SkillsBench: Benchmarking how well agent skills work across diverse tasks
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Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#2The general rule seems to be, the more layers you automate with LLMs, the worse each successive layer gets. Piping LLM output as input into new LLM calls, you're already starting to notice how things fall apart and get lost quickly.
If you have the idea, more or less the implementation plan, let the LLM do the coding, you can end up with something maintainable and nice, it's basically up to you.
Strip away one layer, so you have the idea, but let the LLM come up with the implementation plan, then also the implementation, and things end up a lot less than ideal.
Remove another layer, let the LLM do it all, and it's all a mess.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#3The general rule seems to be, the more layers you automate with LLMs, the worse each successive layer gets. Piping LLM output as input into new LLM calls, you're already starting to notice how things fall apart and get lost quickly. If you have the idea, more or less the implementation plan, let the LLM do the coding, you can end up with something maintainable and nice, it's basically up to you. Strip away one layer,…
I think this principle applies only if you lack feedbacks. Yes, when you go through multiple layers of open loop control, you're going to get less precise at each layer. It's less clear that the situation is as dire if each level has metrics and can self-adjust to optimize its performance.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#4It seems intuitive that a naive self-generated Skill would be low-value, since the model already knows whatever it's telling itself.
However, I've found them to be useful for capturing instructions on how to use other tools (e.g. hints on how to use command-line tools or APIs). I treat them like mini CLAUDE.mds that are specific only to certain workflows.
When Claude isn't able to use a Skill well, I ask it to reflect on why, and update the Skill to clarify, adding or removing detail as necessary.
With these Skills in place, the agent is able to do things it would really struggle with otherwise, having to consume a lot of tokens failing to use the tools and looking up documentation, etc.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#5The general rule seems to be, the more layers you automate with LLMs, the worse each successive layer gets. Piping LLM output as input into new LLM calls, you're already starting to notice how things fall apart and get lost quickly. If you have the idea, more or less the implementation plan, let the LLM do the coding, you can end up with something maintainable and nice, it's basically up to you. Strip away one layer,…
I think this principle applies only if you lack feedbacks. Yes, when you go through multiple layers of open loop control, you're going to get less precise at each layer. It's less clear that the situation is as dire if each level has metrics and can self-adjust to optimize its performance.
But these are inherently subjective things, what the "right idea" is, or the "right implementation" is all up in our head that we can try to verbalize, but I don't think you can come up with an objective score for it, ask 100 programmers you'll get 100 different answers what "clean design" is.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#6Earlier quoted context omitted.
I think this principle applies only if you lack feedbacks. Yes, when you go through multiple layers of open loop control, you're going to get less precise at each layer. It's less clear that the situation is as dire if each level has metrics and can self-adjust to optimize its performance.
But these are inherently subjective things, what the "right idea" is, or the "right implementation" is all up in our head that we can try to verbalize, but I don't think you can come up with an objective score for it, ask 100 programmers you'll get 100 different answers what "clean design" is.
And that's why my whole schtick when it comes to agent design is that agents need to learn online, continuously, and in adapter space via some PEFT mechanism (I like soft prompts and prefix tuning), because it's really hard to ascend gradients in discrete domains like tokens.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#7Despite skills being just a new form of memory and context engineering for an agent, I think the framework is still great for agents to self-develop, given a good prompt to regularly review their own sessions and pick learning points to save as skills. In fact, I think the "craft" of prompt engineering has been lost somewhat - I still enjoy puzzling out and iterating over the best possible starting prompt for a conversation to get the best result I can for a one-shot
FWIW I didn't read the paper and am judging it based on its title, which I think is fair because "self-generated agent skills" is a pretty loose definition.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#8"Self-Generated Skills: No Skills provided, but the agent is prompted to generate relevant procedural knowledge before solving the task. This isolates the impact of LLMs’ latent domain knowledge"
This is a useful result, but it is important to note that this is not necessarily what people have in mind when they think of "LLMs generating skills." Having the LLM write down a skill representing the lessons from the struggle you just had to get something done is more typical (I hope) and quite different from what they're referring to.
I'm sure news outlets and popular social media accounts will use appropriate caution in reporting this, and nobody will misunderstand it.
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#9It seems intuitive that a naive self-generated Skill would be low-value, since the model already knows whatever it's telling itself. However, I've found them to be useful for capturing instructions on how to use other tools (e.g. hints on how to use command-line tools or APIs). I treat them like mini CLAUDE.mds that are specific only to certain workflows. When Claude isn't able to use a Skill well, I ask it to reflec…
+1, I said as much here: https://news.ycombinator.com/item?id=47040811
Re: SkillsBench: Benchmarking how well agent skills work across diverse tasks
#10Despite skills being just a new form of memory and context engineering for an agent, I think the framework is still great for agents to self-develop, given a good prompt to regularly review their own sessions and pick learning points to save as skills. In fact, I think the "craft" of prompt engineering has been lost somewhat - I still enjoy puzzling out and iterating over the best possible starting prompt for a conve…
Develop an ai skill to read articles and come up with a HN post for you :)