Comment by NichoPaolucci
4 days ago
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.
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".
The point is that you can properly describe what X, Y, and Z are.
The irony to me is that a lot of what I'd tell people about this is exactly what I would have told them about writing Stack Overflow questions.
I hardly need to properly describe it.
For example, if I tell it that I want my app translated, it can plan for me what the recommended options are in my framework, what languages I should target for my app, and come up with a skill for a repeatable workflow.
Is Superpowers any good? My coworkers who've used it seem to think that its main purpose is to consume a lot of tokens.
I like the fact that it ends up being a lifecycle. I know it can have different entry points based on what you ask, and which skill you trigger first, but it inherently is chaining together skills. There also seems to be logic built into it such that if the ask is small, but you've still triggered the skill with "brainstorm", it will make judgements like "want me to skip phase X and go straight to implementation?"
I've noticed among my coworkers that we all have different amounts of trust we're willing to give the agent. That seems to manifest into some people only asking questions about the existing code, but never writing anything new with it. Others are willing to do limited targeted changes with the agent, but are unwilling to do things like let it make commits, or connect MCP servers, or really do anything that isnt fully understood by the human before setting the agent loose. Then I find myself, who has dove headlong into it all. I have skills that use MCP servers to check for pull requests, and give me summaries to give me more context for code reviews. I update Jira tickets in batches of 50+. I develop complete features exclusively through prompting the agent to do everything. I know I still have the responsibility to understand it at the same level as if I wrote it my self, and defend it and debug it.
I can easily see that superpowers would be wasted on most of my coworkers, simply because the benefits compound with the complexity of my ask. My coworkers aren't willing to hand off enough control to receive the benefits of superpowers.
I am in the completely same boat as you and completely agree that you need to “give into it” more to get the most out of superpowers. I gain so much confidence out of the actual process and all of the adversarial reviews, re-reviews, refinement, etc.
Honestly, within the last few days I've gone back to a mostly vanilla installation of Claude with a handful of directives in AGENTS.md. Depending on what I'm working on, I don't feel I need the extra plugins or a context repository. (It DOES feel at times like it blows through tokens for no reason).
Been working on some things that require more targeted, smaller scale changes and the base models do perfectly fine when given good instructions.
Superpowers and Compound Engineering are the two that I hear about around our team, both seem "fine" if you're into the fully agentic engineering "big change" future technology stuff.
> Obvious exaggeration, but you get the point.
From some very quick tests, I get the impression that having good grammar, punctuation etc. is really not important (although I try to do it anyway because I'm accustomed to trying to do it), but clarity and precision definitely are. And of course, if you have unknown unknowns, they do need to get figured out before progress is possible. But with the right mindset, that just means you need an extra turn or two, not that you're going to end up at a dead end. (... I guess that counts as a pun?)
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.
I will often do "speak a long rambling set of ideas and ask the LLM to summarize it into a prompt or spec -> manually refine -> drop refined prompt into fresh conversation"