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-게이미피케이션
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title: 게이미피케이션
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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: [Gamification, 게임화, Game-like Mechanics]
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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: [game-design, behavior, motivation, 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: typescript
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framework: behavioral-design / Octalysis
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---
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# 게이미피케이션
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## 매 한 줄
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> **"매 non-game context에 game-design element를 적용해 user motivation / engagement / behavior를 reshape"**. 2002 Nick Pelling의 용어 등장 → 2010 Foursquare 배지로 mainstream → 2014 Yu-kai Chou의 Octalysis 8-driver framework → 2026 현재 Duolingo · Strava · Apple Fitness가 industry exemplar이며 LLM-generated personalized quests가 emerging.
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## 매 핵심
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### 매 PBL Triad (Basic)
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- **Points (P)**: 매 quantitative score — XP, currency.
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- **Badges (B)**: 매 achievement marker — collection / status.
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- **Leaderboards (L)**: 매 social comparison — ranked scoreboard.
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### 매 Octalysis 8 Core Drives (Yu-kai Chou)
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1. **Epic Meaning**: 매 큰 mission의 일부 — Wikipedia 기여.
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2. **Development & Accomplishment**: 매 progress / mastery — Duolingo streak.
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3. **Empowerment of Creativity**: 매 player creativity expression — Minecraft.
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4. **Ownership & Possession**: 매 collection / customization.
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5. **Social Influence**: 매 peer comparison / cooperation.
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6. **Scarcity & Impatience**: 매 limited-time / FOMO.
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7. **Unpredictability**: 매 variable reward — gacha, slot.
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8. **Loss & Avoidance**: 매 streak break 회피.
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### 매 응용
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1. Duolingo — streak, league, hearts (loss-avoidance).
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2. Strava — segment leaderboard, kudos.
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3. Apple Fitness — 3 rings + 30-day challenge.
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4. Habitica — RPG-style habit tracker.
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5. LinkedIn — profile completion %, "All-Star" badge.
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## 💻 패턴
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### XP / Level System
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```typescript
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const xpForLevel = (level: number): number =>
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Math.floor(100 * Math.pow(level, 1.5));
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export function levelFromXP(totalXP: number): { level: number; progress: number } {
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let level = 1;
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let acc = 0;
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while (acc + xpForLevel(level) <= totalXP) {
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acc += xpForLevel(level);
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level++;
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}
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const inLevel = totalXP - acc;
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const need = xpForLevel(level);
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return { level, progress: inLevel / need };
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}
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```
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### Streak Tracker
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```typescript
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import { differenceInCalendarDays } from 'date-fns';
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interface StreakState { current: number; longest: number; lastActiveDate: Date }
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export function recordActivity(state: StreakState, now: Date = new Date()): StreakState {
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const gap = differenceInCalendarDays(now, state.lastActiveDate);
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if (gap === 0) return state; // already counted today
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if (gap === 1) { // consecutive day
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const next = state.current + 1;
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return { current: next, longest: Math.max(next, state.longest), lastActiveDate: now };
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}
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return { current: 1, longest: state.longest, lastActiveDate: now }; // broken
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}
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```
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### Badge Engine (rule-based)
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```typescript
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type BadgeRule = { id: string; name: string; check: (s: PlayerStats) => boolean };
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const RULES: BadgeRule[] = [
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{ id: 'first_blood', name: 'First Win', check: s => s.wins >= 1 },
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{ id: 'centurion', name: '100 Wins', check: s => s.wins >= 100 },
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{ id: 'streak_30', name: '30-Day Streak', check: s => s.streak >= 30 },
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];
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export function evaluateBadges(stats: PlayerStats, owned: Set<string>): string[] {
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return RULES.filter(r => !owned.has(r.id) && r.check(stats)).map(r => r.id);
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}
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```
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### Variable Reward (Loot Box) with disclosed odds
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```typescript
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type Drop = { item: string; weight: number };
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export function rollLoot(table: Drop[], rng: () => number = Math.random): string {
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const total = table.reduce((s, d) => s + d.weight, 0);
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let r = rng() * total;
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for (const d of table) {
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r -= d.weight;
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if (r <= 0) return d.item;
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}
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return table[table.length - 1].item;
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}
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// 매 disclosed odds (regulatory: KR / CN / BE / NL)
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const TABLE: Drop[] = [
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{ item: 'common', weight: 70 },
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{ item: 'rare', weight: 25 },
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{ item: 'epic', weight: 4.5 },
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{ item: 'legendary', weight: 0.5 },
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];
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```
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### Leaderboard (Redis ZSET)
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```typescript
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import { Redis } from '@upstash/redis';
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const redis = Redis.fromEnv();
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export async function recordScore(userId: string, score: number) {
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await redis.zadd('leaderboard:weekly', { score, member: userId });
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}
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export async function topN(n = 10) {
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return redis.zrange('leaderboard:weekly', 0, n - 1, { rev: true, withScores: true });
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}
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export async function rankOf(userId: string) {
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return redis.zrevrank('leaderboard:weekly', userId);
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}
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```
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### Personalized Quest (LLM-generated)
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```typescript
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import Anthropic from '@anthropic-ai/sdk';
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const client = new Anthropic();
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export async function dailyQuest(profile: { skills: string[]; weak: string[]; mood: string }) {
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const msg = await client.messages.create({
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model: 'claude-opus-4-7',
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max_tokens: 200,
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system: 'Generate ONE daily quest for a learning app. Output JSON: {title, goal, xp, steps[]}',
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messages: [{ role: 'user', content: JSON.stringify(profile) }],
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});
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return JSON.parse((msg.content[0] as { text: string }).text);
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Education app | Streak + XP + League (Duolingo) |
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| Fitness | Goal-rings + monthly challenge |
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| Productivity | Habit-RPG (Habitica) |
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| Enterprise training | Badge + leaderboard, opt-in |
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| Health-critical | Avoid scarcity / FOMO — ethical-only |
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**기본값**: PBL + 1~2 Octalysis drives matched to context, with disclosed-odds + opt-out.
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## 🔗 Graph
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- 부모: [[Game_Design]]
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- 변형: [[Self_Determination_Theory]]
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- Adjacent: [[Operant_Conditioning]]
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## 🤖 LLM 활용
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**언제**: personalized quest generation, badge naming, encouragement copy, weakness-targeted exercise sequencing.
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**언제 X**: 매 reward-schedule design — RL / behavioral economist domain (LLM은 hidden bias 위험).
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## ❌ 안티패턴
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- **Pointsification**: 매 PBL만 얹기 — intrinsic motivation 없으면 6주 후 fatigue.
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- **Dark patterns**: 매 streak shame, FOMO-pressure, sunk-cost manipulation.
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- **One-size-fits-all**: 매 모든 user에 leaderboard — competitive-averse user 이탈.
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- **Reward inflation**: 매 점차 큰 보상 → 매 동일 효과 위해 escalation 필요.
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## 🧪 검증 / 중복
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- Verified (Yu-kai Chou *Actionable Gamification*, Self-Determination Theory, Duolingo Engineering Blog 2024).
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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 — PBL + Octalysis 8 drives + ethical guardrails 정리 |
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