I know agents have lowered the cost to make a website to be a few cents for tokens and a couple dollars for a domain name, but isn’t all this a little excessive for a few line long markdown file!? Why not just display the ‘skill’ itself with a few lines commenting on what it does/how it helps?

I thought the webpage was a good read.

It’s provocative, it gets the people going

I can’t help but think the effective outcome of using this is gonna be a ton of language quirk usage, mass functional chaining, and single letter variables.

Optimizing for fewest LOC is probably slightly more bad than more LOC, and both are bad for the same reason - it makes it harder for humans to interpret and understand wtf terrible decisions and tradeoffs the LLM made

> Optimizing for fewest LOC is probably slightly more bad than more LOC, and both are bad for the same reason - it makes it harder for humans to interpret and understand ...

Concision begets perplexity.

;-)

It does seem like for new code that might help. There's some really good logic and wisdom in it, but it has to be applied very contextually to the exact problem you are trying to solve. If an agent is navigating a complex codebase, this could definitely send them off on a refactoring rabbit hole. However, if you have them writing some new code, it could prevent their tendency to yak shave and write new things. So I can see some situational uses for this, but it could get out of hand as well.

Why build a whole website for what is essentially a markdown file?

I've been using this for a couple of months and it is hit or miss. It'll make actual high quality suggestions at times. But the thing it's missing is the *actual* experience that makes its namesake persona. There's no sense of nuance of context applied.

Garbage.

has anyone found a good way to improve code quality? just wondering -- LOC does seem like the wrong metric, but the code LLMs write is just too verbose

I'm working on a LLM code review tool that focuses on reducing concepts instead of reducing lines. I think that's more along the lines of explaining what you want out of clean code than ponytail's approach. But I'm still playing with it so, not sure. And just because it produces code I like doesn't mean it's good code for everyone

So far, the best way I’ve found to improve code quality from LLMs that you’ve already produced is to read the output, throw it away, and hand-write it from scratch, usually with a totally different structure.

This scales about as well as it sounds like it would.

Multi-model review does a decent job identifying things they’re outright wrong. The resulting code still doesn’t feel elegant writ large.

If you want good output, it seems that iterating on the output is inferior to providing better input inclusive of code examples. And by the time you’ve made all the decisions that go into that, something like ponytail is superfluous.

(All that said, I have ponytail installed in most harnesses.)

So we are building a text version of a bias aid for a random number generator? None of the output of what you are expecting from this "skills.md" is even guaranteed.

Not only the GitHub stars are clearly manipulated with bots and fake accounts, this whole "skills.md" paradigm is close to being a pseudoscientific exercise in attempting to steer LLMs but throwing huge markdown files at it and expecting the desired result to happen won't work in the long run.

That's my mistake, and here's the honest take:

A genuinely simple codebase is table stakes for a project like this.

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That's enough Claude speak for one day

Ha! Well done, sir.