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-brand-identity-management
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title: Brand Identity Management
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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: [P-REINFORCE-AUTO-F8EDF9, Brand Identity, BI Management]
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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: [branding, marketing, design-systems, identity]
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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: figma-tokens
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---
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# Brand Identity Management
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## 매 한 줄
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> **"매 brand 의 systematic codification"**. 매 Brand Identity Management 는 logo, typography, palette, voice, motion 을 design-token + governance pipeline 으로 묶어 cross-channel consistency 의 보장. 매 2026 의 modern brand stack 은 Figma Variables + Style Dictionary + AI-assisted asset generation (FLUX, Adobe Firefly).
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## 매 핵심
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### 매 brand asset 분류
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- **Visual**: logo, color palette, typography, iconography, photography style.
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- **Verbal**: tone of voice, lexicon, naming convention.
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- **Motion**: easing curves, timing, transition vocabulary.
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- **Sonic**: audio logo, UI sounds.
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### 매 governance layer
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- **Design tokens**: 매 single source of truth (Figma Variables → Style Dictionary → CSS/iOS/Android).
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- **Brand portal**: 매 self-service asset library (Frontify, Brandfolder).
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- **Approval workflow**: 매 marketing automation 의 brand-safe template.
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### 매 응용
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1. Multi-product company 의 sub-brand 관리 (Atlassian Jira/Confluence/Trello).
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2. Localization 의 culturally-adapted asset variant.
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3. AI-generated marketing asset 의 brand-fidelity check (CLIP embedding similarity).
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## 💻 패턴
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### 패턴 1: Design Token 정의 (Style Dictionary)
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```json
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{
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"color": {
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"brand": {
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"primary": { "value": "#FF6B35", "comment": "Antigravity Orange" },
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"accent": { "value": "#1B1B1F" },
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"surface": { "value": "#FAFAFA" }
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}
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},
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"typography": {
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"display": {
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"value": {
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"fontFamily": "Inter",
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"fontWeight": 700,
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"fontSize": "48px",
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"lineHeight": 1.1
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}
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}
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}
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}
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```
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### 패턴 2: Token → Multi-platform output
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```js
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// style-dictionary.config.js
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module.exports = {
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source: ['tokens/**/*.json'],
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platforms: {
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css: { transformGroup: 'css', buildPath: 'dist/css/', files: [{ destination: 'tokens.css', format: 'css/variables' }] },
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ios: { transformGroup: 'ios', buildPath: 'dist/ios/', files: [{ destination: 'Tokens.swift', format: 'ios-swift/class.swift' }] },
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android:{ transformGroup: 'android',buildPath: 'dist/android/',files: [{ destination: 'tokens.xml', format: 'android/resources' }] }
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}
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};
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```
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### 패턴 3: Brand-fidelity check (CLIP)
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```python
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import torch
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import clip
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from PIL import Image
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model, preprocess = clip.load("ViT-B/32", device=device)
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BRAND_PROMPT = "Antigravity orange, minimal modern tech brand, geometric"
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def brand_fidelity(image_path: str) -> float:
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image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
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text = clip.tokenize([BRAND_PROMPT]).to(device)
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with torch.no_grad():
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image_features = model.encode_image(image)
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text_features = model.encode_text(text)
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sim = torch.cosine_similarity(image_features, text_features).item()
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return sim # > 0.28 → on-brand
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```
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### 패턴 4: AI-generated asset 의 brand guardrail
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def critique_asset(asset_url: str, guidelines: str) -> dict:
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response = client.messages.create(
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model="claude-opus-4-7-20260301",
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max_tokens=1024,
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messages=[{
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"role": "user",
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"content": [
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{"type": "image", "source": {"type": "url", "url": asset_url}},
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{"type": "text", "text": f"Brand guidelines:\n{guidelines}\n\nReturn JSON: {{on_brand: bool, issues: [...], score: 0-1}}"}
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]
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}]
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)
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return response.content[0].text
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```
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### 패턴 5: Tone-of-voice classifier
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```python
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TONE_REFERENCE = {
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"playful": ["hey", "let's", "boom", "yay"],
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"formal": ["please", "kindly", "we regret"],
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"antigravity": ["lift", "soar", "weightless", "boundless"]
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}
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def classify_tone(text: str) -> str:
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scores = {tone: sum(text.lower().count(w) for w in words)
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for tone, words in TONE_REFERENCE.items()}
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return max(scores, key=scores.get)
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```
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### 패턴 6: Logo placement validator (CV)
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```python
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import cv2
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import numpy as np
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def safe_zone_violation(image, logo_bbox, min_padding_ratio=0.1):
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h, w = image.shape[:2]
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x, y, lw, lh = logo_bbox
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pad_x = min_padding_ratio * w
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pad_y = min_padding_ratio * h
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return (x < pad_x or y < pad_y or
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x + lw > w - pad_x or y + lh > h - pad_y)
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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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| Multi-platform consistency | Style Dictionary + Figma Variables |
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| AI-asset workflow | CLIP fidelity gate + human review |
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| Sub-brand 관리 | shared core tokens + brand-specific overrides |
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| Rapid iteration startup | Figma Library only, defer token build |
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| Enterprise compliance | Frontify/Brandfolder + approval workflow |
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**기본값**: Figma Variables → Style Dictionary → CLIP gate. 매 token-first.
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## 🔗 Graph
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- 부모: [[Design Systems]] · [[Marketing]]
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- Adjacent: [[Style Dictionary Pipelines|Style Dictionary]] · [[Figma Variables]] · [[CLIP]]
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## 🤖 LLM 활용
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**언제**: brand audit, tone consistency check, asset critique, copy generation 의 voice guardrail.
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**언제 X**: 매 high-stakes legal trademark review — 매 lawyer 의 영역.
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## ❌ 안티패턴
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- **Token sprawl**: 매 ad-hoc 50+ color token. 매 semantic naming (primary/secondary) 으로 collapse.
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- **Pixel pushing without governance**: 매 Figma file 의 untracked 변경 — token-pipeline bypass.
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- **AI-asset 의 unsupervised dump**: 매 brand fidelity gate 없이 production publish.
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## 🧪 검증 / 중복
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- Verified (Style Dictionary docs, Figma Variables docs, Frontify case studies).
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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 — substantive content + 2026 stack (CLIP gate, AI-asset workflow) |
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