Generated banner for Improve Codebase Architecture
Generated logo for Improve Codebase Architecture

Improve Codebase Architecture

Matt PocockCommunityCreated by Matt Pocock

  • Skill
  • Research and analysis
  • Software development
  • MIT
  • No issues found

From the author

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
— the description by Matt Pocock

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to improving codebase architecture by identifying shallow modules and proposing refactors that increase depth, testability, and AI-navigability. It combines automated codebase scanning with a visual reporting phase and an interactive 'grilling' session to refine architectural decisions. The process is grounded in a specific design vocabulary and respects existing domain models and architectural decision records (ADRs).

What it does

  • Scans codebase hot spots to identify architectural friction and shallow modules.
  • Generates a visual HTML report using Tailwind and Mermaid to present refactoring candidates.
  • Provides before-and-after visualizations for proposed architectural changes.
  • Facilitates an interactive discussion to explore constraints and dependencies for chosen candidates.
  • Updates the project glossary and suggests new ADRs based on architectural decisions.
  • Uses a specific design vocabulary to ensure consistency in architectural discussions.

When to use it

  • When a codebase feels difficult to navigate or test due to shallow, tightly-coupled modules.
  • When you need to identify high-impact areas for refactoring based on recent commit history.
  • When you want to communicate architectural improvement proposals to a team using visual reports.
  • When you need to resolve architectural friction while maintaining alignment with existing domain models.

Advantages

  • Focuses on deepening modules to improve long-term maintainability and testability.
  • Uses visual reporting to make abstract architectural concepts easier to understand.
  • Integrates with existing project documentation like GLOSSARY.md and ADRs.
  • Prevents repetitive suggestions by allowing the recording of load-bearing architectural decisions.
  • Encourages a disciplined approach to design by using a shared, precise vocabulary.

Usage examples

“Scan the codebase for architectural improvements.”
The agent analyzes recent commit history, identifies shallow modules, and generates an HTML report with visual diagrams and recommendations.
“I want to focus on the Order intake module; can you find deepening opportunities there?”
The agent skips the general scan and performs a targeted analysis of the specified module, presenting specific refactoring candidates in an HTML report.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/mattpocock/improve-codebase-architecture/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip improve-codebase-architecture.zip -d .agents/skills

Documentation by Matt Pocock

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.

Files

agents/openai.yaml

interface:
  display_name: "Improve Codebase Architecture"
  short_description: "Find and grill architecture improvements"
policy:
  allow_implicit_invocation: false

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Matt Pocock
Publisher
Matt Pocock (Community)
Source
mattpocock/skills · skills/engineering/improve-codebase-architecture · commit 4ae2317
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Matt Pocock.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download improve-codebase-architecture.zip