improve-codebase-architecture

Files (3)

• HTML-REPORT.md
• SKILL.md
• openai.yaml · agents/

SKILL.md (first lines)

# 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:

- Identify the module interfaces, implementation complexity and behavior seams using the repository's own vocabulary. Prefer opportunities that hide real complexity behind a smaller interface.
- The domain language in `GLOSSARY.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.

Read the project's domain glossary (`GLOSSARY.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**, 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

Present the candidates as markdown in the conversation by default — the substance below is what carries the review, and it reads fine as plain text. Write a self-contained HTML report instead when the user asks for one, or when a before/after visualisation is the only thing that makes a candidate's structure legible.

Taking the HTML route: write the 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 HTML 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. Both CDNs fail silently offline or behind a script blocker, so inline the CSS and hand-build the SVG when the environment can't reach them.

For each candidate, cover:

- **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** — the shallowness and the deepening side by side; a custom-drawn diagram in HTML, the same contrast in prose in markdown
- **Recommendation strength** — one of `Strong`, `Worth exploring`, `Speculative`, rendered as a badge

End with a **Top recommendation** section: which candidate you'd tackle first and why.

**Use GLOSSARY.md vocabulary for the domain and explain architectural terms plainly.** If `GLOSSARY.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 when you take the HTML route.

Do NOT propose interfaces yet. Once the candidates are presented, ask the user: "Which of these would you like to explore?"

### 3. Grilling loop

Once the user picks a candidate, call the Skill tool with "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; call the Skill tool with "domain-modeling" to keep the domain model current as you go:

- **Naming a deepened module after a concept not in `GLOSSARY.md`?** Add the term to `GLOSSARY.md`. Create the file lazily if it doesn't exist.
- **Sharpening a fuzzy term during the conversation?** Update `GLOSSARY.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?** Compare alternative interfaces directly, showing the trade-offs relevant to this module.

From tt-a1i/matt-skills-with-to-goal, MIT.

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