3. 5#楼天井操作平台未按要求编制审批危大方案，且连墙件间距偏大、无剪刀撑；5#楼爬架高度14米，局部两道支座， 顶部悬高超6米无临时拉结措施；且爬架与结构外墙间隙大于15cm，四周全高范围上下贯通无中部翻板。（违反《房屋市政工程生产安全重大事故隐患判定标准（2024版）》第四条第四款、第九条第四款，属于重大事故隐患。）
广东省中医院党委书记史俏蓉介绍，“师带徒”是中医药人才培养的重要方式。自2001年起，广东省中医院率先开启师承全国名老中医的新举措，创造了“跨地区拜师”“集体带、带集体”“一代带二代”“脱产跟师”等模式。至今共有205名“师带徒”指导老师，其中国医大师47名、全国名中医25名。
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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:
This doesn't mean stuffing your content with random numbers. It means grounding your claims in specific, verifiable data wherever possible. Instead of writing "Our tool is widely used," you'd write "Our tool has 150,000 monthly active users with a 4.7 out of 5 satisfaction rating based on 3,200 reviews." The specificity signals credibility to AI models, which learned during training that precise data indicates reliable sources.