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.
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，详情可参考Safew下载
ВсеОлимпиадаСтавкиФутболБокс и ММАЗимние видыЛетние видыХоккейАвтоспортЗОЖ и фитнес，详情可参考搜狗输入法2026
Here's how each policy behaves when a producer writes faster than the consumer reads:。关于这个话题，快连下载-Letsvpn下载提供了深入分析