# Improve Codebase Architecture > Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick. - Skill: `caneff/improve-codebase-architecture` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add caneff/improve-codebase-architecture` - Raw SKILL.md: https://api.skillmd.com/api/skills/caneff/improve-codebase-architecture/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - Author: caneff (https://skillmd.com/u/caneff) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/caneff/improve-codebase-architecture --- # 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. This command is _informed_ by the project's domain model and built on a shared design vocabulary: - Run the `/codebase-design` skill for the architecture vocabulary (**module**, **interface**, **depth**, **seam**, **adapter**, **leverage**, **locality**) and its principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use these terms exactly in every suggestion — don't drift into "component," "service," "API," or "boundary." - The domain language in `CONTEXT.md` gives names to good seams; ADRs in `docs/adr/` record decisions this command 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 put extra weight on the parts of the codebase that have recently changed. 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, walk back a good stretch of the commit history (`git log --oneline`) to find the codebase's 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. Whatever you scope to, skip vendored, generated, and dependency trees (`node_modules`, `dist`, `.venv`, `vendor`, build output, lockfiles) and any `.git/` or `worktrees/` tree — deepen only the project's own tracked source. Read the project's domain glossary (`CONTEXT.md`) and any ADRs in the area you're touching first. Then spawn a sub-agent 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. **Mechanical signals.** Alongside the organic walk, run two tool passes over the scoped area and let confirmed hits pull attention the same way a hot spot does — leads, not verdicts: - **Complexity** — `uvx radon cc --json --min C `. A block radon ranks C or worse is a shallowness signal: the interface hides branching the caller can't see, which is exactly what the deletion test should be run against. - **Coupling / import cycles** — `uvx import-linter`. If the target repo already declares a config (`.importlinter`, or `[tool.importlinter]` in `pyproject.toml`), run it as-is. If it doesn't, write a minimal temp config to the OS temp dir (root package = the top-level package directory, one broad `independence` contract over its immediate subpackages), run import-linter against that, then discard it — nothing lands in the repo, same as the report itself. A reported cycle or forbidden import is a tightly-coupled-seam candidate for the leaking-across-seams question above. Both feed the same candidate cards below — name the tool finding (a radon rank, an import-linter cycle) in the card's **Problem**, the same way an organically spotted seam would be; no separate report. ### 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 `/architecture-review-.html` so each run gets a fresh file. Open it for the user — `xdg-open ` on Linux, `open ` on macOS, `start ` on Windows — and tell them the absolute path. The report is styled with the **visual-teach** design system — the same vendored `vt-*` components and `--vt-*` theme tokens the teaching lessons use — so it opens offline, carries a working light/dark mode, and loads no external host. At render time, copy the assets the report uses next to it with `pagelib.copy_assets` (see the harness HTML-REPORT.md) and link them relatively. Use **Mermaid** (vendored, via the `mermaid.js` bridge) for graph-shaped structure — call graphs, dependencies, sequences — and hand-built `vt-diagram` divs/SVG for the editorial visuals (mass diagrams, cross-sections). Each candidate gets a **before/after visualisation**. Be visual — the diagrams carry the weight. For each candidate, render a card with: - **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 the `/codebase-design` 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](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, run the `/grilling` skill to 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 — run the `/domain-modeling` skill to keep the domain model current as you go: - **Naming a deepened module after a concept not in `CONTEXT.md`?** Add the term to `CONTEXT.md`. Create the file lazily if it doesn't exist. - **Sharpening a fuzzy term during the conversation?** Update `CONTEXT.md` right 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. - **Want to explore alternative interfaces for the deepened module?** Run the `/codebase-design` skill and use its design-it-twice parallel sub-agent pattern.