improve-codebase-architecture

improve-codebase-architecture is a skill for Claude Code, Codex from jamesyang124/agent-skills. It costs 68 tokens per session (1,467 once invoked), scanned A, a copy of improve-codebase-architecture, MIT.

A codebase review method that finds ways to group behavior behind simpler interfaces and align the structure with the project’s domain language and recorded decisions.

In plain words
What is it for?
Use it to find refactoring opportunities, improve testability, and make a codebase easier for both developers and coding agents to navigate.
Why use it?
It exposes tightly connected or difficult-to-test code and suggests changes that make future modifications easier to locate and understand.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to find refactoring opportunities, improve testability, and make a codebase easier for both developers and coding agents to navigate.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jamesyang124/agent-skills/improve-codebase-architecture
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add jamesyang124/agent-skills --skill improve-codebase-architecture
Clone the repo
git clone --depth 1 https://github.com/jamesyang124/agent-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for improve-codebase-architecture

README.md
[![agentmods](https://agentmods.dev/badge/skills/jamesyang124/agent-skills/improve-codebase-architecture/github.svg)](https://agentmods.dev/skills/jamesyang124/agent-skills/improve-codebase-architecture)
Your own site
<a href="https://agentmods.dev/skills/jamesyang124/agent-skills/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/jamesyang124/agent-skills/improve-codebase-architecture/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for improve-codebase-architecture

Your own site · 80×15
<a href="https://agentmods.dev/skills/jamesyang124/agent-skills/improve-codebase-architecture"><img src="https://agentmods.dev/badge/skills/jamesyang124/agent-skills/improve-codebase-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,467 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 77% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.01467
Opus 5.5 $0.00027 $0.00587
Sonnet 5.5 $0.00014 $0.00293
Haiku 4.5 $0.00007 $0.00147

Measured 1mo ago against content hash f00de04c7bb8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

improve-codebase-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

77% identical to improve-codebase-architecture — 72 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

external/skills/improve-codebase-architecture/SKILL.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Glossary

Use these terms exactly in every suggestion. Consistent language is the point — don't drift into "component," "service," "API," or "boundary." Full definitions in LANGUAGE.md.

  • Module — anything with an interface and an implementation (function, class, package, slice).
  • Interface — everything a caller must know to use the module: types, invariants, error modes, ordering, config. Not just the type signature.
  • Implementation — the code inside.
  • Depth — leverage at the interface: a lot of behaviour behind a small interface. Deep = high leverage. Shallow = interface nearly as complex as the implementation.
  • Seam — where an interface lives; a place behaviour can be altered without editing in place. (Use this, not "boundary.")
  • Adapter — a concrete thing satisfying an interface at a seam.
  • Leverage — what callers get from depth.
  • Locality — what maintainers get from depth: change, bugs, knowledge concentrated in one place.

Key principles (see LANGUAGE.md for the full list):

  • Deletion test: imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
  • The interface is the test surface.
  • One adapter = hypothetical seam. Two adapters = real seam.

This skill is informed by the project's domain model. The domain language gives names to good seams; ADRs record decisions the skill should not re-litigate.

Process

1. Explore

Read the project's domain glossary and any ADRs in the area you're touching first.

Then use the Agent tool with subagent_type=Explore 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 — 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?

Read the full file on GitHub · 82 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 82 lines · 68 tokens per session scan A f00de04c7bb8

Subscribe to this mod's changes

improve-codebase-architecture is a skill published in the GitHub repository jamesyang124/agent-skills (2 stars, last pushed 7d ago), licensed MIT. It adds 68 tokens to every session and 1,467 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 77% identical to improve-codebase-architecture, differing in 72 lines, and is treated as a copy.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

component-fixtures

Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.

microsoft/vscode · 53 tokens