deep-module-refactor
Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules
Install / Use
npx skills add zebbern/claude-code-guide --skill deep-module-refactorInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of deep-module-refactor
deep-module-refactor scores 84/100 on our quality scale, 1498th of 3,168 Development & Engineering skills we index (top 48%).
Its SKILL.md is 4.0 KB long, well organised into 9 sections and no code examples: a solid amount of guidance for an agent.
With 4,638 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 2 days ago, so deep-module-refactor is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-28. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
deep-module-refactor compared with similar skills
All 4 of these similar skills score higher than deep-module-refactor; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| deep-module-refactor (this skill)by zebbern | 84 | 4.6k | 2d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.9k | 13d ago | CLAUDE.md |
| ai-job-searchby MadsLorentzen | 100 | 44.3k | today | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.7k | 2d ago | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 5d ago | SKILL.md |
Frequently asked questions
- How do I install deep-module-refactor?
- Run
npx skills add zebbern/claude-code-guide --skill deep-module-refactor. The install tabs above show the steps for each supported agent. - Which AI agents does deep-module-refactor work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is deep-module-refactor safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is deep-module-refactor still maintained?
- The repository was last updated 2 days ago, so deep-module-refactor is actively maintained.
Skill content
View source on GitHubname: deep-module-refactor description: 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.
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:
- 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:
- Interface signature (types, methods, params)
- Usage example showing how callers use it
- What complexity it hides internally
- Dependency strategy (how deps are handled — see REFERENCE.md)
- 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. Do NOT ask the user to review before creating — just create it and share the URL.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
