Your post made me curious to try a problem I have been coming back to ever since ChatGPT was first released: https://open.kattis.com/problems/low
I have had no success using LLM's to solve this particular problem until trying Gemini 3 just now despite solutions to it existing in the training data. This has been my personal litmus test for testing out LLM programming capabilities and a model finally passed.
Just went to the comments searching for a comment like yours and I'm surprised it seems to be the only one calling this out. My take on this is also that "Skills" is just detailed documentation, which like you correctly point out, basically never exist for any project. Maybe LLM skills will be the thing that finally makes us all write detailed documentation but I kind of doubt it.
Can we really? All the reporting on climate change definitely has me thinking otherwise. There are options more respectful to our planet than digging tunnels like for example planting trees to help mediate temperatures.
While I agree with you in principle give Claude 4 a try on something like: https://open.kattis.com/problems/low .
I would expect this to have been included in the training material as well as solutions found on Github. I've tried providing the problem description and asking Claude Sonnet 4 to solve it and so far it hasn't been successful.
Just remembered some more details. The speaker covers the difference in applying something akin to the scientific method vs jumping to conclusions based on previous encounters of the same/similar issues.
I also think he's mostly right but even solved problems can still see much improvement. Take for example algorithmic improvements to improve speed or reduce memory requirements. Then there's also massive power usage improvements to be had by re-implementing existing solutions using more efficient languages [1].