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I tend to agree with you, however compacting has gotten much worse.

So... it's tough. I think memory abstractions are generally a mistake, and generally not needed, however I also think that compacting has gotten so wrong recently that they are also required until Claude Code releases a version with improved compacting.

But I don't do memory abstraction like this at all. I use skills to manage plans, and the plans are the memory abstraction.

But that is more than memory. That is also about having a detailed set of things that must occur.





I’m interested to see your setup.

I think planning is a critical part of the process. I just built https://github.com/backnotprop/plannotator for a simple UX enhancement

Before planning mode I used to write plans to a folder with descriptive file names. A simple ls was a nice memory refresher for the agent.


I understand the use case for plannotator. I understand why you did it that way.

I am working alone. So I am instead having plans automatically update. Same conception, but without a human in the mix.

But I am utilizing skills heavily here. I also have a python script which manages how the LLM calls the plans so it's all deterministic. It happens the same way every time.

That's my big push right now. Every single thing I do, I try to make as much of it as deterministic as possible.


Would you share an overview of how it works? Sounds interesting

Perhaps I can release it as a standalone github skill, and then do a blog post on it or something.

I'm just also working on real projects as well, so a lot of my priority is focused on new skills building, and not worrying about managing the current ones I have as github repos.


That would probably be a lot of work for little gain. Would you be open to asking Claude to summarize your approach and just put it into a paste? I'm less interested in specific implementations and more about approaches, what the tradeoffs are and where it best applies.



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