AI agents -- systems that execute multi-step reasoning workflows with persistent state, tool access, and specialist skills -- represent a qualitative shift from prior automation technologies in social science. Unlike chatbots that respond to isolated queries, AI agents can now read files, run code, query databases, search the web, and invoke domain-specific skills to execute entire research pipelines autonomously. This paper introduces the concept of vibe researching -- the AI-era parallel to vibe coding -- and uses scholar-skill, a 26-skill plugin for Claude Code covering the full research pipeline from idea to submission across 18 orchestrated phases with 53 quality gates, as an illustrative case. I develop a cognitive task framework that classifies research activities along two dimensions -- codifiability and tacit knowledge requirement -- to identify a delegation boundary that is cognitive, not sequential: it cuts through every stage of the research pipeline, not between stages. I argue that AI agents excel at speed, coverage, and methodological scaffolding but struggle with theoretical originality and tacit field knowledge. The paper concludes with an analysis of three implications for the profession -- augmentation with fragile conditions, stratification risk, and a pedagogical crisis -- and proposes five principles for responsible vibe researching.

翻译：AI智能体——能够执行多步推理工作流、具备持久状态、工具访问和专业技能的智能系统——代表着社会科学领域自动化技术的质变。与仅响应孤立查询的聊天机器人不同，AI智能体现已能够读取文件、运行代码、查询数据库、搜索网络，并调用领域特定技能来自主执行完整的研究流程。本文提出"氛围研究"概念——即AI时代的"氛围编程"在科研领域的对应实践——并以scholar-skill（Claude Code平台的26项技能插件，涵盖从构思到投稿的完整研究流程，包含18个协调阶段与53个质量关卡）作为典型案例进行阐释。通过建立认知任务分析框架，将研究活动按"可编码性"与"隐性知识需求"两个维度进行分类，从而界定出贯穿研究全流程（而非阶段间）的认知性任务委托边界。本文论证了AI智能体在速度、覆盖范围和方法论框架构建方面表现卓越，但在理论原创性与领域隐性知识掌握方面仍存在局限。最后，本文分析了该技术对社会科学研究领域的三重影响——附带脆弱条件的增强效应、学科分层风险及教学危机——并提出负责任氛围研究的五项原则。