docs: domain 필드 참조를 문서에서 제거

- CLAUDE.md에서 domain 필드 정의 제거
- CLAUDE.md의 필수 필드 목록 업데이트 (korean, definition만)
- CLAUDE.md에서 domain 색상 매핑 설명 제거
- public/llms.txt에서 domain 필드 설명 제거

Author: 9bow

CLAUDE.md

@@ -30,7 +30,7 @@ Static SPA for AI/ML Korean terminology glossary. No backend; all data is JSON f
 - `/term/:termId` → `TermDetailPage` (accordion per meaning, related terms)
 - `/about` → `AboutPage`
 
-**Data flow**: `data/index.json` lists alphabet JSON files (`a.json`..`w.json`). At build time, `prebuild` copies `data/` → `public/data/`. At runtime, `loadTerms.ts` fetches all files in parallel via `Promise.all`, flattens, and sorts. The `useTerms` hook caches this; `useSearch` debounces (200ms) and filters across `term`, `korean`, `domain`, `synonyms`.
+**Data flow**: `data/index.json` lists alphabet JSON files (`a.json`..`w.json`). At build time, `prebuild` copies `data/` → `public/data/`. At runtime, `loadTerms.ts` fetches all files in parallel via `Promise.all`, flattens, and sorts. The `useTerms` hook caches this; `useSearch` debounces (200ms) and filters across `term`, `korean`, `synonyms`.
 
 **Homepage**: `SearchPage` embeds `HeroSection` (stats) + `AlphabetNavigation` (A-Z chips) + `TermCard` grid. The `Layout` component wraps non-home pages with AppBar + footer; home page hides AppBar.
 
@@ -42,7 +42,6 @@ Defined in `src/types/term.ts`:
 interface TermExample { en: string; ko: string; source?: string }
 interface TermMeaning {
   korean: string              // Required
-  domain: string              // Required, no spaces (e.g., "딥러닝")
   definition: string          // Required
   examples: (string | TermExample)[]  // Union type for backward compat
   synonyms: string[]
@@ -55,8 +54,6 @@ interface Term {
 }
 ```
 
-Domain colors are mapped in `utils/domainColors.ts` (29 domains). New domains need a color entry there.
-
 **Examples format**: Prefer `TermExample` objects (`{en, ko, source?}`) over plain strings. Both are supported. `TermDetailPage` uses `isTermExample()` type guard to render accordingly.
 
 **Optional fields**: `issueNumber` links to the GitHub Issue where the term was discussed. `notes` provides editorial context. Both are set automatically by the co-author workflow, or manually when adding terms via PR.
@@ -65,9 +62,8 @@ Domain colors are mapped in `utils/domainColors.ts` (29 domains). New domains ne
 
 1. Edit the appropriate `data/{letter}.json` file
 2. If creating a new letter file, add it to `data/index.json`
-3. Required fields per meaning: `korean`, `domain` (no spaces), `definition`
-4. Domain names must have no spaces (e.g., `컴퓨터비전` not `컴퓨터 비전`)
-5. CI validates: JSON syntax, required fields, domain spacing, TermExample `en`/`ko` presence
+3. Required fields per meaning: `korean`, `definition`
+4. CI validates: JSON syntax, required fields, TermExample `en`/`ko` presence
 
 ## Co-author Workflow
 
@@ -81,7 +77,7 @@ Domain colors are mapped in `utils/domainColors.ts` (29 domains). New domains ne
 
 ## CI/CD
 
-`.github/workflows/deploy.yml`: On push to `poc`, validates term data → builds → deploys to GitHub Pages. The validation step checks all JSON files for required fields, domain spacing, TermExample structure, and optional field types (`issueNumber`, `notes`).
+`.github/workflows/deploy.yml`: On push to `poc`, validates term data → builds → deploys to GitHub Pages. The validation step checks all JSON files for required fields, TermExample structure, and optional field types (`issueNumber`, `notes`).
 
 `.github/workflows/term-coauthor.yml`: Co-author workflow (see above).
 

public/llms.txt

@@ -8,7 +8,7 @@
 
 ## 용어 데이터
 
-용어 데이터는 알파벳별 JSON 파일로 제공됩니다. 각 용어는 영어 원문(term), 한국어 번역(korean), 분야(domain), 정의(definition), 예시(examples), 유의어(synonyms)를 포함합니다.
+용어 데이터는 알파벳별 JSON 파일로 제공됩니다. 각 용어는 영어 원문(term), 한국어 번역(korean), 정의(definition), 예시(examples), 유의어(synonyms)를 포함합니다.
 
 - [파일 목록 (index.json)](https://poc.terms.kr/data/index.json): 전체 알파벳별 JSON 파일 목록
 - [A](https://poc.terms.kr/data/a.json): attention, agent, activation function, adversarial example, anchor box, all-reduce, autograd
@@ -42,7 +42,6 @@
   "meanings": [
     {
       "korean": "어텐션",
-      "domain": "딥러닝",
       "definition": "입력 시퀀스의 각 요소에 서로 다른 가중치를 부여하여 중요한 정보에 집중하는 메커니즘",
       "examples": [
         {