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:
Most accurate keyword data: Accurate keyword
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is debatable, although historical accounts generally do. They are certainly of a
Brewster runs SpeedPro on three operating principles — growth, profitability, and efficiency — focusing on adding customers and leveraging technology to stay efficient.