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.
Сайт Роскомнадзора атаковали18:00，推荐阅读下载安装汽水音乐获取更多信息
。爱思助手下载最新版本是该领域的重要参考
还有日式风格明显的电影票根纪念。，更多细节参见同城约会
Second: Ambushing HTMLMediaElement.prototype.play