codex + skills finetunes Qwen3-0.6B to +6 on humaneval and beats the base score on the first run.
I reran the experiment from this week, but used codex's new skills integration. Like claude code, codex consumes the full skill into context and doesn't start with failing runs. It's first run beats the base score, and on the second run it beats claude code.
That said, it's not a perfect comparison because of the Codex model mismatch between runs.
The author seems to be doing a lot of work on skills evaluation.
I am very interested in finding ways to combine skills + local models + MCP + aider-ish tools to avoid using commercial LLM providers.
Is this a path to follow? Or, something different?
To be clear, I'm suggesting that any specific format for "skills.md" is a red herring, and all you need to do is provide the LLM with good clear documentation.
A useful comparison would be between: a) make a carefully organised .skills/ folder, b) put the same info anywhere and just link to it from your top-level doc, c) just dump everything directly in the top-level doc.
My guess is that it's probably a good idea to break stuff out into separate sections, to avoid polluting the context with stuff you don't need; but the specific way you do that very likely isn't important at all. So (a) and (b) would perform about the same.
https://xcancel.com/ben_burtenshaw
My guess is that the standardization is going to make its way into how the models are trained and Skills are eventually going to pull out ahead.
0: https://vercel.com/blog/agents-md-outperforms-skills-in-our-...
A +6 jump on a 0.6B model is actually more impressive than a +2 jump on a 100B model. It proves that 'intelligence' isn't just parameter count; it is context relevance. You are proving that a lightweight model with a cheat sheet beats a giant with amnesia. This is the death of the 'bigger is better' dogma
Plus, as has been mentioned multiple times here, standard skills are a lot more about different harnesses being able to consistently load skills into the context window in a programmatic way. Not every AI workload is a local coding agent.
Their reasoning about it is also flawed. E.g. "No decision point. With AGENTS.md, there's no moment where the agent must decide "should I look this up?" The information is already present." - but this is exactly the case for skills too. The difference is just where in the context the information is, and how it is structured.
Having looked at their article, ironically I think the reason it works is that they likely force more information into context by giving the agent less information to work with:
Instead of having a description, which might convince the agent a given skill isn't relevant, their index is basically a list of vague filenames, forcing the agent to make a guess, and potentialy reading the wrong thing.
This is basically exactly what skills were added to avoid. But it will break if the description isn't precise enough. And it's perfectly possible that current tooling isn't aggressive enough about pruning detail that might tempt the agent to ignore relevant files.
we wrote a blog on getting agents to write CUDA kernels and evaluating them: https://huggingface.co/blog/upskill
I see this with Cursor all the time with tools. Cursor will stop editing files in the editor and use the command line to echo edits into a file. It's so frustrating.
I explicitly tell it about the skills and that it should load them when the context feels correct.
```prompt.md
Company Name codebase ...
# Skills
Use the company specific skills that look like `company-*`. Load them once per conversation if they seem relevant to what you are working on.
```
```SKILL.md
---
description: Company TypeScript libraries and conventions
trigger: Writing or reading TypeScript in Company services ---
# company-ts
```
Agent Skills isn't a spec for how information is presented to the model, its a spec whose consumer is the model harness, which might present information made available to it in the format to the model in different ways for different harnesses, or even in the same harness for different models or tasks, considering things like the number and size of the skill(s) available, the size of the model context, the purpose of the harness (is it for a narrow purpose agent where some of the skills are central to that purpose?), and user preference settings.
The site itself has two different main styles of integration for harnesses described ("tool based" and "filesystem based"), but those are more of a starting point for implementers that an exhaustive listing.
The idea is that skill authors don't need to know or care how the harness is presenting the information to the model.
(1) providing a bash tool with direct access to the filesystem storing the skills to the model,
(2) providing read_file and related tools to the model,
(3) by providing specialized tools to access skills to the model,
(4) by processing the filesystem structure and providing a structure that includes the full content of the skills up front to the model.
And probably some other ways or hybrids.
> It increases benchmarks a few points now but what's the point in standardizing all this if it'll be obsolete next year?
Standardizing the information presentation of skills to LLM harnesses lets the harnesses incorporate findings on optimization (which may be specific to models, or at least model features like context size, and use cases) and existing skills getting the benefit of that for free.
And if you're just making docs and letting your models go buck wild in your shell, doesn't an overspecified docs structure ruin the point of general purpose agents?
Like, a good dev should be able to walk into a codebase, look at the structure, and figure out how to proceed. If "hey your docs aren't where I was expecting" breaks the developer, you shouldn't have hired them.
Feels like a weird thing to take "this is how we organize our repos as this company" and turn that into "this is an 'open standard' that you should build your workflows around".