Improve codebase architecture
π€ Curated agent skills for AI agents β one coherent engineering loop that compounds: grill β spec β implement β review β commit β learn. Adopt it or fork it!
npx -y skills add toverux/grimoire --skill improve-codebase-architectureAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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- 27 days oldThe repository was created 27 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
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What its author says it does
Copied from the file, not written here
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
SKILL.md
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Improve Codebase Architecture
Surface architectural friction and propose deepening opportunities β refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.
Built on a shared design vocabulary: run /codebase-design first and use its terms and principles exactly in every suggestion.
Where the project keeps a domain glossary (an AGENTS.md glossary section or CONCEPTS.md), its terms give names to good seams.
Process
1. Explore
Scope before you scan β 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 β a module, a subsystem, a pain point β 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 β the files and areas that keep coming up β 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 first, and search docs/solutions/ for learnings and recorded rejections in the area β decisions already settled there should not be re-litigated.
Then dispatch an exploration subagent to walk the codebase. Don't follow rigid heuristics β explore organically and note where you experience friction:
- Where does understanding one concept require bouncing between many small modules?
- Where are modules shallow β 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 β xdg-open <path> on Linux, open <path> on macOS, start <path> on Windows β 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 β 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 β which files/modules are involved
- Problem β why the current architecture is causing friction
- Solution β plain English description of what would change
- Benefits β explained in terms of locality and leverage, and how tests would improve
- Before / After diagram β side-by-side, custom-drawn, illustrating the shallowness and the deepening
- Recommendation strength β 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 the project's domain glossary for the domain, and the codebase-design vocabulary for the architecture.
If the glossary defines "Order," talk about "the Order intake module" β not "the FooBarHandler," and not "the Order service."
Settled-decision conflicts: if a candidate contradicts a decision recorded in docs/solutions/, only surface it when the friction is real enough to warrant revisiting the decision.
Mark it clearly in the card (e.g. a warning callout: "contradicts a recorded rejection β but worth reopening becauseβ¦").
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, grill them through the decision tree β one question at a time, your recommended answer marked, each branch resolved before the next: constraints, dependencies, the shape of the deepened module, what sits behind the seam, what tests survive.
(The /grilling skill is this discipline in full; the user can invoke it by name.)
Side effects happen inline as decisions crystallize β capture the durable ones through /compound:
- A deepened module named after a term missing from the domain glossary, or a fuzzy term sharpened during the conversation β a glossary candidate.
- The user rejects a candidate with a load-bearing reason β capture it so future architecture reviews don't re-suggest the same thing. Skip ephemeral reasons ("not worth it right now") and self-evident ones.
When alternative interfaces for the deepened module are worth exploring, close with a flow pointer (presentation): /codebase-design β its design-it-twice parallel sub-agent pattern surfaces the options.
Gives 3 of the 12 instructions most architecture codebase skills give
Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-06
- ask the user which candidate to explorehere, and in 46 of 811, across 16 files
- apply the deletion test to suspected shallow moduleshere, and in 43 of 811, across 15 files
- read any relevant architecture decision records firstin 31 of 811, across 7 files
- use exact glossary terms in every suggestionin 29 of 811, across 9 files
- accept dependencies instead of creating themin 24 of 811, across 5 files
- include before and after visualisations for each candidatein 24 of 811, across 5 files
- read the domain glossary before exploringhere, and in 24 of 811, across 6 files
- return results instead of producing side effectsin 23 of 811, across 4 files
- explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
- introduce seams only where things varyin 22 of 811, across 3 files
- reduce the number of methodsin 21 of 811, across 2 files
- design deep modules with small interfacesin 21 of 811, across 2 files
Said here and by no other author read
- run codebase-design first
- scope before scanning
- grill the user through one question at a time
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.