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Agent Skill · 研究检索

compound-engineering(复合工程)

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。

compound-engineering 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 46,073。

总安装 808周安装 33GitHub Stars 公开资料未说明下载 261核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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/snarktank/amp-skills
仓库路径:skills/compound-engineering
安装命令:
npx skills add https://github.com/snarktank/amp-skills --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/snarktank/amp-skills --skill compound-engineering

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

compound-engineering 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。通过 npx skills add 命令从指定仓库安装并使用该技能。安装前建议确认权限范围和维护状态,避免触发联网或文件读写操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/compound-engineering

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/snarktank/amp-skills --skill compound-engineer…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

compound-engineering 怎么安装?

在终端执行 npx skills add https://github.com/snarktank/amp-skills --skill compound-engineering。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

compound-engineering 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

compound-engineering 需要什么权限?

权限级别是只读。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。

compound-engineering 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

compound-engineering 用的人多吗?

公开数据是累计安装 808 次,近一周安装 33 次,下载 261 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

compound-engineering 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
compound-engineering

复合工程 · 本页

808—只读MIT未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

平台分布

Claude Code

29.3%
按下载量换算76

Cursor

24.23%
按下载量换算63

Gemini CLI

16.64%
按下载量换算43

Codex

12.98%
按下载量换算34

Antigravity

7.4%
按下载量换算19

OpenCode

3.36%
按下载量换算9

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

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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