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在过去的六个月里，我在构建AI开发工具包的过程中不断优化我的AI编码工作流程。最重要的变化不是更好的代码生成，而是从可重用的命令和模板转变为一个能够携带上下文、触发正确行为并自动验证工作的工作流程。最近的一个功能使这一点变得显而易见。我使用Codex为“ai-devkit skill add”构建了一个互动技能选择界面。我只提供了一句指令，工作流程便将任务推进到需求、设计、规划、实施、验证、测试和代码审查等各个阶段。整个过程不到一个小时，实际的功能流程大约花了30分钟。我觉得有趣的不仅仅是AI写了代码，而是这个工作流程留下了需求、设计文档、规划材料、基于需求的测试以及与规范的验证，而不仅仅是一个差异对比。在实践中，我觉得有几个重要的方面：- 内存回忆起我曾经存储的一个旧的命令行规则 - 审查阶段可以向后循环，而不是盲目向前推进 - 验证捕捉到了实施与设计之间的偏差 - 我仍然自己做出产品决策，并修复了最后一个失败的测试我很好奇在座的其他人对此是如何思考的。你们主要是在优化提示，还是现在开始尝试优化围绕模型的工作流程层？

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Over the last 6 months, I have been evolving my AI coding workflow while building AI DevKit.The shift that mattered most was not better code generation. It was moving from reusable commands and templates into a workflow that can carry context, trigger the right behavior, and verify work automatically.A recent feature made the difference obvious. I used Codex to build interactive skill selection for "ai-devkit skill add". I gave one sentence of instruction, and the workflow carried the task through requirements, design, planning, implementation, verification, tests, and code review.The whole session took under an hour. The actual feature flow was around 30 minutes.What I found interesting was not just that AI wrote code. It was that the workflow left behind requirements, design docs, planning artifacts, tests derived from requirements, and verification against the spec instead of just a diff.A few things that felt important in practice:- memory pulled back an old CLI rule I had forgotten I stored - review phases could loop backward instead of blindly moving forward - verification caught drift between implementation and design - I still made the product decisions and fixed the last failing test myselfI am curious how others here are thinking about this.Are you mostly optimizing prompts, or are you now trying to optimize the workflow layer around the model?

我的人工智能工作流程从提示演变为一种近乎自主的工作流程。