automate development tasks through reasoning and tool use, are increasingly extended through plugin marketplaces, yet the structure, maintenance, and co-evolution dynamics of these emerging repositories remain empirically unexplored. Unlike traditional software packages that deliver functionality through source code, agent plugins deliver functionality through a combination of natural-language instruction files, scripts, and configuration files, raising the question of whether these plugins are maintained artifacts that co-evolve across components, or one-off artifacts that developers write once and do not need to revisit. To study the maintenance and co-evolution of agent plugins, we conduct an empirical study of 1,926 repositories hosting Claude Code plugin marketplaces, analyzing 8,351 plugins and 77,773 commits across 2,018 marketplaces. We find that the marketplace is expanding rapidly, plugin-touching commit activity growing 8.8x over six months after the October 2025 launch, and plugins targeting Software Engineering tasks accounting for 61.3% of all plugins. Plugin development is predominantly feature-driven, with feature commits occurring at more than twice the rate of conventional open-source software (OSS) (39.6% vs. 17.2%). Claude co-authors 34.9% of all commits, and four commit types (docs, perf, style, and refactor) carry substantially different meanings in plugin repositories than in traditional software. Most component types evolve independently, but within skills directories, natural-language instruction files and implementation scripts co-evolve at above-chance rates, with 78% of co-changes being functionally coupled, representing a new class of maintenance dependency not observed in traditional software engineering.
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