This Claude Code Plugin Writes 94% Less Code (ponytail)

[00:00:00]You know him. Long ponytail, oval glasses, has been at the company longer than the version control. You show him 50 lines, he looks at them, says nothing, and replaces them with one.

[00:00:11]That is the epic description of this new library called Ponytail, which I guess is kind of relatable. We all know that one 10x developer who matches that description perfectly. But Ponytail is actually a really cool tool. It makes your AI coding agent think like the laziest senior dev in the room. And that's actually a compliment. So, in this video, we'll take a look at Ponytail, see how it works, and run some fun demos to find out if this guy is actually the real deal. It's going to be a lot of fun. So, let's dive into it.

[00:00:48]So, Ponytail's mission is simple. Keep everything super concise. eliminate the bloat AI agents usually produce and try to come up with the leanest solution to a problem it can possibly find. It's kind of similar to Caveman, which was the library that made AI coding agents talk less, therefore spending less tokens, which James also did a great video on over here. So, the main idea behind it is embracing the Yagy principle, which stands for you ain't going to need it. It's actually a software engineering idea from the '9s.

[00:01:21]And the core idea of it is don't build something until you actually need it.

[00:01:26]Don't add an abstraction layer. Don't install a library. Don't write the class. If the problem can be solved without it, then just solve it without it. And Ponytail bakes that directly into your agent by giving it a decision ladder it has to climb before writing anything. Does this need to exist at all? Can a standard library handle it?

[00:01:47]Is there a native platform feature for this? Is there already a dependency installed that does this? Can it be a oneliner? Only if every single one of those answers is a no, then it actually writes new code. And even then, it just keeps it to the minimum required to get it working. And if we look at some of their examples, especially the modal dialogue example, we get a clear picture of this methodology. A normal agent when asked to add a modal dialogue for the delete confirmation will immediately reach for installing a radex UI library like the React dialogue and give you a dependency and a portal, an overlay, a root, a trigger, a content wrapper just to show a box with two buttons. But Ponytail looks at this and says, "Hey, the browser already has a dialogue element. It traps focus automatically and it closes on escape, renders a backdrop with a single CSS selector and it's supported in every major browser since 20122.

[00:02:54]So instead of 30 lines in an MPM package, you get eight lines and zero dependencies. And this little ponytail comment right here tells you exactly what it skipped and why it did that. So if one day you actually decide to upgrade it to the radex version or something more fancier, you know where to go and where it was deferred. So it's lazy, but it's not irresponsible. And by embracing this laziness, Ponytail claims to be able to reduce your cost by 47 to 77%. And they actually give some benchmarks behind this claim. So let's look at them for a moment. We have three methods here. using no skill, using caveman and using ponytail and three models and five everyday tasks. 10 runs per cell and for each of them the median result. And crucially, they also check for correctness. A broken oneliner that scores great on lines of code will fail on correctness. So it's not just write less stuff, it has to actually work. And there's also an interesting caveat worth noting. cost reflects singleshot calls that resend the skill every time. In other words, the benchmark works by sending a fresh API call for each test and every time it does that, it includes the full ponytail rule set in the prompt. So in the benchmark, ponytail is being penalized for the cost of its own instructions on every single test. In real life, you pay for those instructions roughly once per session and after that they are cashed. That means the 47 to 77% cheaper figure is actually underelling it. In a real working session spread across many prompts, the cost advantage is even bigger because that skill injection cost gets amvertised across the whole conversation. That said, there is a legitimate critique worth mentioning. A recently published blog post by Colin Eberhart points out that if you actually swap out ponytail for three simple words, follow Yagy principles, the results of that almost perfectly matched Ponytail's benchmark score. And when elaborating to seven words, follow Yagy principles and oneliner solutions, it actually beat the benchmark. So is ponytail magic or is it just a well packaged prompt? Well, honestly, that is a fair question, but I would argue that packaging is the product. You get the right rules injected automatically across different agents with commands, audit tools, and a debt ledger on top.

[00:05:25]Besides, Ponytail has other cool features. Follow Yagy in your system prompt doesn't give you the ponytail audit feature or the ponytail review feature. But now, let's test it out with a simple example. So here I have two claude code instances open and on one of them I'm going to install the ponytail plugin for the local scope only and the other one will be a simple default claude code instance with no plugins activated. I will give them both the same prompt to build a weather dashboard app that detects user location and shows current weather conditions along with some other features. And I'm going to run the same prompt on both instances with the only exception that on the ponytail one I'm going to also ask it to use the ponytail skill because sometimes it doesn't automatically pick it up. So after a few moments we see that ponytail version has already finished the task in under 1 minute while the default one is still crunching. And also we see a very concise overview of what it built and what Ponytail opted out of doing for maximum efficiency. And as we can see here, it chose to have everything in one single HTML file. Meanwhile, on the default window, the task was finished in 2 minutes and 30 seconds. And we can already see that this version is much more bloated. We have three separate files. And this version is run using a Python server. So, while this isn't in no means a bad result, it's much more overengineered than the first version.

[00:06:55]But let's actually look how they operate. So, first off, this is the version without ponytail. And while the app looks great and the UI is beautiful and the API retrieves information as expected, I am quite disappointed that it didn't pick up my location automatically as I asked and instead it shows me London as the default first result. But now if we hop on to the ponytail version here, we can clearly see that upon opening it, it asks to get my current location and then outputs the weather matching that location instead.

[00:07:28]So while the UI is maybe not as fancy and the app is maybe more bare bones, it did follow the instructions more precisely than the default version, which is quite surprising to be honest.

[00:07:39]And lastly, let's look at the usage. And here we can see that yes indeed the version with ponytail ended up being 50% cheaper than the default version and it also produced far less lines of code.

[00:07:53]And as we just saw it was even better in terms of functionality than the default version. So this proves that ponytail does indeed work as expected and it does produce leaner code. So since this test was so successful, I decided to do something even more interesting. What if I combine Caveman and Ponytail together for maximum efficiency? What will that give us? So this time I activated both plugins in a new directory and ran the same prompt again. And once again the task was finished under a minute and the output was fairly similar and I had all the same functionality. So it worked as expected. But if we look at the output, it didn't differ too much from the ponytail version. And the caveman plus ponytail combo ended up being even slightly more expensive than the standalone ponytail version. So this shows that combining them doesn't really give you any big improvement. So you can stick to just using caveman or better yet opted out for using ponytail if we can believe their benchmarks that it is indeed better than caveman. So there you have it folks. That is ponytail in a nutshell. I am honestly genuinely impressed by the positive output Claude was able to produce with the ponytail skill while cutting the bloat and maintaining the quality at the same time. I guess this just goes to show that a lot of our coding solutions are probably overengineered and sometimes less is indeed more if you use it the right way. So, I'm definitely going to be keeping Ponytail as a plug-in in my Claude Code setup and probably use it for future projects. But what do you think about Ponytail? Have you tried it?

[00:09:34]Will you use it? Let us know in the comments section down below. And folks, if you like these types of technical breakdowns, please let me know by smashing that like button underneath the video. And also, don't forget to subscribe to our channel. This has been Andress from Better Stack, and I will see you in the next videos. Heat.

[00:10:01]Heat.