Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
第五十四条 强买强卖商品，强迫他人提供服务或者强迫他人接受服务的，处五日以上十日以下拘留，并处三千元以上五千元以下罚款；情节较轻的，处五日以下拘留或者一千元以下罚款。
。夫子对此有专业解读
Цены на нефть взлетели до максимума за полгода17:55，详情可参考一键获取谷歌浏览器下载
3. I started a new session, and asked it to check the specification markdown file, and to check all the documentation available, and start implementing the Z80 emulator. The rules were to never access the Internet for any reason (I supervised the agent while it was implementing the code, to make sure this didn’t happen), to never search the disk for similar source code, as this was a “clean room” implementation.