Improve Codebase Architecture

Explore a codebase like an AI would, surface architectural friction, discover opportunities for improving testability, and propose module-deepening refactors as GitHub issue RFCs.

A deep module (John Ousterhout, "A Philosophy of Software Design") has a small interface hiding a large implementation. Deep modules are more testable, more AI-navigable, and let you test at the boundary instead of inside.

Process

1. Explore the codebase

Use the Agent tool with subagent_type=Explore to navigate the codebase naturally. Do NOT follow rigid heuristics, explore organically and note where you experience friction:

• Where does understanding one concept require bouncing between many small files?
• Where are modules so shallow that the interface is nearly as complex as the implementation?
• Where have pure functions been extracted just for testability, but the real bugs hide in how they're called?
• Where do tightly-coupled modules create integration risk in the seams between them?
• Which parts of the codebase are untested, or hard to test?

The friction you encounter IS the signal.

2. Present candidates

Present a numbered list of deepening opportunities. For each candidate, show:

• Cluster: Which modules/concepts are involved
• Why they're coupled: Shared types, call patterns, co-ownership of a concept
• Dependency category: See REFERENCE.md for the four categories
• Test impact: What existing tests would be replaced by boundary tests

Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?"

3. User picks a candidate

4. Frame the problem space

Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate: