improve-codebase-architecture
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Skill by jturner421
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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Use cases: documents, frontend
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Files it touches
~/.claude/skills/improve-codebase-architecture
What's inside
- Skill name
improve-codebase-architecture- Description
- Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
- License
- MIT
- Instructions
- 69 lines in SKILL.md · view on GitHub
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:
- 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.
For a codebase large enough that walking it would crowd your context, spawn a sub-agent (or one per hot-spot area, in parallel) to walk it and report friction; explore a small codebase directly. 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
Settle the candidates, their diagrams, and the report's structure before you write, then write it once; drafting it in full in your reasoning and again in the output doubles the cost without improving it.
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. Write the card text in plain, literal language: short sentences, one idea per paragraph, terms spelled out on first use, and a literal phrase wherever one exists instead of a metaphor.
**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, include it with the warning callout and let its recommendation strength carry the judgment: real friction earns `Strong` or `Worth exploring`, a theoretical refactor the ADR forbids gets `Speculative` or is dropped from the top recommendation. Mark it clearly in the card (e.g. a warning callout: _"contradicts ADR-0007, but worth reopening because…"_).
See [HTML-REPORT.md](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.From jturner421/matt_pocock_skills, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 1, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/jturner421/matt_pocock_skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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improve-codebase-architecture skill. Agent Mart. https://agentmart.dev/skills/jturner421-improve-codebase-architecture (last updated 2026-10-24)