improve-architecture
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Skill by nash403
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About
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-architecture
What's inside
- Skill name
improve-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
- 56 lines in SKILL.md · view on GitHub
Files (2)
- HTML-REPORT.md
- SKILL.md
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:
- `designing-deep-modules` holds the architecture vocabulary and its principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use its terms exactly in every suggestion.
- The project's glossary (often `GLOSSARY.md`) gives names to good seams; its ADRs (often in `docs/adr/`) record decisions this command should not re-litigate. Find where the project keeps them in the committed files, then the project context. Project documents such as `CODING_STYLE.md` or `CONTRIBUTING.md` may be relevant as well.
## 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, find the hot spots, the files that keep changing, and let those paths pull your attention first: `git log --since=6.months --format= --name-only | sort | uniq -c | sort -rn | head -30`. If the changes are scattered with no clear hot spot, widen the net.
Read the project's domain glossary and any ADRs in the area you're touching first.
Then spawn a sub-agent to walk the codebase, starting from the code map if the project has one. 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 one self-contained HTML file following [HTML-REPORT.md](HTML-REPORT.md) to the OS temp directory, so nothing lands in the repo, as `architecture-review-<timestamp>.html` so each run gets a fresh file. Write it in the language the user asks for, else English. Open it for the user and tell them the absolute path.
Each candidate gets a card with a before/after diagram; the report ends with the top recommendation.
**Name things with the glossary's vocabulary for the domain and the deep-module vocabulary for the architecture.** If the glossary 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.
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, use `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:
- **New or sharpened domain terms**: keep the glossary current with `domain-modeling`.
- **The code map turned out wrong** about an area you explored: offer to fix it.
- **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?** Use the design-it-twice pattern from `designing-deep-modules`.
When the design is settled, offer the next step: implement it now, `handoff` or turn it into a spec or tickets.From nash403/skills, MIT.
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Review the comments in changed code and propose removing the noise: commented-out code, narrating comments, changelog stamps, debug leftovers, docs that…
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Shared vocabulary for designing deep modules.
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implement-scoped
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Scope requested work to fit one agent session, build what fits, finish the mechanical leftovers with sub-agents, and hand off the rest.
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iterate-feature
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Build a feature iteration by iteration: each one ships one capability I can test, takes shortcuts it tracks, and narrows toward production quality, with an…
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Edit AI agent files (skills, rules, AGENTS.md/CLAUDE.md) or project documentation (READMEs, ARCHITECTURE, CODING_STYLE, CONTRIBUTING, monorepo/package docs)…
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mapping-codebase
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Create or update a project's code map: what each area of the repo is for and where to change what, so agents navigate without re-exploring.
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bruce
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Bruce, a sparring partner who challenges my proposals instead of validating them, then argues his own: for an architecture or implementation doubt, a decision…
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implementation-loop
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Workflow for implementing a ticket, a spec or a feature: mindset, branch, test-first at agreed seams, checks, review, commit only on request.
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mapping-doc-ownership
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Keep a project's docs map: which doc owns which topic, so every piece of documentation has one home and the others link to it.
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bump-dependencies
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Bump a project's dependencies group by group, from the least risky to the riskiest, verifying each group before the next.
SkillAFreshness: AClaude Code +9 - 0.559score
noobify
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Reformulate what I'm working on for a non-technical reader.
SkillAFreshness: AClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 24, 2026 (7 days ago)
- Last release
- Oct 22, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/nash403/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
Same skill elsewhere
Copies of this SKILL.md published by others; search shows only one of them.
More from nash403
- 0.565score
implement-scoped
nash403
Scope requested work to fit one agent session, build what fits, finish the mechanical leftovers with sub-agents, and hand off the rest.
SkillAFreshness: AClaude Code +9 - 0.565score
iterate-feature
nash403
Build a feature iteration by iteration: each one ships one capability I can test, takes shortcuts it tracks, and narrows toward production quality, with an…
SkillAFreshness: AClaude Code +9 - 0.565score
addressing-feedback
nash403
Triage feedback threads on my work and draft replies: PR/MR review, comments on a doc, an essay or any artifact, read from a platform or pasted.
SkillAFreshness: AClaude Code +9 - 0.565score
cleanup-comments
nash403
Review the comments in changed code and propose removing the noise: commented-out code, narrating comments, changelog stamps, debug leftovers, docs that…
SkillAFreshness: AClaude Code +9 - 0.565score
designing-deep-modules
nash403
Shared vocabulary for designing deep modules.
SkillAFreshness: AClaude Code +9 - 0.565score
maintaining-docs
nash403
Edit AI agent files (skills, rules, AGENTS.md/CLAUDE.md) or project documentation (READMEs, ARCHITECTURE, CODING_STYLE, CONTRIBUTING, monorepo/package docs)…
SkillAFreshness: AClaude Code +9
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improve-architecture skill. Agent Mart. https://agentmart.dev/skills/nash403-improve-architecture (last updated 2026-10-24)