hugohe3/ppt-master：README 来源编辑指南

hugohe3/ppt-master 的 README 将项目描述为"AI turns documents or topics into real, native PowerPoint decks,with native shapes, transitions and animations, data-backed charts and tables on demand, audio narration from speaker notes, and support for your own .pptx templates."。本文只整理仓库能直接核验的内容，不把星标、Fork 或宣传语当成质量证明。README 在"PPT Master , AI generates native PowerPoint from any document"下的说明是：Thanks to Kimi is the world's first open 3T-class model, featuring native vision and a 1-million-token context window. With PPT Master, K3 can understand source materials such as PDFs, DOCX files, and web pages, identify key points,。这给出的首先是项目边界，而不是已经完成的生产验证。

从 README 的"Product Positioning"和相关条目看，读者可以先判断它是否解决自己的具体问题：Data stays local , apart from AI model communication, the entire pipeline runs on your machine。如果你的目标与这段说明不一致，就不应仅凭项目热度采用它。这里保留原项目名、命令和组件名，方便回到一手来源核对。 README 还列出了另一条可核对的信息：Transparent, predictable cost , free and open source; the only cost is your AI model usage, with no PPT subscription on top。这类原文条目可以帮助读者设计试运行步骤，但不能代替自己的环境测试。

README 把工作方式分散写在"PPT Master , AI generates native PowerPoint from any document"等段落中。可确认的线索包括：Drop in your source material, and what you get back isn't a static layout you can edit , it's a complete deck with real PowerPoint behavior: native slide transitions, opt-in entrance / emphasis / motion-path / exit animations (off by。这篇整理没有把未写出的架构、性能或安全边界补成结论；真正的运行链仍应结合仓库目录、配置文件和版本标签检查。

第一次安装应从 README 给出的入口开始。当前可复核的命令是： # macOS brew install python # Ubuntu / Debian sudo apt install python3 python3-pip 如果仓库没有提供命令，本文不会替它编造安装步骤，而是建议先打开 README 的"Product Positioning"部分，确认系统依赖、默认端口和首次初始化动作。

日常使用的细节取决于项目实际文档。README 的"Product Positioning"段落提到：Editable is now table stakes , the real question is how much of PowerPoint you actually get. PPT Master delivers PowerPoint's native object model itself, and in depth: native shapes and connectors with working adjustment handles,。对于配置文件、环境变量、权限和数据目录，当前稿只记录来源明确的部分；未写明的默认值必须在测试环境中验证，并保留可回滚的配置副本。 同一部分还提到：No platform lock-in , any agent-capable AI IDE can drive it; Claude, GPT, Gemini, Kimi, and other models all work。