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-lighting-composition
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title: "Lighting & Composition"
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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: [Cinematography, Visual Composition, Lighting Design]
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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: [photography, cinematography, generative-AI, prompt-engineering]
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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: prompt
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framework: FLUX-Sora-Veo
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---
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# Lighting & Composition
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## 매 한 줄
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> **"매 image / video 의 emotional weight 의 90%는 lighting + composition"**. 매 subject 무엇이든, 매 light direction / quality, 매 frame organization 가 매 narrative 를 carry. 매 2026 의 generative AI (FLUX 1.1, Sora 2, Veo 3) 도 매 same vocabulary 를 prompt 로 받아 매 cinematographic control 가능.
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## 매 핵심
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### 매 lighting 의 axes
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- **Direction**: front / side / back / top / bottom (각 emotional valence 다름).
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- **Quality**: hard (sharp shadows) vs soft (diffused).
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- **Color temperature**: warm (3000K) vs cool (6500K).
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- **Ratio**: key:fill ratio (1:1 flat, 4:1 dramatic).
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### 매 composition 의 axes
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- **Rule of thirds**: 매 subject 매 third lines 의 intersection.
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- **Leading lines**: 매 viewer's eye 의 directing.
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- **Negative space**: 매 emptiness 가 carry meaning.
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- **Depth layers**: foreground / mid / background.
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- **Aspect ratio**: 16:9 (cinematic), 9:16 (mobile), 1:1 (square), 2.39:1 (anamorphic).
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### 매 응용
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1. **Photography**: portrait / product / landscape lighting setups.
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2. **Cinematography**: 매 scene mood 의 establishing.
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3. **Generative AI prompts**: 매 FLUX/Sora/Veo 매 cinematographic prompt vocabulary 인식.
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4. **UI design**: 매 hero image direction selection.
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## 💻 패턴
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### Three-point lighting prompt
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```
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A portrait of a software engineer, three-point lighting:
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key light from camera-left at 45 degrees (soft, 5500K),
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fill light from camera-right at 1:2 ratio,
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backlight rim from upper-right (warm 3200K),
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shallow depth of field, 85mm lens equivalent,
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shot on Arri Alexa 35.
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```
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### Rembrandt lighting (FLUX 1.1 prompt)
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```python
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flux_prompt = """
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Rembrandt lighting on subject's face:
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small triangle of light on shadow-side cheek,
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key light camera-left high at 45°,
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deep shadow on right side,
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chiaroscuro mood, oil painting aesthetic,
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photorealistic, 8K detail.
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"""
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```
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### Golden hour video (Sora 2)
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```python
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sora_prompt = {
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"shot": "tracking shot through wheat field",
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"lighting": "golden hour, sun low at 15° angle camera-back-left, "
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"warm 2800K, long shadows, lens flare",
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"composition": "rule of thirds, horizon on lower third, "
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"leading lines from wheat rows toward subject",
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"camera": "Steadicam, 24fps, 35mm anamorphic, T2.0",
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"duration_s": 8,
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}
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```
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### Composition checker (CV-based QA)
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```python
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import cv2
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import numpy as np
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def rule_of_thirds_score(img: np.ndarray) -> float:
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"""매 saliency map peak 매 third-line proximity."""
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h, w = img.shape[:2]
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saliency = cv2.saliency.StaticSaliencyFineGrained_create()
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_, sal = saliency.computeSaliency(img)
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peak_y, peak_x = np.unravel_index(sal.argmax(), sal.shape)
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third_lines_x = [w/3, 2*w/3]
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third_lines_y = [h/3, 2*h/3]
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dx = min(abs(peak_x - tx) for tx in third_lines_x) / w
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dy = min(abs(peak_y - ty) for ty in third_lines_y) / h
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return 1.0 - min(dx, dy) * 2 # higher = better composition
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```
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### Color temp grading (Veo 3 prompt augmentation)
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```python
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def grade_prompt(base: str, mood: str) -> str:
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grades = {
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"warm_nostalgic": "teal-orange grade, warm midtones (3200K), cool shadows",
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"cold_clinical": "desaturated blues, 6500K key, high-key flat lighting",
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"noir": "high-contrast B&W, low-key, single hard source, 4:1 ratio",
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}
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return f"{base} | grade: {grades[mood]}"
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```
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## 매 결정 기준
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| Mood | Lighting | Composition |
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|---|---|---|
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| Heroic | Backlight rim + low-key fill | Low angle, centered |
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| Intimate | Soft key, high ratio | Close-up, off-center |
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| Tense | Hard side light, deep shadow | Dutch tilt, asymmetric |
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| Whimsical | Bright fill, warm tones | Wide, symmetrical |
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| Documentary | Available light | Eye-level, rule of thirds |
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**기본값**: 매 three-point + rule of thirds — 매 safe baseline.
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## 🔗 Graph
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- 부모: [[Cinematography]]
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## 🤖 LLM 활용
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**언제**: 매 image/video prompt engineering — 매 cinematographic vocabulary 매 quality lift 큼.
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**언제 X**: 매 abstract / non-representational generation — vocabulary 의 X.
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## ❌ 안티패턴
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- **Flat front lighting**: 매 amateur look — depth loss.
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- **Centered everything**: 매 visual boring.
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- **Mixed color temps unintentional**: 매 amateur giveaway.
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- **Over-prompting**: 매 50+ tokens 의 lighting → 매 model confusion.
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## 🧪 검증 / 중복
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- Verified (Brown *Cinematography*; Block *Visual Story*; FLUX 1.1 prompt guide; Sora 2 system card 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 — lighting/composition vocabulary + 2026 generative AI prompts |
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