[smithkl42]

It's all about managing context. The bitter lesson applies over the long haul - and yes, over the long haul, as context windows get larger or go away entirely with different architectures, this sort of thing won't be needed. But we've defined enough skills in the last month or two that if we were to put them all in CLAUDE.md, we wouldn't have any context left for coding. I can only imagine that this will be a temporary standard, but given the current state of the art, it's a helpful one.

[OtherShrezzing]

I use Claude pretty extensively on a 2.5m loc codebase, and it's pretty decent at just reading the relevant readme docs & docstrings to figure out what's what. Those docs were written for human audiences years (sometimes decades) ago.

I'm very curious to know the size & state of a codebase where skills are beneficial over just having good information hierarchy for your documentation.

[SOLAR_FIELDS]

Claude can always self discover its own context. The question becomes whether it's way more efficient to have it grepping and lsing and whatever else it needs to do randomly poking around to build a half-baked context, or whether having a tailor made context injection that is dynamic can speed that up.

In other words, if you run an identical prompt, one with skill and one without, on a test task that requires discovering deeply how your codebase works, which one performs better on the following metrics, and how much better?

1. Accuracy / completion of the task

2. Wall clock time to execute the task

3. Token consumption of the task

[pertymcpert]

Skills are more than code documentation. They can apply to anything that the model has to do, outside of coding.

[iainmerrick]

To clarify, when I mentioned the bitter lesson I meant putting effort into organising the "skills" documentation in a very specific way (headlines, descriptions, etc).

Splitting the docs into neat modules is a good idea (for both human readers and current AIs) and will continue to be a good idea for a while at least. Getting pedantic about filenames, documentation schemas and so on is just bikeshedding.

[storus]

Why not replace the context tokens on the GPU during inference when they become no longer relevant? i.e. some tool reads a 50k token document, LLM processes it, so then just flush those document tokens out of active context, rebuild QKV caches and store just some log entry in the context as "I already did this ... with this result"?

[killerstorm]

Anthropic added features like this into 4.5 release:

https://claude.com/blog/context-management

> Context editing automatically clears stale tool calls and results from within the context window when approaching token limits.

> The memory tool enables Claude to store and consult information outside the context window through a file-based system.

But it looks like nobody has it as a part of an inference loop yet: I guess it's hard to train (i.e. you need a training set which is a good match for what people use context in practice) and make inference more complicated. I guess more high-level context management is just easier to implement - and it's one of things which "GPT wrapper" companies can do, so why bother?

[zozbot234]

This is what agent calls do under the hood, yes.

[ledauphin]

how is it different or better than maintaining an index page for your docs? Or a folder full of docs and giving Claude an instruction to `ls` the folder on startup?

[d1sxeyes]

Vercel think it isn’t:

https://vercel.com/blog/agents-md-outperforms-skills-in-our-...

[Avicebron]

It's hard to tell unless they give some hard data comparing the approaches systematically.. this feels like a grift or more charitably trying to build a presence/market around nothing. But who knows anymore, apparently saying "tell the agent to write it's own docs for reference and context continuity" is considered a revelation.

[stingraycharles]

Not sure why you’re being downvoted so much, it’s a valid point.

It’s also related to attention — invoking a skill “now” means that the model has all the relevant information fresh in context, you’ll have much better results.

What I’m doing myself is write skills that invoke Python scripts that “inject” prompts. This way you can set up multi-turn workflows for eg codebase analysis, deep thinking, root cause analysis, etc.

Works very well.