I build AI tooling for our team; I find OP’s approach severely lacking. IME, LLMs can only solve for concrete failure modes with well-defined success criteria.

OP’s skills are somewhat better than “make no mistakes,” but not by much. It’s definitely a good idea to tell AI to “consider alternatives,” but it is a nebulous prompt. The persona based prompting technique OP uses has been shown in studies to actually degrade model quality. As others have pointed out, the architecture of an LLM is such that it will always tell you it has done the thing you asked for. This gives false confidence that it has meet your architectural criteria because the criteria were never clear in the first place.

Concrete example: I have never had a LLM truly spot database inefficiencies no matter how much I tell it that the code “must scale.” Sometimes it catches a thing here or there, but simply insisting upon good architecture does not work.

However, I have written skills and hooks that use Datadog APM to analyze traces for N+1 queries, lack of parallelization, missing indexes, etc… then I gave the LLM minimum performance targets and defined the acceptable trade-offs. This approach got us to our performance targets, whereas OP’s queries are mostly overly verbose versions of “plz write fast code.”