Yes, and one of my favorite anecdotes like this: at one of the greatest jazz concert ever recorded, Charlie Parker played a cheap plastic saxophone because he hadn't brought his own.
This is great and echoes my experience. Although I would add a caveat that this mostly applies to solo work. Once you need to collaborate or operate on a team, many of limits of self-hosting return.
I believe slash commands are all loaded into the initial context and executed when invoked by the user. Skills on the other hand only load the name and description into initial context, and the agent (not user) determines when to invoke them, and only then is the whole skill loaded into context. So skills shift decision making to the agent and use progressive disclosure for context efficiency.
Thank you for publishing that paper, which I think greatly helped address this problem at the time, which you accurately describe. I guess things have to be taken in their historical context, and science is a community project which may not uniformly follow best practices, but work like this can help get everyone in line! It's unfortunate, and no fault of the authors, that the general public has run wild with referencing this work to reject fMRI as a experimental technique. There's plenty of different ways to criticize it today, for sure.
This study was really highlighting a statistical issue which would occur with any imaging technique with noise (which is unavoidable). If you measure enough things, you'll inevitably find some false positives. The solution is to use procedures such as Bonferroni and FDR to correct for the multiple tests, now a standard part of such imaging experiments. It's a valid critique, but it's worth highlighting that it's not specific to fMRI or evidence of shaky science unless you skip those steps (other separate factors may indicate shakiness though).
To a large extent, I think this could be solved by labs having more long-term permanent research staff (technicians, data analysts, scientists) and reducing the number of PhD students. Many students would gladly stay on in that position instead of leaving, so it increases job opportunities. It would also improve the quality of the science because the permanent staff would have more historical knowledge, in contrast to the current situation where students constantly rotate in and out with somewhat messy hand-offs. The students could also then focus more on scholarly work, planning and overseeing research execution with the team. The problem is that the incentives are aligned to allocate students to doing all lab tasks, not long term staff. I think we could change this through changes to the requirements and structure of science funding mechanisms however, since ultimately that's the source of the incentives.
One other feature with CLAUDE.md I’ve found useful is imports: prepending @ to a file name will force it to be imported into context. Otherwise, whether a file is read and loaded to context is dependent on tool use and planning by the agent (even with explicit instructions like “read file.txt”). Of course this means you have to be judicial with imports.
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