Correct me if I'm wrong but I think the new "skillss are exactly this, but better.
Indeed, the article links to the skill documentation which says:
Skills are modular capabilities that extend Claude’s functionality through organized folders containing instructions, scripts, and resources.
And
Extend Claude’s capabilities for your specific workflows
E.g. building your project is definitely a workflow.
It als makes sense to put as much as you can into a skill as this an optimized mechanism for claude code to retrieve relevant information based on the skill’s frontmatter.
I have found that more context comments and info damage quality on hard problems.
I actually for a long time now have two views for my code.
1. The raw code with no empty space or comments. 2. Code with comments
I never give the second to my LLM. The more context you give the lower it's upper end of quality becomes. This is just a habit I've picked up using LLMs every day hours a day since gpt3.5 it allows me to reach farther into extreme complexity.
I suppose I don't know what most people are using LLMs for but the higher complexity your work entails the less noise you should inject into it. It's tempting to add massive amounts of xontext but I've routinely found that fails on the higher levels of coding complexity and uniqueness. It was more apparent in earlier models newer ones will handle tons of context you just won't be able to get those upper ends of quality.
Compute to informatio ratio is all that matters. Compute is capped.
See it as a human, the comments are there to speed up understanding of the code.