| ▲ | postalcoder 8 hours ago |
| Folks have run comparisons. From a huggingface employee: 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.
https://xcancel.com/ben_burtenshaw/status/200023306951767675...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. https://github.com/huggingface/upskill |
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| ▲ | iainmerrick 8 hours ago | parent | next [-] |
| I can't quite tell what's being compared there -- just looks like several different LLMs? 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. |
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| ▲ | postalcoder 8 hours ago | parent | next [-] | | Your skepticism is valid. Vercel ran a study where they said that skills underperform putting a docs index in AGENTS.md[0]. 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-... | | |
| ▲ | vidarh 6 hours ago | parent [-] | | Agents add a docs index in context for skills, so this is an issue of finding that the current specific implementation of skills in Claude Code is suboptimal. 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. |
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| ▲ | anupamchugh 5 hours ago | parent | prev [-] | | > If you want a clean comparison, I’d test three conditions under equal context budgets: (A) monolithic
> AGENTS.md, (B) README index that links to docs, (C) skills with progressive disclosure. Measure task
> success, latency, and doc‑fetch count across 10–20 repo tasks. My hunch: (B)≈(C) on quality, but (C)
> wins on token efficiency when the index is strong. Also, format alone isn’t magic—skills that reference
> real tools/assets via the backing MCP are qualitatively different from docs‑only skills, so I’d
> separate those in the comparison. Have you seen any benchmarks that control for discovery overhead? |
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| ▲ | pton_xd 8 hours ago | parent | prev | next [-] |
| I think the point is it smells like a hack, just like "think extra hard and I'll tip you $200" was a few years ago. It increases benchmarks a few points now but what's the point in standardizing all this if it'll be obsolete next year? |
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| ▲ | 8 hours ago | parent | next [-] | | [deleted] | |
| ▲ | mbesto 7 hours ago | parent | prev | next [-] | | I think this tweet sums it correctly doesn't? 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
Which is essentially the bitter lesson that Richard Sutton talks about? | | |
| ▲ | Der_Einzige 3 hours ago | parent [-] | | Nice ChatGPT generated response in that tweet. Anyone too lazy to deslop their tweet shouldn't be listened to. |
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| ▲ | 9dev 7 hours ago | parent | prev [-] | | Standards have to start somewhere to gain traction and proliferate themselves for longer than that. 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. |
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| ▲ | xrd 8 hours ago | parent | prev | next [-] |
| Does this indicate running locally with a very small (quantized?) model? 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? |
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| ▲ | 8cvor6j844qw_d6 8 hours ago | parent | prev [-] |
| Sounds like the benchmark matrix just got a lot bigger, model * skill combinations. |
