improve-codebase-architecture

Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

11 stars

Best use case

improve-codebase-architecture is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

Teams using improve-codebase-architecture should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/improve-codebase-architecture/SKILL.md --create-dirs "https://raw.githubusercontent.com/jvgomg/podkit/main/.agents/skills/improve-codebase-architecture/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/improve-codebase-architecture/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How improve-codebase-architecture Compares

Feature / Agentimprove-codebase-architectureStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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](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.

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