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SPEC 기반 개발 인터뷰 — 심층 질문을 통해 상세한 SPEC.md 명세서를 생성합니다. Triggers on: 스펙, spec, 명세서, 인터뷰, 기능 설계. NOT for: 바로 구현, 간단한 수정, 코드 작성.

Overview

Publisherjh941213
Repositorymy-cc-harness
Skill namespec
Stars
125
Forks
35
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jh941213 on GitHub. Read the source before you install it.

Installation

Install the Spec AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jh941213/my-cc-harness.git /tmp/my-cc-harness
mkdir -p .claude/skills
cp -r /tmp/my-cc-harness/skills/spec .claude/skills/spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Spec on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Spec is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SPEC 기반 개발 - 인터뷰

Anthropic 엔지니어 Thariq의 SPEC 기반 개발 워크플로우입니다.

트리거

  • "스펙", "spec", "명세서", "인터뷰", "기능 설계"

워크플로우

1단계: SPEC.md 확인 또는 생성

현재 디렉토리에 SPEC.md가 있는지 확인합니다.

  • 있으면: 해당 파일을 읽고 인터뷰 시작
  • 없으면: 사용자에게 기본 아이디어를 물어본 후 초안 생성

2단계: 심층 인터뷰

반드시 AskUserQuestion 도구를 사용하여 다음 영역에 대해 질문합니다:

  1. 기술적 구현

    • 어떤 기술 스택을 사용할지
    • 기존 코드베이스와의 통합 방법
    • 성능 요구사항
  2. UI & UX

    • 사용자 흐름
    • 디자인 패턴
    • 접근성 요구사항
  3. 우려 사항

    • 보안 고려사항
    • 확장성 문제
    • 유지보수 복잡도
  4. 트레이드오프

    • 시간 vs 품질
    • 단순함 vs 기능성
    • 유연성 vs 성능

3단계: 질문 규칙

  • 뻔한 질문 금지: "어떤 언어 쓸까요?" 같은 명확한 건 스킵
  • 깊이 있게: 40개 이상 질문도 가능
  • 연속 인터뷰: 완료될 때까지 계속 질문
  • 한 번에 1-2개 질문: 사용자가 답변하기 쉽게

4단계: 명세서 작성

인터뷰 완료 후 SPEC.md 파일을 상세하게 업데이트합니다:

markdown
# [기능명] 명세서

## 개요
[한 문장 설명]

## 목표
- [ ] 목표 1
- [ ] 목표 2

## 기술 스택
- ...

## 상세 요구사항
### 기능적 요구사항
1. ...

### 비기능적 요구사항
1. ...

## UI/UX 명세
- ...

## API 설계 (해당 시)
- ...

## 데이터 모델 (해당 시)
- ...

## 보안 고려사항
- ...

## 테스트 계획
- ...

## 마일스톤
1. [ ] 단계 1
2. [ ] 단계 2

## 열린 질문 / 결정 필요
- ...

5단계: 안내

명세서 작성 후 사용자에게 안내:

"명세서가 완성되었습니다. 새 세션에서 다음 명령어로 구현을 시작하세요:"

SPEC.md 읽고 구현 시작해줘

구현 완료 후 검증:

/spec-verify

핵심 원칙

  1. 컨텍스트 분리: 인터뷰 세션 ≠ 구현 세션
  2. 사용자 컨트롤: 질문을 통해 사용자가 방향을 결정
  3. 상세한 문서화: 다음 세션에서 바로 실행 가능한 수준

Frequently asked questions

What does the Spec AI skill do?

SPEC 기반 개발 인터뷰 — 심층 질문을 통해 상세한 SPEC.md 명세서를 생성합니다. Triggers on: 스펙, spec, 명세서, 인터뷰, 기능 설계. NOT for: 바로 구현, 간단한 수정, 코드 작성.

Why use Spec on TypingMind?

Because you install it once and use it with any model. Spec is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Spec in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jh941213/my-cc-harness/tree/main/skills/spec. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Spec?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Spec?

As many as you like. As long as a model supports skills, you can use Spec with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Spec AI skill free?

It is published on GitHub by jh941213. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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