Comment by alexhans
6 hours ago
- 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 and good small fast tools that they call.
- I change them as a new problem arises. Not just because.
Skills can't be eaten by model capabilities if skills represent a workflow that is custom to my team or my person.
I wrote about a good mental model in the past:
https://alexhans.github.io/posts/series/evals/building-agent...
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?
You don't. If you're using skills to force the AI to fullfill some must criterias, it's not going to work. Must criterias need deterministic checks -> be it hooks or what not.
This is also my biggest gripe with AI. I.e. for specifications, no matter what hype machine I tried, it never fulfilled my criterias, which are: easily verifiable, concise, small specs. Hence I built https://github.com/RicardoMonteiroSimoes/Yamlet initially for claude code, but then decided to use extend it for pi.dev. I now have a dedicated docker image for pi.dev, that only contains Yamlet plugin, and whenever I work on spec I spin it up.
The end result is a .yaml file that easily works in git + git diff, so that I can then proceed with the technical specs-
Why do you have a debugging skill? Just tell it to read the docs.
Skills are for packaging instructions for how to interact with your organizations homebrew process and tools. By definition skills shouldn’t be useful outside of your org because they’re just docs and third party tools already have them for humans.
A skill should only document behaviour the LLM didn't/couldn't exhibit on its own.
So you take your failed case (eg. working with gdb or whatever), write a skill and then test for that failed case.
There are also skills that help LLM do the thing it can do without the skill, but faster (by cutting out unnecessary discovery). I guess for such skills the fail case is "being slow"?
I imagine many fail cases can burn a lot of tokens/usage/time because failing LLMs can be very persistent. Maybe some upper bound (turn count, timeout) would help too.
Big yes on this. I do not understand the appeal of skill shopping. The one exception I have is things like the Axiom Apple development skills and e.g. the official Flutter skills. At that point the skills are just docs though. It's either I remember to paste a URL to the official docs or I just install the skill. But shopping around for random skills just sounds extremely unappealing.
I do both, or rather I do 'skill browsing', for new ideas to then evaluate the skill with my agent if they are useful. Most are not, but some I extract ideas from to augment my own skills https://github.com/flurdy/agent-skills/tree/main/skills#shar...
Though most of the time my skills are just things I found useful and could avoid repeating myself by having as a skill.
That I also use it to route model used with https://github.com/flurdy/pi-skill-model-router is also a reason