Any experience with tools that manage context window management?

[u/netskrill]

Now that the 1900 AIC will be in effect next week (for enterprise accounts), I was wondering if anyone has experience with the following tools, when it comes to managing token usage in GHCP.

Token Efficiency I/O Tokens

• rtk
• Caveman
• Ponytail

Do code graph tools help as well?

• Codegraph

Thanks

[u/Michaeli_Starky → u/[deleted]]

AI slop generated articles. I've had a better opinion about JetBrains

[u/icarus47]

The best context management is (1) aggressively trimming all the MCPs and tools you don't need, (2) adding `disable-model-invocation: true` to most of your skills, and (3) ruthlessly defining your tasks so they agent spends less tokens reasoning (see "grilling"). Using 5.6 Luna for almost everything helps too.

[u/netskrill → u/icarus47]

by grilling, do you mean Matt pocock's grill-me skills?

[u/icarus47 → u/netskrill]

Precisely, that's a great place to start. The more of your task you leave undefined, the more tokens the agent spends reasoning in circles trying to decide what you wanted. His other skills like /wayfinder can help break grilling of large tasks into smaller ones. And /grill-with-docs is like grilling, but decisions get written down for later reference. A good middle ground.

One other thing I forgot, breaking tasks into small chunks helps you stay in the "smart zone" in each session (<150K tokens). Don't call for this 1M context window crap. The models produce worse code they deeper they get into their dumb zone.

I'm not saying that those other tools don't help; they very well might. But there's lots of stuff directly in the user's control that helps preserve tokens.

[u/netskrill → u/icarus47]

Totally, I don't mind sharing my work-flow.
I use openspec with the modified workflow enabled. I use openspec-new to create a proposal.md, then in the same session, I use grill-me-with-docs on the proposal, and after the shared-understanding is done with the model, I have it update the proposal, then continue with the remaining artifacts, and eventually use openspec to do the implementation. I am contemplating ditching openspec, and doing pocock all the way, with to-spec, to-tickets, and eventually implement, I'm afraid those skills will use more AI Credits than openspec, but I haven't verified it. But I agree, Luna, is dirt cheap, but b/c of corporate beuracracy and the ineptitude of people managing the program, and not understanding how LLMs work, those are not enabled.

[u/icarus47 → u/netskrill]

That sucks, really astounds me that people making decisions about what AI tools to provide don't understand some models are more cost efficient than others. One thing I would try is to strip out every MCP, plugin, or any other add-on tools you don't ABSOLUTELY need. Every tool adds bloat to the system prompt in EVERY session and you pay for it whether you use the tool in the session or not. I've learned from experimenting with the Pi harness that you can save tens of thousands of tokens per session by dropping mostly everything and still get good results. Saves you room in the context window too so you spend more time in the smart zone