Improve Codebase Architecture

SkillDev tools

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick. Informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Improve Codebase Architecture skill

What this skill tells your AI

The instructions your AI receives, as published by ayoubben18/ab-method in .agents/skills/improve-codebase-architecture/SKILL.md and read by ahel’s review.

Surface architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.

This skill is built on a shared design vocabulary and informed by the project's domain model:

  • Architecture vocabulary — run the codebase-design skill for the terms (module, interface, depth, seam, adapter, leverage, locality) and the principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use those terms exactly in every suggestion — don't drift into "component," "service," "API," or "boundary." It is the single source; this skill doesn't restate it.
  • Domain languageCONTEXT.md gives names to good seams; ADRs in docs/adr/ record decisions this skill should not re-litigate. See CONTEXT-FORMAT.md and ADR-FORMAT.md.

Process

1. Explore

Scope before you scan — YAGNI. Deepening a module pays off by making future changes to it easier, so weight the parts of the codebase that keep changing. Decide where to look before looking:

  • 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 history (git log --oneline) 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 accumulated drift first if the project has it. Every task's docs/tasks/*/change-map.md records the modules that task planned to touch and the ones it actually touched. A module that shows up as unplanned across several tasks is a sharper hot-spot signal than raw commit frequency: it isn't just where changes land, it's where changes land that nobody expected them to. That is a locality problem by definition, and it's the highest-value place to start. (change-map only reports it — acting on it is this skill's job.)

Read the existing documentation first:

  • CONTEXT.md (or CONTEXT-MAP.md + each CONTEXT.md in a multi-context repo)
  • Relevant ADRs in docs/adr/ (and any context-scoped docs/adr/ directories)

If any of these files don't exist, proceed silently — don't flag their absence or suggest creating them upfront.

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 — open on macOS, xdg-open on Linux, start on Windows — and tell the user the absolute path.

The report uses Tailwind via CDN for layout and Mermaid via CDN for graph-shaped diagrams. Each candidate gets a before/after visualisation. Be visual — the diagrams carry the weight, the prose is sparse.

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 — in terms of locality and leverage, and how tests would improve
  • Before / After diagram — side by side, illustrating the shallowness and the deepening
  • Recommendation strengthStrong, Worth exploring, or Speculative, rendered as a badge

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

See HTML-REPORT.md for the scaffold, diagram patterns, and styling guidance.

No network? Fall back to markdown. The Tailwind and Mermaid CDNs are the only external dependency; if the environment is offline or the user asks for it, present the same candidate cards as a numbered markdown list in the conversation — same fields, same badges, same Top recommendation. The report format is a presentation choice, not the substance.

Use CONTEXT.md vocabulary for the domain and 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. an amber callout: "contradicts ADR-0007 — but worth reopening because…"). Don't list every theoretical refactor an ADR forbids.

Do NOT propose interfaces yet. After the file is written, ask: "Which of these would you like to explore?"

3. Grilling loop

Once the user picks a candidate, invoke the grill-with-docs 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 — the grill maintains the domain model as it goes:

  • Naming a deepened module after a concept not in CONTEXT.md? Add the term to CONTEXT.md — same discipline as /domain-model (see CONTEXT-FORMAT.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. See ADR-FORMAT.md.
  • Want to explore alternative interfaces for the deepened module? Run the codebase-design skill and use its DESIGN-IT-TWICE.md parallel sub-agent pattern.
  • Deepening a cluster with awkward dependencies? DEEPENING.md has the four dependency categories and the seam discipline for each.

This skill proposes and designs; it does not implement. Once the shape is agreed, the work becomes a task — /create-task grills it into missions and tdd builds it. Don't start editing source here.

Signals

GitHub stars
190
Forks
14
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
improve-codebase-architecture-ayoubben18
Source
github.com/ayoubben18/ab-method