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:
第九十二条 公安机关办理治安案件，有权向有关单位和个人收集、调取证据。有关单位和个人应当如实提供证据。，更多细节参见safew官方版本下载
数据背后，是一位 36 岁连续创业者的孤注一掷，她抵押杭州房产，拿出全部积蓄，只为做一款真正热爱的游戏。，详情可参考91视频
The common pattern across all of these seems to be filesystem and network ACLs enforced by the OS, not a separate kernel or hardware boundary. A determined attacker who already has code execution on your machine could potentially bypass Seatbelt or Landlock restrictions through privilege escalation. But that is not the threat model. The threat is an AI agent that is mostly helpful but occasionally careless or confused, and you want guardrails that catch the common failure modes - reading credentials it should not see, making network calls it should not make, writing to paths outside the project.，更多细节参见一键获取谷歌浏览器下载
Unified lifecycle management