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compound-engineering复合工程

Agent Skill

compound-engineering 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

912

周安装

49

GitHub Stars

228

下载量

384
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

3

许可证

MIT

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:compound-engineering(复合工程)
来源仓库:https://github.com/rohunj/claude-build-workflow
仓库路径:skills/compound-engineering
安装命令:
npx skills add https://github.com/rohunj/claude-build-workflow --skill compound-engineering
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/rohunj/claude-build-workflow --skill compound-engineering

简介

实施复合工程方法论,使每项工作都为后续开发创造便利而非增加负担。

  • 通过记录每次问题的解决洞察,形成可复用的学习循环降低技术债务。
  • 核心是建立“问题→解决→文档化→未来受益”的正向积累机制。
  • 适用于希望系统性减少重复错误、提升团队效率的研发流程优化。
  • 安装前建议确认项目是否允许持续写入学习记录文件。

SKILL.md

This skill implements Compound Engineering—a development methodology where each unit of work makes subsequent work easier, not harder. Inspired by Every.to's engineering approach.

Core Philosophy

Each unit of engineering work should make subsequent units of work easier—not harder.

Traditional development accumulates technical debt. Every feature adds complexity. Every change increases maintenance burden. Compound engineering inverts this by creating a learning loop where each bug, failed test, or problem-solving insight gets documented and used by future work.

The Compound Engineering Loop

Plan → Work → Review → Compound → (repeat)
  1. Plan (40%): Research approaches, synthesize information into detailed implementation plans
  2. Work (20%): Execute the plan systematically with continuous validation
  3. Review (20%): Evaluate output quality and identify learnings
  4. Compound (20%): Feed results back into the system to make the next loop better

80% of compound engineering is in planning and review. 20% is in execution.

Step 1: Plan

Before writing any code, create a comprehensive plan. Good plans start with research:

Research Phase

  1. Codebase Analysis: Search for similar patterns, conventions, and prior art in the codebase
  2. Commit History: Use git log to understand how related features were built
  3. Documentation: Check README, AGENTS.md, and inline documentation
  4. External Research: Search for best practices relevant to the problem

Plan Document Structure

Create a plan document (markdown) with:

# Feature: [Name]

## Context
- What problem does this solve?
- Who is affected?
- What's the current behavior vs desired behavior?

## Research Findings
- Similar patterns found in codebase: [list with file links]
- Relevant prior implementations: [commit references]
- Best practices discovered: [external references]

## Acceptance Criteria
- [ ] Criterion 1 (testable)
- [ ] Criterion 2 (testable)
- [ ] Criterion 3 (testable)

## Technical Approach
1. Step 1: [specific action]
2. Step 2: [specific action]
3. Step 3: [specific action]

## Code Examples
[Include code snippets that follow existing patterns]

## Testing Strategy
- Unit tests: [what to test]
- Integration tests: [what to test]
- Manual verification: [steps]

## Risks & Mitigations
- Risk 1: [mitigation]
- Risk 2: [mitigation]

Detail Levels

  • Minimal: Quick issues for simple features (1-2 hours work)
  • Standard: Issues with technical considerations (1-2 days work)
  • Comprehensive: Major features requiring architecture decisions (multi-day work)

Step 2: Work

Execute the plan systematically:

Execution Workflow

  1. Create isolated environment: Use feature branch or git worktree
  2. Break down into tasks: Create TODO list from plan
  3. Execute systematically: One task at a time
  4. Validate continuously: Run tests after each change
  5. Commit incrementally: Small, focused commits with clear messages

Working Principles

  • Follow existing patterns discovered in research
  • Run tests after every meaningful change
  • If something fails, understand why before proceeding
  • Keep changes focused—don't scope creep

Quality Checks During Work

# After each change, verify:
npm run typecheck  # or equivalent
npm test           # run affected tests
npm run lint       # check code quality

Step 3: Review

Before merging, perform comprehensive review:

Review Checklist

Code Quality

  • Follows existing codebase patterns and conventions
  • No unnecessary complexity—prefer duplication over wrong abstraction
  • Clear naming that matches project conventions
  • No debug code or console.logs left behind

Security

  • No secrets or sensitive data exposed
  • Input validation where needed
  • Safe handling of user data

Performance

  • No obvious performance regressions
  • Database queries are efficient (no N+1)
  • Appropriate caching if applicable

Testing

  • Tests cover acceptance criteria
  • Edge cases considered
  • Tests are maintainable, not brittle

Architecture

  • Change is consistent with system design
  • No unnecessary coupling introduced
  • Follows separation of concerns

Multi-Perspective Review

Consider the code from different angles:

  • Maintainer perspective: Will this be easy to modify in 6 months?
  • Performance perspective: Any bottlenecks?
  • Security perspective: Any vulnerabilities?
  • Simplicity perspective: Can this be simpler?

Step 4: Compound

This is where the magic happens—capture learnings to make future work easier:

What to Compound

Patterns: Document new patterns discovered or created

## Pattern: [Name]
When to use: [context]
Implementation: [example code]
See: [file reference]

Decisions: Record why certain approaches were chosen

## Decision: [Choice Made]
Context: [situation]
Options considered: [alternatives]
Rationale: [why this choice]
Consequences: [trade-offs]

Failures: Turn every bug into a lesson

## Lesson: [What Went Wrong]
Symptom: [what was observed]
Root cause: [actual problem]
Fix: [solution]
Prevention: [how to avoid in future]

Where to Codify Learnings

  1. AGENTS.md: Project-wide guidance that applies everywhere
  2. Subdirectory AGENTS.md: Specific guidance for subsystems
  3. Inline comments: Only when the code isn't self-explanatory
  4. Test cases: Turn bugs into regression tests

Compounding in Practice

After completing work, ask:

  • What did I learn that others should know?
  • What mistake did I make that can be prevented?
  • What pattern did I discover or create?
  • What decision was made and why?

Document these in the appropriate location so future agents (and humans) benefit.

Practical Commands

Planning a Feature

Plan implementation for: [describe feature]
- Research the codebase for similar patterns
- Check git history for related changes
- Create a detailed plan with acceptance criteria
- Include code examples that match existing patterns

Executing Work

Execute this plan: [plan reference]
- Create feature branch
- Break into TODO list
- Work through systematically
- Run tests after each change
- Create PR when complete

Reviewing Code

Review this change: [PR/diff reference]
- Check for code quality issues
- Look for security concerns
- Evaluate performance implications
- Verify test coverage
- Suggest improvements

Compounding Learnings

Compound learnings from: [work just completed]
- What patterns were used or created?
- What decisions were made and why?
- What failures occurred and how to prevent them?
- Update AGENTS.md with relevant guidance

Key Principles

  1. Prefer duplication over wrong abstraction: Simple, clear code beats complex abstractions
  2. Document as you go: Every command generates documentation that makes future work easier
  3. Quality compounds: High-quality code is easier to modify
  4. Systematic beats heroic: Consistent processes beat individual heroics
  5. Knowledge should be codified: Learnings should be captured and reused

Success Metrics

You're doing compound engineering well when:

  • Each feature takes less effort than the last similar feature
  • Bugs become one-time events (documented and prevented)
  • New team members can be productive quickly (institutional knowledge is accessible)
  • Code reviews surface fewer issues (patterns are established and followed)
  • Technical debt decreases over time (learnings compound)

Remember: You're not just building features—you're building a development system that gets better with each use.

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

04

需要参考平台分布和安装热度时

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

Claude Code

29.93%
按下载量换算115

OpenCode

20.26%
按下载量换算78

Codex

15.89%
按下载量换算61

Antigravity

11.64%
按下载量换算45

Gemini CLI

8.3%
按下载量换算32

windsurf

3.37%
按下载量换算13

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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来源信息

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