Generative AI is changing how research software is developed, but rapid AI-assisted development can weaken continuity, traceability, and methodological clarity. SHAPR (Solo, Human-centred, AI-assisted PRactice) was proposed as a framework for structuring AI-assisted research software development. This paper presents a documented case of applying SHAPR to the development of a modular share trading system. From the outset, the project adopted a SHAPR-informed working configuration that shaped how interaction, implementation, and documentation were organised. Across iterative development cycles, the project generated a structured evidence base including reflection notes, development cycle review notes, source-of-truth documents, contracts, quick captures, workflow notes, and evolving code artefacts. The case showed that continuous documentation updates, supported by quick capture and AI-assisted refinement, helped maintain organised and usable project knowledge throughout development. Five recurring lessons were identified: contracts stabilised AI-assisted coding, a maintained source-of-truth layer improved coherence, cycle-boundary snapshots strengthened continuity, code and documentation co-evolved through quick capture and iterative refinement, and environment setup itself contributed to knowledge generation. The case also illustrates a practical SHAPR operating configuration in which a ChatGPT Project and cycle-specific chats supported interaction, reasoning, summarisation, and coding collaboration, PyCharm supported artefact implementation, and Obsidian supported external working memory, structured documentation, reflection, continuity, and repository-oriented note organisation, while remaining consistent with SHAPR's tool-agnostic principle. The paper contributes practical guidance and good practices for researchers conducting AI-assisted research software development.

翻译：生成式人工智能正在改变研究软件的开发方式，但快速的人工智能辅助开发可能削弱连续性、可追溯性和方法论清晰性。SHAPR（以人为中心的单人人工智能辅助实践）被提出作为结构化人工智能辅助研究软件开发的框架。本文呈现了将SHAPR应用于模块化股票交易系统开发的实证案例。项目从初始阶段就采用了基于SHAPR的工作配置，从而塑造了交互、实施和文档的组织方式。在迭代开发周期中，项目生成了包含反思笔记、开发周期评审笔记、事实源文档、契约、快速捕获、工作流笔记及演进代码工件的结构化证据库。案例表明，在快速捕获和人工智能辅助优化支持下持续更新文档，有助于在整个开发过程中维护有序且可用的项目知识。研究识别出五项重复出现的经验：契约稳定了人工智能辅助编码、维护事实源层提升了连贯性、周期边界快照增强了连续性、通过快速捕获和迭代优化实现代码与文档协同演进、环境配置本身也促进了知识生成。该案例还展示了实用的SHAPR操作配置：其中ChatGPT项目与周期特定对话支持交互、推理、总结和编码协作，PyCharm支持工件实现，Obsidian在保持与SHAPR工具无关性原则一致的同时，提供外部工作记忆、结构化文档、反思、连续性及面向仓库的笔记组织功能。本文为从事人工智能辅助研究软件的研究人员提供了实践指导与良好实践建议。