Am I missing something here?

Obviously directly including context in something like a system prompt will put it in context 100% of the time. You could just as easily take all of an agent's skills, feed it to the agent (in a system prompt, or similar) and it will follow the instructions more reliably.

However, at a certain point you have to use skills, because including it in the context every time is wasteful, or not possible. this is the same reason anthropic is doing advanced tool use ref: https://www.anthropic.com/engineering/advanced-tool-use, because there's not enough context to straight up include everything.

It's all a context / price trade off, obviously if you have the context budget just include what you can directly (in this case, compressing into a AGENTS.md)

> Obviously directly including context in something like a system prompt will put it in context 100% of the time.

How do you suppose skills get announced to the model? It's all in the context in some way. The interesting part here is: Just (relatively naively) compressing stuff in the AGENTS.md seems to work better than however skills are implemented.

I agree with you.

I think Vercel mixes skills and context configuration up. So the whole evaluation is totally misleading because it tests for two completely different use cases.

To sum it up: Vercel should us both files, agents.md is combination with skills. Both functions have two totally different purposes.

You aren't wrong, you really want a bit of both.

1. You absolutely want to force certain context in, no questions or non-determinism asked (index and sparknotes). This can be done conditionally, but still rule based on the files accessed and other "context"

2. You want to keep it clean and only provide useful context as necessary (skills, search, mcp; and really a explore/query/compress mechanism around all of this, ralph wiggum is one example)

This is one of the reasons the RLM methodology works so well. You have access to as much information as you want in the overall environment, but only the things relevant to the task at hand get put into context for the current task, and it shows up there 100% of the time, as opposed to lossy "memory" compaction and summarization techniques, or probabilistic agent skills implementations.

Having an agent manage its own context ends up being extraordinarily useful, on par with the leap from non-reasoning to reasoning chats. There are still issues with memory and integration, and other LLM weaknesses, but agents are probably going to get extremely useful this year.

My reading was that copying the doc's ToC in markdown + links was significantly more effective than giving it a link to the ToC and instructions to read it.

Which makes sense.

& some numbers that prove that.

You're right, the results are completely as expected.

The article also doesn't mention that they don't know how the compressed index output quality. That's always a concern with this kind of compression. Skills are just another, different kind of compression. One with a much higher compression rate and presumably less likely to negatively influence quality. The cost being that it doesn't always get invoked.

I’ve been using symlinked agent files for about a year as a hacky workaround before skils became a thing load additional “context” for different tasks, and it might actually address the issue you’re talking about. Honestly, it’s worked so well for me that I haven’t really felt the need to change it.