중국어, README-zh.md를 참조하십시오.

오픈 소스 프로젝트에서 재사용 가능한 기술로 엔지니어링 판단을 켭니다.

oss-skill] 연구 강한 오픈 소스 저자, 저장소, 및 유지 관리 팀은 엔지니어링 결정을 내리고, 그 패턴을 Claude Code 및 Codex와 같은 코딩 에이전트에 재사용 가능한 기술로 전환합니다.

무엇 그것

• 코드, 커밋, PR, 문제, 릴리즈 노트 및 디자인 문서 읽기
• 프로젝트 또는 유지보수자가 결정하는 방법을 추출합니다.
• 코딩, 검토, 복원, API 디자인, 디버깅 및 유지 보수 결정에 대한 재사용 가능한 기술을 생성

산출은 보통 이 같이 보입니다:

설치

그런 다음 에이전트에서 사용:

실행 후, skills/ -playbook는 현재 프로젝트에 생성됩니다. 에이전트의 기술 디렉토리에 폴더를 복사, 예를 들어 ~/.codex/skills/.

사용할 때

좋은 적합:

• 저자, 저장소, 또는 유지자 팀을 증류
• 프로젝트별 표준과 패치 검토
• 성숙한 오픈 소스 프로젝트의 학습 엔지니어링 습관
• 재사용 가능한 팀 기술로 Turning maintainer 판단

좋은 적합:

• 성격 모방
• Non-software 주제
• 명확한 기술설계 목표를 가진 요구

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1. 명세.

oss-skill

For Chinese, see README-zh.md.

Turn engineering judgment from open-source projects into reusable skills.

oss-skill studies how strong open-source authors, repositories, and maintainer teams make engineering decisions, then turns those patterns into reusable skills for coding agents like Claude Code and Codex.

What it does

• Reads code, commits, PRs, issues, release notes, and design docs
• Extracts how a project or maintainer makes decisions
• Generates reusable skills for coding, review, refactoring, API design, debugging, and maintenance decisions

The output usually looks like this:

skills/-playbook/
├─ SKILL.md
├─ scripts/
└─ references/
   └─ research/

Install

npx skills add lianchi/oss-skill

Then use it in your agent:

> Distill the Vite project
> Distill the vercel-labs team
> Review this patch by React maintainer standards

After a run, skills/-playbook will be generated in your current project. Copy that folder into your agent’s skills directory, for example ~/.codex/skills/.

When to use it

Good fits:

• Distilling an author, repository, or maintainer team
• Reviewing a patch with a project-specific standard
• Learning engineering habits from mature open-source projects
• Turning maintainer judgment into a reusable team skill

Not a good fit:

• Personality imitation
• Non-software topics
• Requests with no clear engineering goal

How it works

1. Pick a target, or start from a problem and work backward to the right target.
2. Research it with a code-first approach.
3. Organize the evidence across 6 dimensions.
4. Turn the result into an executable skill.

The 6 research dimensions are:

1. Architecture
2. Commits and PRs
3. Issues and reviews
4. Docs, talks, and public writing
5. Key decision cases
6. Evolution and lineage

The final skill should help with real development work, such as:

• feature implementation
• bug fixing
• patch review
• refactoring
• API design
• performance optimization

Why this exists

AI is getting very good at writing code, and general coding agents are already good at producing complete solutions.

What oss-skill tries to strengthen is another layer: turning the engineering judgment repeatedly proven in strong open-source authors and mature repositories into skills that can still be reused today.

Inspired by nuwa-skill

oss-skill is clearly inspired by alchaincyf/nuwa-skill in its overall approach, structural organization, and implementation, and it also directly reuses some of its practices, such as parts of the scripting approach.

It deserves huge thanks and respect. nuwa-skill turned “distillation” into a practical workflow, and helped many people realize that public materials, engineering traces, and structured prompts can be organized into reusable capabilities.

The focus is different: