Tech Decision logo

Tech Decision

Organization
team-attention
tech-decision

This skill should be used when the user asks to "기술 의사결정", "뭐 쓸지 고민", "A vs B", "비교 분석", "라이브러리 선택", "아키텍처 결정", "어떤 걸 써야 할지", "트레이드오프", "기술 선택", "구현 방식 고민", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.

Overview

Publisherteam-attention
Repositoryplugins-for-claude-natives
Skill nametech-decision
Stars
827
Forks
106
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by team-attention on GitHub. Read the source before you install it.

Installation

Install the Tech Decision 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.
    https://github.com/team-attention/plugins-for-claude-natives/tree/main/plugins/dev/skills/tech-decision
  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/team-attention/plugins-for-claude-natives.git /tmp/plugins-for-claude-natives
mkdir -p .claude/skills
cp -r /tmp/plugins-for-claude-natives/plugins/dev/skills/tech-decision .claude/skills/tech-decision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tech Decision 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 Tech Decision 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 Tech Decision 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.

Tech Decision - 기술 의사결정 깊이 탐색

기술적 의사결정을 체계적으로 분석하고 종합적인 결론을 도출하는 스킬.

핵심 원칙

두괄식 결과물: 모든 보고서는 결론을 먼저 제시하고, 그 다음에 근거를 제공한다.

사용 시나리오

  • 라이브러리/프레임워크 선택 (React vs Vue, Prisma vs TypeORM)
  • 아키텍처 패턴 결정 (Monolith vs Microservices, REST vs GraphQL)
  • 구현 방식 선택 (Server-side vs Client-side, Polling vs WebSocket)
  • 기술 스택 결정 (언어, 데이터베이스, 인프라 등)

의사결정 워크플로우

Phase 1: 문제 정의

의사결정 주제와 맥락을 명확히 한다:

  1. 주제 파악: 무엇을 결정해야 하는가?
  2. 옵션 식별: 비교할 선택지들은 무엇인가?
  3. 평가 기준 수립: 어떤 기준으로 평가할 것인가?
    • 성능, 학습 곡선, 생태계, 유지보수성, 비용 등
    • 프로젝트 특성에 맞는 기준 우선순위 설정
    • 상세 기준은 references/evaluation-criteria.md 참조

Phase 2: 병렬 정보 수집

여러 소스에서 동시에 정보를 수집한다. 반드시 병렬로 실행:

┌─────────────────────────────────────────────────────────────┐
│  동시 실행 (Task tool로 병렬 실행)                            │
├─────────────────────────────────────────────────────────────┤
│  1. codebase-explorer agent                                 │
│     → 기존 코드베이스 분석, 현재 패턴/제약사항 파악              │
│                                                             │
│  2. docs-researcher agent                                   │
│     → 공식 문서, 가이드, best practices 리서치                │
│                                                             │
│  3. Skill: dev-scan                                         │
│     → 커뮤니티 의견 수집 (Reddit, HN, Dev.to, Lobsters)       │
│                                                             │
│  4. Skill: agent-council                                    │
│     → 다양한 AI 전문가 관점 수집                              │
│                                                             │
│  5. [선택] Context7 MCP                                     │
│     → 라이브러리별 최신 문서 조회                              │
└─────────────────────────────────────────────────────────────┘

실행 방법:

markdown
# Agents는 Task tool로 병렬 실행
Task codebase-explorer: "분석할 주제와 컨텍스트"
Task docs-researcher: "리서치할 기술/라이브러리"

# 기존 스킬은 Skill tool로 호출
Skill: dev-scan (커뮤니티 의견)
Skill: agent-council (전문가 관점)

Phase 3: 종합 분석

수집된 정보를 바탕으로 tradeoff-analyzer agent를 실행:

  • 각 옵션별 pros/cons 정리
  • 평가 기준별 점수화
  • 충돌하는 의견 정리
  • 신뢰도 평가 (출처 기반)

Phase 4: 최종 보고서 생성

decision-synthesizer agent로 두괄식 종합 보고서 작성 (상세 템플릿: references/report-template.md):

markdown
# 기술 의사결정 보고서: [주제]

## 결론 (Executive Summary)
**추천: [Option X]**
[1-2문장 핵심 이유]

## 평가 기준 및 가중치
| 기준 | 가중치 | 설명 |
|------|--------|------|
| 성능 | 30% | ... |
| 학습곡선 | 20% | ... |

## 옵션별 분석

### Option A: [이름]
**장점:**
- [장점 1] (출처: 공식 문서)
- [장점 2] (출처: Reddit r/webdev)

**단점:**
- [단점 1] (출처: HN 토론)

**적합한 경우:** [시나리오]

### Option B: [이름]
...

## 종합 비교
| 기준 | Option A | Option B | Option C |
|------|----------|----------|----------|
| 성능 | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ |
| 학습곡선 | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ |
| **총점** | **X점** | **Y점** | **Z점** |

## 추천 근거
1. [핵심 근거 1 with 출처]
2. [핵심 근거 2 with 출처]
3. [핵심 근거 3 with 출처]

## 리스크 및 주의사항
- [주의점 1]
- [주의점 2]

## 참고 출처
- [출처 목록]

활용하는 리소스

Agents (이 플러그인)

Agent역할
codebase-explorer기존 코드베이스 분석, 패턴/제약사항 파악
docs-researcher공식 문서, 가이드, best practices 리서치
tradeoff-analyzer옵션별 pros/cons 정리, 비교 분석
decision-synthesizer두괄식 최종 보고서 생성

기존 스킬 (Skill tool로 호출)

Skill용도호출 방법
dev-scanReddit, HN, Dev.to 등 커뮤니티 의견Skill: dev-scan
agent-council다양한 AI 전문가 관점 수집Skill: agent-council

MCP (선택적)

  • Context7: 라이브러리별 최신 공식 문서 조회

빠른 실행 가이드

1. 간단한 비교 (A vs B)

사용자: "React vs Vue 뭐가 나을까?"

실행:
1. Task docs-researcher + Task codebase-explorer (병렬)
2. Skill: dev-scan
3. Task tradeoff-analyzer
4. Task decision-synthesizer

2. 깊은 분석 (복잡한 의사결정)

사용자: "우리 프로젝트에 상태관리 라이브러리 뭘 쓸지 고민이야"

실행:
1. Task codebase-explorer (현재 상태 분석)
2. 병렬 실행:
   - Task docs-researcher (Redux, Zustand, Jotai, Recoil 등)
   - Skill: dev-scan
   - Skill: agent-council
3. Task tradeoff-analyzer
4. Task decision-synthesizer

3. 아키텍처 결정

사용자: "모놀리스 vs 마이크로서비스 어떻게 해야 할까?"

실행:
1. Task codebase-explorer (현재 규모/복잡도 분석)
2. 병렬 실행:
   - Task docs-researcher (각 아키텍처 best practices)
   - Skill: agent-council (아키텍트 관점)
3. Task tradeoff-analyzer (팀 규모, 배포 복잡도 등 고려)
4. Task decision-synthesizer

주의사항

  1. 컨텍스트 제공: 프로젝트 특성, 팀 규모, 기존 기술 스택 등 맥락 정보가 많을수록 정확한 분석 가능
  2. 평가 기준 확인: 사용자에게 중요한 기준이 무엇인지 먼저 확인
  3. 신뢰도 표시: 출처가 불분명하거나 오래된 정보는 명시
  4. 결론 먼저: 항상 두괄식으로 결론부터 제시

추가 리소스

참고 파일

  • references/report-template.md - 상세 보고서 템플릿
  • references/evaluation-criteria.md - 평가 기준 가이드

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tech Decision AI skill do?

This skill should be used when the user asks to "기술 의사결정", "뭐 쓸지 고민", "A vs B", "비교 분석", "라이브러리 선택", "아키텍처 결정", "어떤 걸 써야 할지", "트레이드오프", "기술 선택", "구현 방식 고민", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.

Why use Tech Decision on TypingMind?

Because you install it once and use it with any model. Tech Decision 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 Tech Decision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/team-attention/plugins-for-claude-natives/tree/main/plugins/dev/skills/tech-decision. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tech Decision?

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 Tech Decision?

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

Is the Tech Decision AI skill free?

Yes. It is published on GitHub by team-attention under the MIT license. 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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