improve-codebase-architecture
mattpocock/skills · Agent Skill
Scans a codebase for deepening opportunities and turns them into a prioritized visual report.
Jump to install ↓Overview
| Publisher | |
|---|---|
| Repository | mattpocock/skills ↗ |
| Skill folder | improve-codebase-architecture |
| Stars | 303,927 |
| Forks | 25,575 |
| Bundled files | 3 |
| License | MIT |
| Last updated | October 22, 2026 |
| Links | Skill source ↗ · Repository ↗ |
Mozgi check
What we found in the skill files. Details come with a subscription.
Installation
A skill is a folder with a SKILL.md file. Pick your agent and run the commands in a terminal (macOS, Linux or Git Bash on Windows) from your project root.
Copy the prompt into Claude, Codex or another assistant: it will review the skill and install it for you.
Install the “improve-codebase-architecture” skill from mozgi.io (https://mozgi.io/skills/mattpocock-improve-codebase-architecture/) for me. 1. Download the archive: https://mozgi.io/skills/mattpocock-improve-codebase-architecture/download If it fails, take the skill folder from the source: https://github.com/mattpocock/skills/tree/main/skills/engineering/improve-codebase-architecture 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “improve-codebase-architecture” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/improve-codebase-architecture/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/mattpocock/skills.git /tmp/skills mkdir -p .claude/skills cp -r /tmp/skills/skills/engineering/improve-codebase-architecture .claude/skills/improve-codebase-architecture
Restart Claude Code — the skill appears in the list; call it manually with /improve-codebase-architecture. For all projects use ~/.claude/skills.
git clone --depth 1 --branch main https://github.com/mattpocock/skills.git /tmp/skills mkdir -p .agents/skills cp -r /tmp/skills/skills/engineering/improve-codebase-architecture .agents/skills/improve-codebase-architecture
Codex looks for skills in .agents/skills from the current folder up to the repo root; call one with /skills or $improve-codebase-architecture. For all projects use ~/.agents/skills.
git clone --depth 1 --branch main https://github.com/mattpocock/skills.git /tmp/skills mkdir -p .cursor/skills cp -r /tmp/skills/skills/engineering/improve-codebase-architecture .cursor/skills/improve-codebase-architecture
Cursor picks the skill up on start; call it with “/” in Agent chat. Cursor also reads .claude/skills and .agents/skills. For all projects use ~/.cursor/skills.
git clone --depth 1 --branch main https://github.com/mattpocock/skills.git /tmp/skills mkdir -p .gemini/skills cp -r /tmp/skills/skills/engineering/improve-codebase-architecture .gemini/skills/improve-codebase-architecture
Gemini CLI offers to activate the skill when a task matches — you approve it. For all projects use ~/.gemini/skills.
Read SKILL.md and any companion files before deciding whether to install. Click a file to preview it.
SKILL.md contents
Original author text · read-onlyImprove 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.mdgives names to good seams; ADRs indocs/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 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 toGLOSSARY.md. Create the file lazily if it doesn't exist. - Sharpening a fuzzy term during the conversation? Update
GLOSSARY.mdright 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.
Frequently asked questions
What does the improve-codebase-architecture skill do?
Scans a codebase for deepening opportunities and turns them into a prioritized visual report.
How do I install improve-codebase-architecture?
Open the “Installation” block on this page and pick your agent — Claude Code, Codex, Cursor or Gemini CLI — for ready-made commands that copy the skill folder to the right place. Or copy the prompt from the “Via assistant” tab and let the assistant review and install the skill.
Which AI models work with improve-codebase-architecture?
Skills are plain Markdown instructions (the Agent Skills format), so improve-codebase-architecture works with any agent that reads SKILL.md: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and more. The model loads the instructions only when a task matches the skill's description.
Is improve-codebase-architecture free?
The skill is published openly by mattpocock under the MIT license. The skill itself costs nothing — you only pay your AI provider for the tokens you use.
Is it safe to install skills?
A skill is a set of instructions your agent will follow, so read SKILL.md and its companion files before installing. Scripts in the skill folder only run if your agent runs them — keep an eye on what it does and do not grant unnecessary permissions.
How is a skill different from an MCP server or a plugin?
An MCP server or plugin gives the model new tools — code that runs and returns a result. A skill gives it knowledge and process: how to approach a task, which steps to follow and what good output looks like. It needs no server or API key.