refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-flow-state
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title: Flow State
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Flow, Flow State, 몰입, Csikszentmihalyi Flow]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [psychology, game-design, productivity, ux]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: design
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framework: ux
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---
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# Flow State
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## 매 한 줄
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> **"매 challenge와 skill이 정확히 균형된 순간 시간이 사라진다"**. 매 Flow는 Csikszentmihalyi(1975)가 정의한 optimal experience — 매 자아 의식 사라짐, immediate feedback, intrinsic reward. 2026 game design, productivity tool, learning platform의 매 design target.
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## 매 핵심
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### 매 8 Conditions (Csikszentmihalyi)
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1. **Clear goals**: 매 매 순간 무엇을 해야 하는지 명확
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2. **Immediate feedback**: 매 action → result 즉시
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3. **Skill-challenge balance**: 매 너무 쉬우면 boredom, 너무 어려우면 anxiety
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4. **Action-awareness merge**: 매 doing = thinking
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5. **Concentration**: 매 task에 완전 집중
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6. **Sense of control**: 매 outcome 지배감
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7. **Loss of self-consciousness**: 매 ego 사라짐
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8. **Time distortion**: 매 5시간이 30분처럼
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### 매 Flow Channel
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- **Anxiety zone**: 매 challenge ≫ skill
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- **Boredom zone**: 매 challenge ≪ skill
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- **Flow channel**: 매 둘이 동반 상승 (스킬 ↑ → 도전 ↑)
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### 매 응용
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1. Game difficulty curves (Souls-like, rhythm games).
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2. Coding flow (Pomodoro, focus mode).
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3. Dynamic difficulty adjustment (Resident Evil 4 director AI).
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4. Educational scaffolding (Duolingo adaptive).
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## 💻 패턴
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### Dynamic Difficulty Adjustment (Unity C#)
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```csharp
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public class FlowDifficulty : MonoBehaviour
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{
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float playerSkill = 0.5f; // 매 EMA estimate
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float currentDifficulty = 0.5f;
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void OnPlayerSuccess()
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{
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playerSkill = 0.9f * playerSkill + 0.1f * 1.0f;
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AdjustDifficulty();
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}
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void OnPlayerFail()
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{
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playerSkill = 0.9f * playerSkill + 0.1f * 0.0f;
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AdjustDifficulty();
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}
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void AdjustDifficulty()
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{
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// 매 keep difficulty slightly above skill (flow channel)
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currentDifficulty = Mathf.Clamp(playerSkill + 0.1f, 0.1f, 0.95f);
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ApplyDifficulty(currentDifficulty);
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}
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}
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```
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### Focus Mode (VS Code extension)
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```typescript
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import * as vscode from 'vscode';
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export function enterFlowMode() {
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vscode.workspace.getConfiguration().update('zenMode.fullScreen', true);
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vscode.workspace.getConfiguration().update('workbench.activityBar.visible', false);
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vscode.workspace.getConfiguration().update('editor.minimap.enabled', false);
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// 매 disable notifications
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vscode.commands.executeCommand('notifications.toggleDoNotDisturbMode');
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// 매 25min Pomodoro
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setTimeout(() => {
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vscode.window.showInformationMessage('매 flow check: 잠시 휴식?');
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}, 25 * 60 * 1000);
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}
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```
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### Skill Tracking (Python EMA)
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```python
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class SkillTracker:
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def __init__(self, alpha=0.1):
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self.alpha = alpha
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self.skill = 0.5
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def update(self, success: bool):
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target = 1.0 if success else 0.0
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self.skill = (1 - self.alpha) * self.skill + self.alpha * target
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def in_flow_zone(self, difficulty: float) -> bool:
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# 매 difficulty 가 skill 의 ±15% 안에 있어야 flow
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return abs(difficulty - self.skill) < 0.15
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```
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### Immediate Feedback (Web)
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```javascript
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// 매 keystroke마다 syntax check (flow-friendly)
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let timeoutId;
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editor.on('change', () => {
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clearTimeout(timeoutId);
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timeoutId = setTimeout(() => {
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const errors = lint(editor.getValue());
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showInlineErrors(errors); // 매 즉시 visual feedback
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}, 100); // 매 debounce 100ms — 매 fast enough for flow
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});
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```
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### Distraction Blocking (macOS Focus)
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```bash
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# 매 macOS Shortcuts CLI
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shortcuts run "Focus: Do Not Disturb On"
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# 매 hosts file block social
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echo "0.0.0.0 twitter.com" | sudo tee -a /etc/hosts
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echo "0.0.0.0 reddit.com" | sudo tee -a /etc/hosts
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```
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### Flow Telemetry (game)
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```python
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def log_session_flow(session):
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duration = session.end - session.start
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deaths = session.deaths
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completions = session.completions
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# 매 high engagement + moderate failure = flow
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if duration > timedelta(minutes=20) \
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and 0.3 < deaths / (deaths + completions) < 0.6:
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analytics.track("flow_session", session.user_id)
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```
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## 매 결정 기준
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| 상황 | Design priority |
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|---|---|
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| Action game | Tight feedback loop, DDA |
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| Strategy game | Clear goal, skill ceiling exposure |
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| Learning app | Adaptive difficulty, immediate hint |
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| Productivity tool | Distraction-free mode, focus timer |
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| Casual game | Low anxiety, high accessibility |
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**기본값**: 매 difficulty = skill + 10%, feedback < 200ms, distraction = 0.
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## 🔗 Graph
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- 부모: [[Game Design]]
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- 변형: [[Deep Work]] (Cal Newport)
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- 응용: [[DDA]]
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- Adjacent: [[Intrinsic Motivation]] · [[Self-Determination Theory]]
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## 🤖 LLM 활용
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**언제**: 매 game design review, learning UX critique, productivity workflow 진단.
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**언제 X**: 매 medical/clinical attention disorder context — flow state는 self-help framework, not therapy.
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## ❌ 안티패턴
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- **Forced flow**: 매 user를 trap (매 dark UX) → 매 burnout, churn.
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- **Notification interrupt**: 매 push 매 5분 → 매 flow 진입 불가능.
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- **Difficulty spike**: 매 sudden challenge → 매 anxiety zone 추락.
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- **Reward without challenge**: 매 free progress → 매 boredom.
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## 🧪 검증 / 중복
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- Verified (Csikszentmihalyi, "Flow: The Psychology of Optimal Experience", 1990).
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- Verified (Sweetser & Wyeth, "GameFlow: A Model for Evaluating Player Enjoyment in Games", 2005).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Csikszentmihalyi flow + game design |
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