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-덱-빌딩-deck-building
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title: 덱 빌딩 (Deck building)
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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: [Deck Building, Deckbuilding, 덱빌딩, TCG Deck Construction]
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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, tcg, ccg, strategy, balance]
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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: text
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framework: TCG/CCG game design
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---
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# 덱 빌딩 (Deck building)
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## 매 한 줄
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> **"매 60장 안에 win condition / mana curve / interaction / consistency 의 balance를 압축하는 행위"**. Magic: The Gathering (1993)에서 시작한 덱 빌딩은 Hearthstone (2014) · Marvel Snap (2022) · Pokémon TCG Pocket (2024)을 거치며 mobile-first 12-card mini deck → 60-card constructed의 spectrum으로 evolve. 2026 현재 LLM-assisted deckbuilder (e.g. Untapped.gg AI Coach)가 meta-aware suggestion을 제공.
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## 매 핵심
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### 매 4 Pillar of Deck Construction
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- **Win Condition**: 매 매치 종료시키는 primary path (combo / aggro burn / control late-game / mill).
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- **Mana Curve**: 매 cost-by-turn distribution — 1-drop ~8, 2-drop ~12, 3-drop ~10, 4+ tapering.
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- **Consistency**: 매 draw engine + tutor (search effect) — variance 감소.
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- **Interaction**: 매 removal + counterspell — 상대 plan 방해.
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### 매 Format별 제약
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- **Constructed (60+)**: max 4-of (Hearthstone 2-of), banlist 적용.
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- **Limited / Draft (40)**: 매 sealed pool에서 on-the-fly 구성.
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- **Singleton / Commander (100)**: 매 1-of, 100-card highlander.
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- **Marvel Snap (12)**: 매 ultra-compressed — 매 card가 "tech choice".
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### 매 응용
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1. Pokémon TCG Pocket pack-opening + 20-card deck — onboarding-first design.
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2. Slay the Spire roguelike deckbuilder — run마다 deck 점진 구성.
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3. MTG Arena Brawl — 60-card singleton + commander.
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4. Hearthstone Battlegrounds — auto-battler에 deckbuild 제거, hero-pool curation.
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## 💻 패턴
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### Mana Curve Validator
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```python
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from collections import Counter
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def validate_curve(deck: list[dict]) -> dict:
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"""deck = [{'name': str, 'cmc': int, 'count': int}, ...]"""
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curve = Counter()
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total = 0
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for c in deck:
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curve[min(c['cmc'], 7)] += c['count']
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total += c['count']
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pct = {k: v / total for k, v in curve.items()}
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ideal = {1: 0.13, 2: 0.20, 3: 0.17, 4: 0.13, 5: 0.08, 6: 0.05, 7: 0.04}
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deviation = sum(abs(pct.get(k, 0) - v) for k, v in ideal.items())
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return {'curve': dict(curve), 'total': total, 'deviation': deviation}
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```
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### Hypergeometric Draw Probability
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```python
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from math import comb
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def draw_at_least_one(copies: int, deck_size: int = 60, draws: int = 7) -> float:
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"""Opening hand에 specific card 포함 확률."""
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miss = comb(deck_size - copies, draws) / comb(deck_size, draws)
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return 1 - miss
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# 매 4-of in 60: ~40% to see in opening hand
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print(draw_at_least_one(4)) # 0.3994
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```
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### Archetype Classifier (LLM-assisted)
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```python
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import anthropic
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client = anthropic.Anthropic()
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def classify_archetype(decklist: list[str]) -> str:
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=200,
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system="You are an MTG meta analyst. Classify decks as Aggro/Midrange/Control/Combo/Tempo.",
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messages=[{"role": "user", "content": "\n".join(decklist)}],
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)
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return msg.content[0].text
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```
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### Snap-style 12-Card Synergy Score
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```python
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def synergy_score(deck: list[str], synergy_graph: dict[tuple, float]) -> float:
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score = 0.0
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for i, a in enumerate(deck):
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for b in deck[i+1:]:
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score += synergy_graph.get((a, b), 0) + synergy_graph.get((b, a), 0)
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return score / len(deck)
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```
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### Sideboard Optimizer
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```python
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def optimize_sideboard(main: list[str], meta: dict[str, float],
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cards: dict[str, dict[str, float]]) -> list[str]:
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"""meta = {archetype: weight}, cards[c][archetype] = win_rate_delta."""
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scored = []
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for c, deltas in cards.items():
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if c in main: continue
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ev = sum(meta.get(a, 0) * d for a, d in deltas.items())
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scored.append((ev, c))
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scored.sort(reverse=True)
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return [c for _, c in scored[:15]]
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```
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### Draft Pick EV
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```python
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def pick_ev(card: str, picks_so_far: list[str], wheels: dict[str, float]) -> float:
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"""Card power × signal × wheel probability."""
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base = card_power(card)
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in_color_bonus = 1.3 if shares_color(card, picks_so_far) else 0.8
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wheel_pen = 1.0 - wheels.get(card, 0) * 0.2 # 매 wheel 가능성 → 후순위
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return base * in_color_bonus * wheel_pen
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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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| Mobile onboarding | 12-card (Snap) or 20-card (Pocket) |
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| Competitive depth | 60-card constructed |
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| Social play | Commander 100-card singleton |
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| Roguelike | Procedural deckbuild (StS) |
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| Beginner | Pre-constructed starter |
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**기본값**: 60-card constructed with 4-of legality + 15-card sideboard.
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## 🔗 Graph
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- 부모: [[Game_Design]]
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- 변형: [[Draft_Mode]]
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## 🤖 LLM 활용
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**언제**: meta classification, sideboard suggestion, decklist parse → archetype, beginner coaching.
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**언제 X**: 매 micro-frame technical play (turn-by-turn) — Monte Carlo simulator가 superior.
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## ❌ 안티패턴
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- **Random pile**: 매 win condition 없는 60장 collection.
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- **Curve cliff**: 매 mana curve의 4+ slot 비대 → opening hand brick.
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- **No interaction**: 매 pure goldfish deck — 상대 plan 무시.
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- **Over-tutoring**: 매 tutor 8장 + 1-of toolbox → consistency illusion, 실제는 expensive.
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## 🧪 검증 / 중복
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- Verified (Frank Karsten *Mana Math*, MTG Pro Tour data 2014-2025).
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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 — TCG/CCG deck construction 4-pillar + format spectrum + 2026 LLM coach 정리 |
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