Earlier quoted context omitted.
Yep. Now I need an AI to help me use AI
I mean, that is a very common thing that I do.
That's why the key word for all the AI horror stories that have been emerging lately is "recursion".
Claude Skills
41–50 of 443 posts
Re: Claude Skills
#42It superficially reminds me of the old "Alexa Skills" thing (I'm not even sure if Alexa still has "Skills"). It might just be the name making that connection for me.
And how many of those Alexa Skills are still being updated... This is where waiting for this stuff to stablize/standardize, and then writing a "skill" based on an actual RFC or standard protocol makes more sense, IMO. I've been burned too many times building vendor-locked chatbot extensions.
> And how many of those Alexa Skills are still being updated...
Not mine! I made a few when they first opened it up to devs, but I was trying to use Azure Logic Apps (something like that?) at the time which was supremely slow and finicky with F#, and an exercise in frustration.
Re: Claude Skills
#43getting hard to keep up with skills, plugins, marketplaces, connectors, add-ons, yada yada
Agreed, but I think it's actually simple.
Plugins include: * Commands * MCPs * Subagents * Now, Skills
Marketplaces aggregate plugins.
Re: Claude Skills
#44Subagents, plugins, skills, hooks, mcp servers, output styles, memory, extended thinking... seems like a bunch of stuff you can configure in Claude Code that overlap in a lot of areas. Wish they could figure out a way to simplify things.
Re: Claude Skills
#45Earlier quoted context omitted.
I mean, that is a very common thing that I do.
That's why the key word for all the AI horror stories that have been emerging lately is "recursion".
Does that imply no human in the loop? If so, that's not what I meant, or do. Whoever is doing that at this point: bless your heart :)
Re: Claude Skills
#46I love how the promise of free labor motivates everyone to become API first, document their practices, and plan ahead in writing before coding.
Cheaper, not free. Also, no training to learn a new skill.
Building a new one that works well is a project, but then it will scale up as much as you like.
This is bringing some of the advantages of software development to office tasks, but you give up some things like reliable, deterministic results.
Re: Claude Skills
#47I feel like a danger with this sort of thing is that the capability of the system to use the right skill is limited by the little blurb you give about what the skill is for. Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. But Claude is always starting from ground zero and skimming your descriptions.
LLMs are a probability based calculation, so it will always skim to some degree, and always guess to some degree, and often pick the best choice available to it even though it might not be the best.
For folks who this seems elusive for, it's worth learning how the internals actually work, helps a great deal in how to structure things in general, and then over time as the parent comment said, specifically for individual cases.
Re: Claude Skills
#48I feel like a danger with this sort of thing is that the capability of the system to use the right skill is limited by the little blurb you give about what the skill is for. Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. But Claude is always starting from ground zero and skimming your descriptions.
IMO this is a context window issue. Humans are pretty good are memorizing super broad context without great accuracy. Sometimes our "recall" function doesn't even work right ("How do you say 'blah' in German again?"), so the more you specialize (say, 10k hours / mastery), the better you are at recalling a specific set of "skills", but perhaps not other skills.
On the other hand, LLMs have a programatic context with consistent storage and the ability to have perfect recall, they just don't always generate the expected output in practice as the cost to go through ALL context is prohibitive in terms of power and time.
Skills.. or really just context insertion is simply a way to prioritize their output generation manually. LLM "thinking mode" is the same, for what it's worth - it really is just reprioritizing context - so not "starting from scratch" per se.
When you start thinking about it that way, it makes sense - and it helps using these tools more effectively too.
Re: Claude Skills
#49Earlier quoted context omitted.
It's interesting (to me) visualizing all of these techniques as efforts to replicate A* pathfinding through the model's vector space "maze" to find the desired outcome. The potential to "one shot" any request is plausible with the right context.
> The potential to "one shot" any request is plausible with the right context. You too can win a jackpot by spinning the wheel just like these other anecdotal winners. Pay no attention to your dwindling credits every time you do though.
On the other hand, our industry has always chased the "one baby in one month out of 9 mothers" paradigm. While you couldn't do that with humans, it's likely you'll soon (tm) be able to do it with agents.