181–190 of 306 posts
Skills are mostly snake oil, the way people use them (the aspiration to download kung foo from a celebrity). There was a time when maybe it mattered (last year), but with good repos and good prompts today's agents can find exactly what they need without any skills. "Skills" as developer macros can be useful, but at most those are things shared with the team (in the repo), not something you download from the internet.…
The first dimension is easy: I simply keep copies of debugged skill files in iCloud and copy them where I need them.
The second dimension is where I spend my time: I use short skill files for fast inference APIs and tiny skill files when I am running slow local models, and I simply spend a lot of time writing and tuning tiny skills files.
Of course, with increasingly better models, skill files become less relevant, but not totally irrelevant.
I don't use any skills, what kinds of skills are people finding most useful? For general tasks, the model seems perfectly capable of figuring out things itself, for project or environment specific tasks, I just put that information in the readme or agents.md file.
- I don't find skills, I create them - Keep them organised in software repos that you install with symlinks for all coding harnesses that you have. Progressive disclosure based on the frontmatter does the rest. - I make sure they work with AI evals. Think of them like integration tests to prove behaviour. They're useful to optimize your flows. I try to make my skills be mostly a translation between natural language a…
People always say this about the evals, but I find it hard to have a practical implementation of such a thing where you won’t end up spending 100x the amount of time on the evals than building the skill itself. Like, ok, I have a debugging skill, now how do I make evals except for the most trivial things?
They get pinned with nix together with the software that they come from.
It's just two 3rd party skills now:
playwright-cli and herdr.
All the rest are skills for the software itself, so they live in the same repo and get updated the same way docs get updated.
- I don't find skills, I create them - Keep them organised in software repos that you install with symlinks for all coding harnesses that you have. Progressive disclosure based on the frontmatter does the rest. - I make sure they work with AI evals. Think of them like integration tests to prove behaviour. They're useful to optimize your flows. I try to make my skills be mostly a translation between natural language a…
I need to manage skill files across 2 Macs and 1 VPS, and also across fast inferencing APIs vs. very slow local models. The first dimension is easy: I simply keep copies of debugged skill files in iCloud and copy them where I need them. The second dimension is where I spend my time: I use short skill files for fast inference APIs and tiny skill files when I am running slow local models, and I simply spend a lot of ti…