I'm not familiar with Skills, but looking at the repo I find the amount of decorative code/text as overkill for what amounts to just the following prompt in a bash script (yikes) executing after a commit is run:
{"hookSpecificOutput":{"hookEventName":"PostToolUse","additionalContext":"[learning-opportunities-auto] The user just committed code. Per the learning-opportunities skill, consider whether this is a good moment to offer a learning exercise. If the committed work involved new files, schema changes, architectural decisions, refactors, or unfamiliar patterns, ask the user (one short sentence) if they'd like a 10-15 minute exercise. Do not start the exercise until they confirm. If they decline, note it — no more offers this session."}}
Skills are just a good standard to describe repeatable workflows saving context through progressive disclosure, prompt sharing and, very underused feature, also bound the non deterministic parts with determism (which could be scripts).
Conceptually, you should treat them as incremental software instead of magic you grab from others [1]
The killer feature is that coding harnesses tend to have SkillBuilder agent skills so creating them becomes very easy and you can evolve them.
I recommend you build your own for your particular pain points.
Very simple example [2] showing what another user mentioned around "evals" so that you can really achieve good enough correctness for your automation.
most stuff in these tools is just another md file which get spliced into prompt somehow. its how llms work.. this is normal. its also why id recommend people to use claude to build a similar tool for themselve. you will spend some tokens on it and then after save like 90% token costs using your own tool... its really crazy how much less tokens and calls are needed to do meaningful work....
also you can secure/lockdown tool calls better and make the agents tasks retryable, give it failure modes etc. (not if ur laptop dies during agent work its only god and the agent who know what happened to your code.. oh no wait. the agent needs to just spend 100k tokens to remember where it was (great way to spend ur money).
I really love the idea, I've had Claude make textbooks for me on the fly using open source textbooks and documentation. Is it possible to extend this skill to more generalized areas of learning / application? Or, is it domain specific to code?
I think it means human skill development. It offers learning opportunities to the user.
> When you complete architectural work (new files, schema changes, refactors), Claude offers optional 10-15 minute learning exercises grounded in evidence-based learning science. The exercises use techniques like prediction, generation, retrieval practice, and spaced repetition to provide you with semi-worked examples from across your own project work.
Looks interesting! I know it's easy to setup and test it but I'm on mobile current so I think it'd be great if there was full-interaction example to better understand how it works.
Spring is reasonably easy to learn. The hard part is knowing where beans are defined, because Spring doesn't make that easy at all. Anyone and anything can define new beans in any library you pull.
I still don't see why AI would be mandatory. It's helpful, yes, but not mandatory.
is that why navigating a Spring codebase is so confusing? I'm jumping through implementations and definitions and whatever without ever reaching the actual business logic most of the time
I'm not familiar with Skills, but looking at the repo I find the amount of decorative code/text as overkill for what amounts to just the following prompt in a bash script (yikes) executing after a commit is run:
Skills are just a good standard to describe repeatable workflows saving context through progressive disclosure, prompt sharing and, very underused feature, also bound the non deterministic parts with determism (which could be scripts).
Conceptually, you should treat them as incremental software instead of magic you grab from others [1]
The killer feature is that coding harnesses tend to have SkillBuilder agent skills so creating them becomes very easy and you can evolve them.
I recommend you build your own for your particular pain points.
Very simple example [2] showing what another user mentioned around "evals" so that you can really achieve good enough correctness for your automation.
- [1] https://alexhans.github.io/posts/series/evals/building-agent...
- [2] https://alexhans.github.io/posts/series/evals/sketch-to-text...
most stuff in these tools is just another md file which get spliced into prompt somehow. its how llms work.. this is normal. its also why id recommend people to use claude to build a similar tool for themselve. you will spend some tokens on it and then after save like 90% token costs using your own tool... its really crazy how much less tokens and calls are needed to do meaningful work....
also you can secure/lockdown tool calls better and make the agents tasks retryable, give it failure modes etc. (not if ur laptop dies during agent work its only god and the agent who know what happened to your code.. oh no wait. the agent needs to just spend 100k tokens to remember where it was (great way to spend ur money).
I really love the idea, I've had Claude make textbooks for me on the fly using open source textbooks and documentation. Is it possible to extend this skill to more generalized areas of learning / application? Or, is it domain specific to code?
What exactly is the "adaptive dynamic textbook approach"?
Examples?
> Generation effect: Accepting generated code and decreasing generating one's own code can skip the active processing that builds understanding.
Holy truth.
No benchmarks and evals present, how do you know it produces better result than /create-skill ? Naive testing doesn't provide any confidence
I think it means human skill development. It offers learning opportunities to the user.
> When you complete architectural work (new files, schema changes, refactors), Claude offers optional 10-15 minute learning exercises grounded in evidence-based learning science. The exercises use techniques like prediction, generation, retrieval practice, and spaced repetition to provide you with semi-worked examples from across your own project work.
Confusing name though.
Hey, it's awesome that you mention evals. May I ask what you currently use, or look for? Do you roll your own or use an existing framework?
Looks interesting! I know it's easy to setup and test it but I'm on mobile current so I think it'd be great if there was full-interaction example to better understand how it works.
Mhh, interesting.
I want to learn Java spring, and probably let ai help me / quiz me. I will take a look into the skills for inspiration.
Is there a reason why making a spring app and learning hands-on is not feasible?
I know I sometimes get demotivated mid-way, but that also tells me it might not be worth the investment
Spring is reasonably easy to learn. The hard part is knowing where beans are defined, because Spring doesn't make that easy at all. Anyone and anything can define new beans in any library you pull.
I still don't see why AI would be mandatory. It's helpful, yes, but not mandatory.
is that why navigating a Spring codebase is so confusing? I'm jumping through implementations and definitions and whatever without ever reaching the actual business logic most of the time