Agent skill

Improve Codebase Architecture

by nodetool-ai in nodetool-ai/nodetool

Survey a codebase for architecture improvements and present a visual report.

AGPL-3.0Auto-check passed

Install Improve Codebase Architecture

skills CLI
$ npx skills add nodetool-ai/nodetool --skill improve-codebase-architecture -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nodetool-ai/nodetool improve-codebase-architecture --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nodetool-ai/nodetool.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/improve-codebase-architecture .claude/skills/improve-codebase-architecture && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
improve-codebase-architecture
GitHub stars
554
Token cost
~1.5k tokens
SKILL.md length
862 words
Files
3
Skills in repo
130
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Survey a codebase for architecture improvements and present a visual report.

  • Works in 3 steps: Explore → Present candidates as an HTML report → Grilling loop
  • Calls git

What it does

Improve Codebase Architecture is an agent skill from nodetool-ai/nodetool. Survey a codebase for architecture improvements and present a visual report. Explore a selected candidate when requested.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `HTML-REPORT.md` and `agents/openai.yaml`).

The repository describes itself as: Agent-first Creative Workspace. The licence is AGPL-3.0.

Example prompts

  • “/improve-codebase-architecture”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Explore
  2. Present candidates as an HTML report
  3. Grilling loop

What it can do on your machine

Read from SKILL.md and the folder at commit f99c652. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Improve Codebase Architecture loads about 1.5k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 862 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nodetool-ai/nodetool at commit f99c652, republished under its AGPL-3.0 licence (© nodetool-ai). 862 words, ~1,546 tokens.

Download SKILL.mdSave it as .claude/skills/improve-codebase-architecture/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
improve-codebase-architecture
description
Survey a codebase for architecture improvements and present a visual report. Explore a selected candidate when requested.
disable-model-invocation
true

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:

  • Run the /codebase-design skill 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 — don't drift into "component," "service," "API," or "boundary."
  • The domain language in CONTEXT.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 (CONTEXT.md) and any ADRs in the area you're touching first.

Explore locally, or delegate bounded areas when agent tools are available and permitted and useful independent work remains. 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 — 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
Show full SKILL.md (312 more words)Show less

End the report with a Top recommendation section: which candidate you'd tackle first and why.

Use CONTEXT.md vocabulary for the domain, and the /codebase-design vocabulary for the architecture. If CONTEXT.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.

For a survey request, deliver the report and recommend a candidate. If the user already selected an area or asked for a design, continue within that scope. Ask for a choice only when the next step depends on an unresolved preference.

3. Grilling loop

Once the user picks a candidate, use grill-with-docs 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 — run the /domain-modeling skill to keep the domain model current as you go:

  • Naming a deepened module after a concept not in CONTEXT.md? Add the term to CONTEXT.md. Create the file lazily if it doesn't exist.
  • Sharpening a fuzzy term during the conversation? Update CONTEXT.md right there.
  • User rejects the candidate with a consequential 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? Run the /codebase-design skill and use its design-it-twice parallel sub-agent pattern.

© nodetool-ai, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in .agents/skills/improve-codebase-architecture of nodetool-ai/nodetool.

  • SKILL.md
  • HTML-REPORT.md
  • agents/openai.yaml

Open the folder on GitHubat commit f99c652

Compare with similar skills

Improve Codebase Architecture next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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PresentationRightNow-AI/openfang18k—~829Automated safety check: PassApache-2.0
AI Presenter VideoNousResearch/hermes-agent252k—~2.3kAutomated safety check: PassMIT
Agent Scout Explorerruvnet/ruflo74k3 repos~1.6kAutomated safety check: PassMIT
Surveysdavepoon/buildwithclaude3.6k—~1.5kAutomated safety check: PassMIT

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Questions about Improve Codebase Architecture

What does Improve Codebase Architecture do?

Survey a codebase for architecture improvements and present a visual report. Improve Codebase Architecture is an agent skill from nodetool-ai/nodetool. Survey a codebase for architecture improvements and present a visual report.

How do I install Improve Codebase Architecture in Claude Code?

Run `npx skills add nodetool-ai/nodetool --skill improve-codebase-architecture -a claude-code`. Or copy the skill folder (.agents/skills/improve-codebase-architecture in nodetool-ai/nodetool) into .claude/skills/improve-codebase-architecture in your project. Claude Code loads it when a task matches its description.

How do I install Improve Codebase Architecture in Codex?

Run `npx skills add nodetool-ai/nodetool --skill improve-codebase-architecture -a codex`. Or copy the skill folder (.agents/skills/improve-codebase-architecture in nodetool-ai/nodetool) into .agents/skills/improve-codebase-architecture in your project. Codex loads it when a task matches its description.

Can I use Improve Codebase Architecture in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nodetool-ai/nodetool --skill improve-codebase-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/improve-codebase-architecture, .gemini/skills/improve-codebase-architecture, .github/skills/improve-codebase-architecture and .opencode/skills/improve-codebase-architecture in your project.

What does Improve Codebase Architecture need to run?

Going by SKILL.md and its folder, Improve Codebase Architecture needs the command-line tools its instructions call (git).

Does Improve Codebase Architecture access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Improve Codebase Architecture safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Improve Codebase Architecture use?

Improve Codebase Architecture is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Improve Codebase Architecture use?

About 1.5k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Improve Codebase Architecture?

Skills that share tags, products or a category with Improve Codebase Architecture: Presentations (asgeirtj/system_prompts_leaks, 69k stars), Presentation (RightNow-AI/openfang, 18k stars), AI Presenter Video (NousResearch/hermes-agent, 252k stars) and Agent Scout Explorer (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Improve Codebase Architecture?

nodetool-ai (a GitHub organization) maintains it in nodetool-ai/nodetool, which has 554 GitHub stars. The repository holds 130 skills in this directory. The repository was last updated on October 7, 2026.

Source: nodetool-ai/nodetool on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.