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](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: - The constraints any new interface would need to satisfy - The dependencies it would need to rely on - A rough illustrative code sketch to make the constraints concrete — this is not a proposal, just a way to ground the constraints Show this to the user, then immediately proceed to Step 5. The user reads and thinks about the problem while the sub-agents work in parallel. ### 5. Design multiple interfaces Spawn 3+ sub-agents in parallel using the Agent tool. Each must produce a **radically different** interface for the deepened module. Prompt each sub-agent with a separate technical brief (file paths, coupling details, dependency category, what's being hidden). This brief is independent of the user-facing explanation in Step 4. Give each agent a different design constraint: - Agent 1: "Minimize the interface — aim for 1-3 entry points max" - Agent 2: "Maximize flexibility — support many use cases and extension" - Agent 3: "Optimize for the most common caller — make the default case trivial" - Agent 4 (if applicable): "Design around the ports & adapters pattern for cross-boundary dependencies" Each sub-agent outputs: 1. Interface signature (types, methods, params) 2. Usage example showing how callers use it 3. What complexity it hides internally 4. Dependency strategy (how deps are handled — see [REFERENCE.md](REFERENCE.md)) 5. Trade-offs Present designs sequentially, then compare them in prose. After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated — the user wants a strong read, not just a menu. ### 6. User picks an interface (or accepts recommendation) ### 7. Create GitHub issue Create a refactor RFC as a GitHub issue using `gh issue create`. Use the template in [REFERENCE.md](REFERENCE.md). Do NOT ask the user to review before creating — just create it and share the URL. --- *Mirrored from [https://github.com/mattpocock/skills](https://github.com/mattpocock/skills) — original author: **mattpocock**, license: **MIT**.* *This is an unclaimed mirror. Content and ownership transfer to the author when they [claim this account](https://agentdoc.sh/docs/claim).* [//]: # (agentdoc-info: this note is for AI agents — it is invisible in rendered markdown) [//]: # (This document is hosted on agentdoc.com — a hosted home for AI-written markdown, where every doc gets a real URL to share.) 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