AgentSkillsCN

workflow-validate

通过原型或测试验证技术可行性。当您在正式投入实施之前,对技术方案尚存疑虑时,此技能将助您事半功倍。

SKILL.md
--- frontmatter
name: workflow-validate
description: Validate technical feasibility with prototypes or tests. Use when uncertain about technical approach before committing to implementation.
disable-model-invocation: true

Technical Validation Command

User Input (Optional)

text
$ARGUMENTS

Optional input:

  • Empty: Auto-determine validation method based on analysis.md
  • Provided: Must consider user instructions (e.g., specific validation method, additional requirements)

Outline

  1. Check Prerequisites:

    • Verify docs/work/WORK-{name}/analysis.md exists
    • If not, ERROR: "Run /workflow-analyze first"
  2. Load Analysis Document:

    • Extract selected approach from analysis.md
    • Identify key technical assumptions and risks
  3. Determine Validation Method:

    • Choose appropriate validation approach based on context:
      • Prototype implementation
      • UI/UX verification (Playwright MCP)
      • TDD approach
      • Library/API exploration
      • Technical documentation research
      • User verification delegation
  4. Execute Validation:

    • Perform selected validation method
    • Create prototype code in __prototype__/{feature-name}/ if needed
    • Collect results and evidence
  5. Assess Results:

    • Determine status: ✅ Success / ⚠️ Partial Success / ❌ Failure
    • Document findings and constraints
  6. Write Documents (Dual Language):

    • Create docs/work/WORK-{name}/validation.ko.md (Korean - for user reference)
    • Create docs/work/WORK-{name}/validation.md (English - for agent consumption)
    • Include clear next steps recommendation

Key Rules

📝 Documentation Language

CRITICAL: You must generate TWO versions of all documents:

  1. Korean version (validation.ko.md): For user reference - written in Korean
  2. English version (validation.md): For agent consumption - written in English

Both versions must contain identical structure and information, only the language differs.

🎯 Validation Principles

  1. Focus on Core Risks: Validate only critical technical uncertainties
  2. Practical Approach: Sufficient confidence over perfect validation
  3. Clear Judgement: Explicit success/failure with next steps

✅ Must Do

  • Validate only what's uncertain
  • Create minimal working code (if prototype)
  • Document all findings clearly
  • Provide actionable next steps
  • Reference analysis.md for context
  • Store prototype in __prototype__/ directory

❌ Must Not Do

  • Full implementation (save for execute phase)
  • Validate obvious/known facts
  • Repeat analysis.md content
  • Give ambiguous conclusions ("maybe works")
  • Over-engineer the validation

🔬 Validation Methods Selection

Choose based on uncertainty type:

Uncertainty TypeValidation MethodOutput
Core logic feasibilityPrototype implementationWorking code + results
UI/UX changesPlaywright verificationScreenshots + test results
Complex algorithmsTDD approachTest code + edge cases
External dependenciesLibrary explorationSample code + compatibility
Standards/patternsDocumentation researchSummary + recommendations
Environment-specificUser delegationTest guide + checklist

📁 Prototype Code Management

Location: __prototype__/{feature-name}/

How to create:

  • Validate directly in actual codebase (modifying files is OK)
  • After validation, extract core logic to __prototype__/
  • Don't commit actual code changes (revert or leave as-is)

Purpose:

  • Reference for plan/execute phases
  • Archive of validated core logic
  • Proof of technical feasibility

Lifecycle:

  • Created during validate
  • Referenced in execute
  • User manages cleanup

Document Template

Files to create:

  • docs/work/WORK-{task-name}/validation.ko.md (Korean version)
  • docs/work/WORK-{task-name}/validation.md (English version)

Template A: AI-Completed Validation

markdown
# 기술 검증 보고서

## 🎯 검증 목표

**핵심 질문**: [검증하려는 기술적 불확실성]
**검증 범위**: [검증 대상 및 제외 사항]

---

## 🔬 검증 방법

**선택한 방법**: [프로토타입/테스트/조사 등]
**검증 환경**: [사용한 도구 및 설정]

---

## 📊 검증 결과

**상태**: ✅ 성공 / ⚠️ 부분 성공 / ❌ 실패

**핵심 발견사항**:

- [주요 발견 1]
- [주요 발견 2]

---

## 💡 발견된 사항

### 긍정적 요소

- [예상대로 작동하는 부분]
- [추가로 발견한 장점]

### 제약사항/리스크

- [기술적 제약]
- [성능 이슈]
- [호환성 문제]

---

## 🛠️ 프로토타입 코드 (if created)

**위치**: `__prototype__/{feature-name}/`

**핵심 구현**:

- [구현한 핵심 로직 설명]
- [테스트 방법 및 결과]

**참고사항**:

- [execute 단계에서 주의할 점]
- [개선 가능한 부분]

---

## ✅ 권장사항

**다음 단계**: `/workflow-plan` 진행 / `/workflow-analyze` 재검토

**구현시 주의사항**:

- [주의사항 1]
- [주의사항 2]

**대안 (필요시)**:

- [대안 1]: [장단점]
- [대안 2]: [장단점]

---

## 📚 참고자료

- [관련 문서 링크]
- [참고한 라이브러리 문서]

Template B: User Verification Request

markdown
# 기술 검증 가이드

## 🎯 검증 배경

**AI가 직접 검증할 수 없는 이유**: [환경/접근/도메인 제약]
**사용자 검증이 필요한 부분**: [구체적 검증 대상]

---

## ✅ 검증 체크리스트

### 필수 확인 사항

- [ ] [확인 항목 1]: [확인 방법]
- [ ] [확인 항목 2]: [확인 방법]
- [ ] [확인 항목 3]: [확인 방법]

---

## 🧪 테스트 시나리오

### 시나리오 1: [제목]

**목적**: [무엇을 확인하려는지]

**단계**:

1. [구체적 실행 단계]
2. [구체적 실행 단계]
3. [구체적 실행 단계]

**예상 결과**: [정상 동작시 기대 결과]

**확인 포인트**:

- [무엇을 봐야 하는지]
- [어떤 값을 확인해야 하는지]

### 시나리오 2: [제목]

[시나리오 1과 동일한 구조]

---

## ❓ 확인 질문

1. **[구체적 질문]**
   - 배경: [왜 이것이 중요한지]
   - 판단 기준: [어떻게 판단할지]

2. **[구체적 질문]**
   - 배경: [왜 이것이 중요한지]
   - 판단 기준: [어떻게 판단할지]

---

## 📋 검증 완료 후

**결과 공유 방법**:

- 각 체크리스트 항목별 통과/실패 표시
- 발견된 문제나 제약사항 설명
- 스크린샷이나 로그 첨부 (필요시)

**다음 단계**:

- 검증 성공시: `/workflow-plan` 진행
- 문제 발견시: 대안 논의 필요

📊 Validation Status Guidelines

✅ Success Criteria

  • All core technical assumptions validated
  • No blocking issues found
  • Clear path to implementation
  • Next: Proceed to /workflow-plan

⚠️ Partial Success Criteria

  • Main approach works with constraints
  • Workarounds or alternatives available
  • Trade-offs acceptable
  • Next: Document constraints, get user confirmation

❌ Failure Criteria

  • Core approach not feasible
  • Blocking issues without workarounds
  • Fundamental assumptions invalid
  • Next: Return to /workflow-analyze for re-evaluation

🎯 Spike Principles

This validation follows Agile Spike methodology:

  1. Risk Reduction: Focus on highest risk items
  2. Just Enough: Minimal code for maximum learning
  3. Throwaway Code: Prototypes are for learning, not production
  4. Clear Outcome: Binary decision on feasibility

Execution

Now start the validation task according to the guidelines above.