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mattpocock/improve-codebase-architecture

mattpocock

improve-codebase-architecture

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

NewUpdated Oct 30, 2026

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:

  • Call the Skill tool with "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 these terms exactly in every suggestion, and don't drift into "component," "service," "API," or "boundary."
  • 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 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

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, 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 GLOSSARY.md vocabulary for the domain, and the /codebase-design vocabulary for the architecture. 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 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, 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? Call the Skill tool with "codebase-design" and use its design-it-twice parallel sub-agent pattern.
Files3
3 files · 7.7 KB

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Overall Score

88/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

92

Quality

87

Clarity

85

Completeness

83

Summary

This skill guides an AI agent through a three-phase architecture review: scanning a codebase for architectural friction points (shallow modules, coupling, testability issues), generating a visual HTML report with refactoring candidates, and then interactively walking through the user's chosen candidate via a "grilling" conversation. The skill is built on a shared design vocabulary (module, interface, depth, seam, adapter) and integrates with project documentation (GLOSSARY.md, ADRs) to ground recommendations in the codebase's domain model.

Detected Capabilities

code analysisfile readinggit log parsingHTML file generationtemporary file writingfile opening/launchingcross-file documentation updatesdomain model updates

Trigger Keywords

Phrases that agents use to match this skill to user intent.

refactor shallow modulesarchitecture reviewfind couplingdeepening candidatesmodule designarchitectural frictioninterface collapse

Risk Signals

INFO

Reads project commit history via git log

SKILL.md, Process/1.Explore
INFO

Writes to OS temp directory with timestamp

SKILL.md, Process/2.Present
INFO

Opens generated HTML files using platform-specific commands (xdg-open, open, start)

SKILL.md, Process/2.Present
INFO

Updates GLOSSARY.md with new terms discovered during grilling

SKILL.md, Process/3.Grilling loop
INFO

Creates ADR files in docs/adr/ based on user rejection reasons

SKILL.md, Process/3.Grilling loop
INFO

References external CDNs for Tailwind and Mermaid in HTML

HTML-REPORT.md, Scaffold

Referenced Domains

External domains referenced in skill content, detected by static analysis.

cdn.jsdelivr.netcdn.tailwindcss.com

Use Cases

  • Identify shallow modules causing testability friction
  • Visualize architectural coupling and leakage patterns
  • Prioritize refactoring efforts based on codebase hot spots
  • Deepen modules by collapsing interfaces and concentrating logic
  • Record architectural decisions as ADRs during exploration
  • Build shared vocabulary around architectural concepts
  • Validate refactoring candidates against existing ADRs

Quality Notes

  • Strong pedagogical structure: the three-phase process (Explore → Present → Grill) is clearly delineated with specific decision points
  • Excellent vocabulary constraint: skill explicitly requires use of defined glossary terms and forbids substitutions (component, service, layer), ensuring consistency
  • Clear scope boundaries: only reads project files and documentation, writes only to temp directory, no access to credentials or external systems
  • Well-documented output format: HTML-REPORT.md provides detailed guidance on diagram patterns, styling, tone, and example structures
  • Thoughtful handling of domain integration: skill checks GLOSSARY.md and ADRs before making suggestions, respects prior architectural decisions
  • Good edge case handling: instructions address scattered changes, lack of clear hot spots, ADR conflicts, and when not to propose ADRs
  • Practical guidance on when to use Mermaid vs. hand-built diagrams avoids over-reliance on graph rendering
  • Interactive loop design (grilling) allows refinement and learning; domain model updates are inline
  • No rigid heuristics: exploration is described as organic, guided by friction signals rather than checklist rules
  • Minor clarity gap: 'sub-agent with fresh context window' implies agent architecture not fully spelled out for implementers, but intent is clear
Model: claude-haiku-4-5-20251001Analyzed: Oct 30, 2026

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Version History

  1. v2.3

    Content updated

    ✦ AISub-agent spawned with fresh context window for codebase walk instead of default context.

    2026-10-30

    LATEST
  2. v2.2

    Content updated

    ✦ AIUpdates all references from CONTEXT.md to GLOSSARY.md as the domain vocabulary source.

    2026-09-30

    View This Version
  3. v2.1

    Content updated

    ✦ AIAdds scoping guidance (YAGNI analysis of commit history before exploration), changes Agent tool reference to generic sub-agent spawn, and refines wording throughout.

    2026-09-09

    View This Version
  4. v2.0

    Contract changed: description

    ✦ AIChanges activation contract: now outputs visual HTML report and delegates domain vocabulary to a separate skill.

    triggering2026-06-28

    View This Version
  5. v1.0

    2026-05-02

    View This VersionInitial version

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