I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too.

Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "your output should include: " style prompting).

A few of us run almost the same exact setup at my shop (Base Claude Code w/ SuperPowers + a context repository) and the models are somewhat unhelpful to some, and give meaningful output to others. The only correlation I notice is that their prompts are no-good. Not from a meta "prompt" engineering standpoint, but from a general English 101 standpoint.

"dudde no i wanted the function to return 3 things. not like that. do it again"

VS something like

"Modify the "renderThreeVars()" function signature to accept another variable called "z" and add it to the return statement at line 64."

Obvious exaggeration, but you get the point.

Is Superpowers any good? My coworkers who've used it seem to think that its main purpose is to consume a lot of tokens.

Why not open-ended assistance requests?

I ask it all the time about whether X is feasible, how we can get started on Y, and to investigate issue Z.

It is working great for me in a >100k LOC project.

Perhaps this works less well with weaker models. I suspect the people who say Qwen 3.6 27B is working well, are using prompts like "modify the renderThreeVars() function in rendering.py".

Well said. I’ve been trying to put my finger on this for a while. The interaction plane for an LLM is __all of human language__

Speaking a prompt with a long ramble is very powerful.