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eb-improve-codebase-architectureeb 改进代码库架构

Agent Skill

eb-improve-codebase-architecture 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

2,399

周安装

102

GitHub Stars

公开资料未说明

下载量

840
OpenClaw

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

MIT-0

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:eb-improve-codebase-architecture(eb 改进代码库架构)
来源仓库:https://github.com/emersonbraun/eb-improve-codebase-architecture
安装命令:
openclaw skills install eb-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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install eb-improve-codebase-architecture

简介

eb-improve-codebase-architecture 用于识别代码库架构改进机会。

  • 适合在 OpenClaw 中探索模块解耦、可测试性提升等优化方向。
  • 重点分析浅层模块以增强整体结构清晰度。
  • 安装命令为 openclaw skills install eb-improve-codebase-architecture,需确认代码读取权限。
  • 不建议现场实施重构,应记录为后续 issue 待规划处理。

SKILL.md

name
improve-codebase-architecture
description
Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.
metadata
author
EmersonBraun
version
1.0.0

Improve Codebase Architecture

Explore a codebase like an AI would, surface architectural friction, discover opportunities for improving testability, and propose module-deepening refactors as GitHub issue RFCs.

A deep module (John Ousterhout, "A Philosophy of Software Design") has a small interface hiding a large implementation. Deep modules are more testable, more AI-navigable, and let you test at the boundary instead of inside.

Process

1. Explore the codebase

Use the Agent tool with subagent_type=Explore to navigate the codebase naturally. Do NOT follow rigid heuristics — explore organically and note where you experience friction:

  • Where does understanding one concept require bouncing between many small files?
  • Where are modules so shallow that the interface is 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?
  • Where do tightly-coupled modules create integration risk in the seams between them?
  • Which parts of the codebase are untested, or hard to test?

The friction you encounter IS the signal.

2. Present candidates

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

  • Cluster: Which modules/concepts are involved
  • Why they're coupled: Shared types, call patterns, co-ownership of a concept
  • Dependency category: See REFERENCE.md for the four categories
  • Test impact: What existing tests would be replaced by boundary tests

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

3. User picks a candidate

4. Frame the problem space

Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate:

  • The constraints any new interface would need to satisfy
  • The dependencies it would need to rely on
  • A rough illustrative code sketch to make the constraints concrete — this is not a proposal, just a way to ground the constraints

Show this to the user, then immediately proceed to Step 5. The user reads and thinks about the problem while the sub-agents work in parallel.

5. Design multiple interfaces

Spawn 3+ sub-agents in parallel using the Agent tool. Each must produce a radically different interface for the deepened module.

Prompt each sub-agent with a separate technical brief (file paths, coupling details, dependency category, what's being hidden). This brief is independent of the user-facing explanation in Step 4. Give each agent a different design constraint:

  • Agent 1: "Minimize the interface — aim for 1-3 entry points max"
  • Agent 2: "Maximize flexibility — support many use cases and extension"
  • Agent 3: "Optimize for the most common caller — make the default case trivial"
  • Agent 4 (if applicable): "Design around the ports & adapters pattern for cross-boundary dependencies"

Each sub-agent outputs:

  1. Interface signature (types, methods, params)
  2. Usage example showing how callers use it
  3. What complexity it hides internally
  4. Dependency strategy (how deps are handled — see REFERENCE.md)
  5. Trade-offs

Present designs sequentially, then compare them in prose.

After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated — the user wants a strong read, not just a menu.

6. User picks an interface (or accepts recommendation)

7. Create GitHub issue

Create a refactor RFC as a GitHub issue using gh issue create. Use the template in REFERENCE.md. Do NOT ask the user to review before creating — just create it and share the URL.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

80.31%
按下载量换算675

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install eb-improve-codebase-architecture 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

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