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improve-codebase-architecture改进代码库架构

Agent Skill

improve-codebase-architecture 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

212

周安装

9

GitHub Stars

公开资料未说明

下载量

74
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:improve-codebase-architecture(改进代码库架构)
来源仓库:https://github.com/danielvm-git/skills
仓库路径:skills/improve-codebase-architecture
安装命令:
npx skills add https://github.com/danielvm-git/skills --skill improve-codebase-architecture
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/danielvm-git/skills --skill improve-codebase-architecture

简介

improve-codebase-architecture 用于改进代码库架构, 识别模块间摩擦点并提出深化机会。improve-codebase-architecture 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

  • 目标是提升测试性和 AI 可导航性, 使用模块、接口、实现等术语保持语言一致性。
  • 使用时需严格遵循术语定义,避免漂移到其他概念,专注于接口清晰度和模块深度。

SKILL.md

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 — CONTEXT.md and any docs/adr/. The domain language gives names to good seams; ADRs record decisions the skill should not re-litigate. See CONTEXT-FORMAT.md and ADR-FORMAT.md.

Process

1. Explore

Read existing documentation first:

  • CONTEXT.md (or CONTEXT-MAP.md + each CONTEXT.md in a multi-context repo)
  • Relevant ADRs in docs/adr/ (and any context-scoped docs/adr/ directories)

If any of these files don't exist, proceed silently — don't flag their absence or suggest creating them upfront.

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?

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

Present a numbered list of deepening opportunities. For each candidate:

  • 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 also in how tests would improve

Use CONTEXT.md vocabulary for the domain, and LANGUAGE.md 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 (e.g. *"contradicts ADR-0007 — but worth reopening because…"*). Don't list every theoretical refactor an ADR forbids.

Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?"

3. Grilling loop

Once the user picks a candidate, drop into a grilling conversation. Walk the design 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:

  • Naming a deepened module after a concept not in CONTEXT.md? Add the term to CONTEXT.md — same discipline as /domain-model (see CONTEXT-FORMAT.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 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. See ADR-FORMAT.md.
  • Want to explore alternative interfaces for the deepened module? See INTERFACE-DESIGN.md.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.1%
按下载量换算24

Claude

30.16%
按下载量换算22

Cursor

20.33%
按下载量换算15

Gemini CLI

8.39%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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