
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by whatever domain language and architectural decisions are already documented in the repo. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/sammcj/agentic-coding --skill improve-codebase-architecture- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads improve-codebase-architecture automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Improve Codebase Architecture
Surface architectural friction and propose deepening opportunities - refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.
Glossary
Use these terms exactly in every suggestion. Consistent language is the point - don't drift into "component," "service," "API," or "boundary." Full definitions in LANGUAGE.md.
- Module - anything with an interface and an implementation (function, class, package, slice).
- Interface - everything a caller must know to use the module: types, invariants, error modes, ordering, config. Not just the type signature.
- Implementation - the code inside.
- Depth - leverage at the interface: a lot of behaviour behind a small interface. Deep = high leverage. Shallow = interface nearly as complex as the implementation.
- Seam - where an interface lives; a place behaviour can be altered without editing in place. (Use this, not "boundary.")
- Adapter - a concrete thing satisfying an interface at a seam.
- Leverage - what callers get from depth.
- Locality - what maintainers get from depth: change, bugs, knowledge concentrated in one place.
Key principles (see LANGUAGE.md for the full list):
- Deletion test: imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
- The interface is the test surface.
- One adapter = hypothetical seam. Two adapters = real seam.
This skill is informed by the project's domain model. The domain language gives names to good seams; ADRs record decisions the skill should not re-litigate.
Process
1. Explore
Scope before you scan - YAGNI. Deepening a module pays off by making future changes to it easier, so a deepening opportunity in code nobody touches is a refactor you never cash in. Decide where to look before you look:
- If the user named a direction - a module, a subsystem, a pain point - take it and skip the inference below.
- Otherwise, read back over the last ~20 commits (
git log --oneline -20 --name-only) to find the hot spots - the files and areas that keep coming up - and let those paths pull your attention first. If the changes are scattered with no clear hot spot, widen the net.
Read the project's domain glossary and any ADRs in the area you're touching first.
Then use the Agent tool with subagent_type=Explore to walk the codebase. Don't follow rigid heuristics - explore organically and note where you experience friction:
- Where does understanding one concept require bouncing between many small modules?
- Where are modules shallow - interface 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 (no locality)?
- Where do tightly-coupled modules leak across their seams?
- Which parts of the codebase are untested, or hard to test through their current interface?
Apply the deletion test to anything you suspect is shallow: would deleting it concentrate complexity, or just move it? A "yes, concentrates" is the signal you want.
2. Present candidates as an HTML report
Write a self-contained HTML file to the OS temp directory so nothing lands in the repo. Resolve the temp dir from $TMPDIR, falling back to /tmp (or %TEMP% on Windows), and write to <tmpdir>/architecture-review-<timestamp>.html so each run gets a fresh file. Open it for the user - xdg-open <path> on Linux, open <path> on macOS, start <path> on Windows - and tell them the absolute path.
The report uses Tailwind via CDN for layout and styling, and Mermaid via CDN for diagrams where a graph/flow/sequence reliably communicates the structure. Mix Mermaid with hand-crafted CSS/SVG visuals - use Mermaid when relationships are graph-shaped (call graphs, dependencies, sequences), and hand-built divs/SVG when you want something more editorial (mass diagrams, cross-sections, collapse animations). Each candidate gets a before/after visualisation. Be visual.
For each candidate, the same template as before, but rendered as a card:
- Files - which files/modules are involved
- Problem - why the current architecture is causing friction
- Solution - plain English description of what would change
- Benefits - explained in terms of locality and leverage, and how tests would improve
- Before / After diagram - side-by-side, custom-drawn, illustrating the shallowness and the deepening
- Recommendation strength - one of
Strong,Worth exploring,Speculative, rendered as a badge
End the report with a Top recommendation section: which candidate you'd tackle first and why.
Use CONTEXT.md vocabulary for the domain, and LANGUAGE.md vocabulary for the architecture. If CONTEXT.md defines "Order," talk about "the Order intake module" - not "the FooBarHandler," and not "the Order service."
ADR conflicts: if a candidate contradicts an existing ADR, only surface it when the friction is real enough to warrant revisiting the ADR. Mark it clearly in the card (e.g. a warning callout: "contradicts ADR-0007 - but worth reopening because..."). Don't list every theoretical refactor an ADR forbids.
See HTML-REPORT.md for the full HTML scaffold, diagram patterns, and styling guidance.
Do NOT propose interfaces yet. After the file is written, ask the user: "Which of these would you like to explore?"
3. Grilling loop
Once the user picks a candidate, drop into a grilling conversation. Walk the decision tree with them - constraints, dependencies, the shape of the deepened module, what sits behind the seam, what tests survive.
Side effects happen inline as decisions crystallize:
- Naming a deepened module after a concept not in
CONTEXT.md? Add the term toCONTEXT.md- same discipline as/grill-with-docs(see CONTEXT-FORMAT.md). Create the file lazily if it doesn't exist. - Sharpening a fuzzy term during the conversation? Update
CONTEXT.mdright there. - User rejects the candidate with a load-bearing reason? Offer an ADR, framed as: "Want me to record this as an ADR so future architecture reviews don't re-suggest it?" Only offer when the reason would actually be needed by a future explorer to avoid re-suggesting the same thing - skip ephemeral reasons ("not worth it right now") and self-evident ones. See ADR-FORMAT.md.
- Want to explore alternative interfaces for the deepened module? See INTERFACE-DESIGN.md.
FAQ
What does the improve-codebase-architecture skill do?
Find deepening opportunities in a codebase, informed by whatever domain language and architectural decisions are already documented in the repo. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
How do I install improve-codebase-architecture?
Run npx skills add https://github.com/sammcj/agentic-coding --skill improve-codebase-architecture in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/improve-codebase-architecture/ (personal) or .claude/skills/improve-codebase-architecture/ (project).
Which AI agents can use improve-codebase-architecture?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is improve-codebase-architecture safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains improve-codebase-architecture?
It is published by sammcj in sammcj/agentic-coding. It has 47 recorded installs.
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