Invoke /domain-modeling to resolve and read the project's configured artifacts; persist accepted model changes through that owner only when this task authorizes edits. Use /swarm for any delegated work with the actual host schema, isolated writers, and disclosed fallback.

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:

• Invoke /codebase-design 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 this vocabulary for design reasoning while preserving the project's established domain names.
• The configured vocabulary and model give names to good seams; the configured ADRs record decisions this command should not re-litigate. Resolve these locations through /domain-modeling.

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.

Read the project's configured vocabulary, model, and relevant ADRs first.

Then spawn a sub-agent with a fresh context window 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, with an 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

Before grading candidates, invoke /principles for this planning phase and apply the selected principles to each candidate and to the top recommendation.

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). Escape repository and issue text before embedding it. Each candidate gets a before/after visualisation. Be visual.

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 the configured vocabulary for the domain and /codebase-design for architecture reasoning. Name a proposed seam after its domain concept. Preserve established project terms such as "Order service" when they have a specific documented meaning.

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, invoke /grilling 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; invoke /domain-modeling to keep the domain model current as you go:

• Naming a deepened module after a new domain concept? Record the accepted term in the configured vocabulary through /domain-modeling.
• Sharpening a fuzzy term or relationship? Reconcile its authoritative vocabulary or model document through that same owner.
• User rejects a candidate for a durable tradeoff? Apply /domain-modeling's ADR criteria. Record a qualifying settled decision within the current grant; leave an unresolved decision as a question. Only record it 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? Invoke /codebase-design and use its design-it-twice parallel sub-agent pattern.

Complete when the report grounds each candidate in observed friction, the selected candidate's design frontier is resolved, and accepted model changes are reconciled without duplicate artifacts. Implementation requires its own task scope.