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I wish there was a clean way to compact the conversation into a prompt with all necessary context for a new fresh conversation.
Have you tried Matt Pocock's "handoff" skill?
> Write a handoff document summarising the current conversation so a fresh agent can continue the work. > […]
Like some others, I also built this myself. Overly simply, with tmux, a memory tree, and handoff files and an orchestrator. And yet for how simple it was, it was so effective at minimizing the amount of duplicate context. It's like having shared specialist subagents who source and derive important shared knowledge from separate threads. It's useful because some skills just take too much of a token penalty to invoke a…
Does this work for different harnesses like Codex and Antigravity?
Earlier quoted context omitted.
There is, but your wish of "clean" is ambiguous.
With clean I mean the opposite of how I currently do it, which is by asking the model to compact the whole thread into a prompt which will act as context for next model. My way of prompting this varies and every time I receive the blob of output, I can’t fell how well it managed to capture the necessary details. This way feels lika a dirty way to transfer knowledge from one conversation to another. You said there is?…
I wish there was a clean way to compact the conversation into a prompt with all necessary context for a new fresh conversation.
I'm fed up with compaction. I want my agent to get compacted but also retain full access to the prior conversation via search and tool calls - I want it to know "the requirements for X were discussed in detail previously in conversation C51E31CE-C985-4633-A749-DCC9805A7FEB" and have a tool that lets it dispatch a subagent to find those details again. Do any of the coding agents have this already?
I think the more general problem is that compaction is just a bandaid: you HAVE to dump context to keep going and searching back for it is more expensive than if you had just kept the right context. The better a job the harness does at filtering out junk, the more likely compaction is to remove context that might have been, forgive me, “load bearing”
IMO the default Claude Code / Codex (which to my understanding is almost continually-compacting?) compaction has got much better over the part few months. If you spam sub agents then context will naturally nest, and you can just resurrect them as needed without polluting the main thread.
Earlier quoted context omitted.
Have you tried Matt Pocock's "handoff" skill?
https://github.com/mattpocock/skills/blob/main/skills/produc... > Write a handoff document summarising the current conversation so a fresh agent can continue the work. > […]
They don’t really know what they’re handing off or what you’re trying to actually do, so in a sense it’s not a grounded task for them. Actually, if you think about it, any scenario in which a handoff doc might be valuable is probably almost always better as a subagent thread, because you are paying the same amount of read/write tokens but you can clear things up synchronously.
I’ve found two-way message passing (each get their own write file, they read each others) to work much better because the communication is more grounded in actual coordination/work. You can also give each an inbox so that multiple can write to it. If you do the “progressive disclosure” right it scales subquadratically because they only read/write to others when it’s relevant to what they’re working on.
But IMO “write a handoff” is a trap, as a human you end working in some kind of robot-graffiti codebase full of junk, and it ends up being a booby trap for agents literally within days.
Earlier quoted context omitted.
With clean I mean the opposite of how I currently do it, which is by asking the model to compact the whole thread into a prompt which will act as context for next model. My way of prompting this varies and every time I receive the blob of output, I can’t fell how well it managed to capture the necessary details. This way feels lika a dirty way to transfer knowledge from one conversation to another. You said there is?…
My largest issue is that when I'm looking for this I'm already dangerously close to autocompaction. And what I really want is a prompt which manages to preserve the most important parts of the chat log. And my opinion of important will not be the same as Claude, so we'll need to iterate on what that handoff really is.
If you snapshotted at 90% max context you could pretty reliably start iteratively trim that down, I think? I personally try to save the logs so agents can slice and dice them with sed/awk/jq/whatever when they need to look stuff up, because I’d rather pay the penalty on read (when it’s motivated by something) than in write(where you don’t really know what if anything will be needed), and they can figure out what they need on their own.
What I’d rather have is some way to bake history into the actual model weights (the same way it can recite certain literature or historical/factual stuff without context), with like multi-lora / “experts” that get trained out of band. But this is contrary to the “one fat model” approach to scaling and doesn’t work with closed labs’ business/IP models
I hacked this together with a small local irc server
I wish there was a clean way to compact the conversation into a prompt with all necessary context for a new fresh conversation.
You can refine the protocol as you realize what's working and what isn't. I've been using that for months and it rarely drops important things now.
I was reading about the HF hack and one of the first thing the GPT swarm did was build a messaging system for themselves. This reminded me of that. (Also of how weirded out I was by Moltbook when it came out. Time flies!) Unrelated: when my Claude/Codex finishes cooking (or needs my attention), it pings a local HTTP endpoint that plays a frog sound on my laptop. I found this massively boosts quality of life.
How do you know when they’re done (or need your attention)? Is that a standard “api”/hook? I want that!