[btown]

If it were in the context of parachuting into a codebase, I’d make these skills an important familiarization exercise: how are tests made, what are patterns I see frequently, what are the most important user flows. By forcing myself to distill that first, I’d be better at writing code that is in keeping with the codebase’s style and overarching/subtle goals. But this makes zero sense in a green-field task.

[ljm]

There's overlap in that with brownfield or legacy code you are strongly opinionated on the status quo, and on the greenfield you are strongly opinionated with fewer constraints.

You have to work with conviction though. It's when you offload everything to the LLM that things start to drift from expectations, because you kept the expectations in your head and away from the prompt.

[btown]

Do skills extracted from existing codebases cause better or worse code in that they bias the LLM towards existing bad practices? Or, can they assist in acknowledging these practices, and bias it towards actively ensuring they're fixed in new code? How dependent is this on the prompt used for the skill extraction? Are the skills an improvement over just asking to do this extraction at the start of the task?

Now this dynamic would be a good topic to research!