I like to actually have hierarchical plans.

Start with the big idea. Pin down the product manager-level description, and maybe some details about which features are in or out, how the phased design/roadmap will work, maybe what the basic tech stack will be.

That document gets saved, and has the big picture context.

Next, we go a little deeper, flesh out schemas, APIs, a little more about what the code should look like, the overall testing strategy. This document gets saved, and the AI can reference it for context.

The next level is a very specific implementation plan. Think individual small JIRA tickets in an epic. The AI writes this based on previous context. I make sure each individual step has very explicit instructions about branch names, which branch is branched from where, the testing and documentation that will be done for that step. It should be obvious from the higher level documents, but that helps keep the AI to only worrying about the local document in its context window.

Then have a DAG of stacked pull requests designed for subagents to work through.

All of this keeps the context window down (as well as costs down, and accuracy up) and works super well, and I can get a tremendous amount of code that is easy to review through stacked PRs. Invariably I will want to fix earlier ones then percolate that down the stack, so I have some skills for that.

I try to do similar. I'll even break down parts of the plan into separate docs and have it solely refer to them one by one as needed with /clear between. It's a more manual process but not the worst. I could probably even whip up some kind of skill to automate it somewhat with subagents or similar.