improve-codebase-architecture
Skill4 starsChanged 7 months agoMITView on GitHub
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.
195 other projects use a version of this, 37 of them edited.
Adopted by
Most recently taken up by
- chrysogonus/power-agentstheir own version, first seen Nov 8, 2026
- caidanw/skillstheir own version, first seen Nov 8, 2026
- domengabrovsek/agent-configtheir own version, first seen Nov 8, 2026
- domengabrovsek/claudetheir own version, first seen Nov 8, 2026
- The-Vibe-Company/quivrtheir own version, first seen Nov 8, 2026
- miklschmidt/archboardtheir own version, first seen Nov 8, 2026
Used with
Projects with it also use migrate-to-shoehorn, writing-beats and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
simo97Skillmigrate-to-shoehorn
Migrate test files from `as` type assertions to @total-typescript/shoehorn. Use when user mentions shoehorn, wants to replace `as` in tests, or needs partial test data.
Also in 73 projects that use this
LesaniSkillwriting-beats
Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
Also in 68 projects that use this
simo97Skillrequest-refactor-plan
Use this skill when user wants to create a refactor plan.
Also in 43 projects that use this
liufeng1976Skillsetup-ts-deep-modules
Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.
Also in 43 projects that use this
FabienDehopreSkilldecision-mapping
Turn a loose idea into a sequenced map of investigation tickets, then drive them to resolution one at a time.
Also in 8 projects that use this
LesaniSkillwriting-shape
Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
Also in 65 projects that use this
- +2 more from simo97
Other versions195 projects use a version of this.
Word for word · 0
None found.
Edited · 37
- sanity-io/sanity★ 6.4k99% same
- openstatusHQ/data-table-filters★ 2.3k99% same
- cashew-labs/libretto★ 89811% same
- saffron-health/libretto★ 88311% same
- deadlock-mod-manager/deadlock-mod-manager★ 58414% same
- amazingloft999-droid/mattpocock-skills★ 42212% same
- akitaonrails/my-skills★ 28312% same
- letehaha/moneymatter★ 22112% same
- yamcodes/arkenv★ 15111% same
- alex-on-ai/WebReaper★ 14912% same
27 more not listed
Wrote their own version · 158
- mattpocock/skills★ 309ksame name
- danny-avila/LibreChat★ 47ksame name
- LibreChat-AI/LibreChat★ 47ksame name
- QuivrHQ/quivr★ 40ksame name
- The-Vibe-Company/quivr★ 40ksame name
- windmill-labs/windmill★ 18ksame name
- GreyDGL/PentestGPT★ 16ksame name
- humanlayer/humanlayer★ 13ksame name
- ArcReel/ArcReel★ 6.3ksame name
- vinvcn/mattpocock-skills-zh-CN★ 5.5ksame name
148 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository21 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
5 rules. 1 is common, 3 are distinctive, and 1 is very specific to this project.
1
Common
Most of the 184 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
Use the Agent tool with subagent_type=Explore to navigate the codebase naturally.
- Common: line 38
Do NOT propose interfaces yet.
- Distinctive: line 44
Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate:
- Distinctive: line 73
After comparing, give your own recommendation: which design you think is strongest and why.
- Very specific: line 79
Create a refactor RFC as a GitHub issue using gh issue create.
“Common” is measured: kept by at least 6 in 10 of the 184 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

