Code Engineering

by kangarooking

Code Engineering is an AI Engineering skill for Claude Code, published by kangarooking in system-prompt-skills.

182 stars40 forkson kangarooking/system-prompt-skillsAdded 2026/09/06+3% in starsRepository updated 2026/05/04

View source on GitHub Download repo ZIP

agent-designagent-skillsai-agentscangjie-skillcontext-managementprompt-engineeringprompt-injectionsystem-prompttool-use

Install in seconds

Install Code Engineering

Copy Code Engineering into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/kangarooking/system-prompt-skills/tree/main/code-engineering ~/.claude/skills/code-engineering

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/kangarooking/system-prompt-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: code-engineering/SKILL.md in kangarooking/system-prompt-skills
Installs to: ~/.claude/skills/code-engineering
Collection: One of 15 skills cataloged from this repository
Category: AI Engineering — 3278 skills

What Code Engineering does

Code Engineering defines system-prompt rules for coding agents that edit files, manage Git workflows, and verify changes. Use it for autonomous or guided code modification tasks, not for pure code Q&A or infrastructure setup.

Code Engineering is cataloged under AI Engineering on DirSkills. Code Engineering comes from a repository tagged agent-design, agent-skills, ai-agents, cangjie-skill and context-management.

Documentation

README

Excerpt

编程代理模式

R — 原文 (Reading)

Claude Code 实现文件记忆、Agent 子任务委派、并行工具调用、Git 安全协议和"量两次切一次"原则；Codex 具备脏工作树感知、Plan 工具带跳过规则、代码审查按严重度排序；Jules 定义 plan→review→execute 生命周期，用 Playwright 验证前端；Claude Design 固定 React+Babel 版本并制定反 AI 糟粕指南。核心模式：安全优先 Git 工作流、计划生命周期管理、上下文感知编辑、验证循环、自主与引导模式。

I — 方法论骨架 (Interpretation)

安全优先 Git 工作流：任何破坏性操作（force push、hard reset、clean）需显式用户确认，优先创建新提交而非修改已有提交，保护主分支。
计划生命周期管理：采用 plan→review→execute 三阶段模型——先理解意图生成计划，用户确认后再执行，执行后验证结果。
上下文感知文件编辑：编辑前先读取文件内容，理解上下文后再修改，避免破坏性覆盖；优先使用差异编辑而非全文重写。
验证循环：代码修改后运行测试或构建验证，前端变更使用浏览器工具截图确认视觉效果。
自主与引导模式切换：简单任务可自主完成（YOLO 模式），复杂任务需逐步确认，根据任务性质自动选择模式。
反 AI 糟粕规则：禁止生成典型的 AI 风格代码（过度注释、不必要的抽象、冗余类型声明），追求简洁专业的工程代码。

A1 — 案例分析 (Past Application)

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Code Engineering

What else does kangarooking publish alongside Code Engineering?
Code Engineering is one of 15 skills that DirSkills catalogs from kangarooking/system-prompt-skills, the repository it ships in. Its siblings there include Agent Delegation, Citation System and Context Management. Each one is a separate skill with its own page in this directory, installs the same way Code Engineering does, and is maintained by kangarooking in that same repository. The rest of the collection is listed on the kangarooking/system-prompt-skills page.
How does Code Engineering compare to other AI Engineering skills?
Code Engineering ranks #3045 by stars among the 3278 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Code Engineering against them. Open each page to compare what they document and how they install.