This creates both an opportunity and a maintenance requirement. The opportunity is that regularly updating content can improve AI citation rates even if the core information hasn't changed dramatically. The requirement is that high-performing content needs periodic refreshes to maintain its competitive position as newer articles on the same topics emerge.
[&:first-child]:overflow-hidden [&:first-child]:max-h-full"
。旺商聊官方下载对此有专业解读
（三）在铁路、城市轨道交通线路、桥梁、隧道、涵洞处挖掘坑穴、采石取沙的；
compareCount++;
I then added a few more personal preferences and suggested tools from my previous failures working with agents in Python: use uv and .venv instead of the base Python installation, use polars instead of pandas for data manipulation, only store secrets/API keys/passwords in .env while ensuring .env is in .gitignore, etc. Most of these constraints don’t tell the agent what to do, but how to do it. In general, adding a rule to my AGENTS.md whenever I encounter a fundamental behavior I don’t like has been very effective. For example, agents love using unnecessary emoji which I hate, so I added a rule: