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>

10_Wiki/Topics/Domain_General/From_Other/Middle-Out-Thinking.md

@@ -0,0 +1,140 @@
+---
+id: wiki-2026-0508-middle-out-thinking
+title: Middle Out Thinking
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Middle-Out Reasoning, Anchor-First Design]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [thinking, problem-solving, design]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: conceptual
+  framework: methodology
+---
+
+# Middle Out Thinking
+
+## 매 한 줄
+> **"매 problem-solving은 middle anchor에서 시작한다"**. 매 top-down (high-level vision)도 bottom-up (raw details)도 아닌, 매 가장 stable / well-understood 한 layer에서 양방향으로 expand 하는 reasoning approach. 매 Silicon Valley series 의 fictional compression 농담에서 시작해 매 product design / ML architecture / writing 의 real methodology 로 자리 잡았다.
+
+## 매 핵심
+
+### 매 왜 middle 인가
+- Top-down: 매 vision 명확하지만 매 details 의 unknown 많음 → premature commitment.
+- Bottom-up: 매 details 견고하지만 매 coherence 부재 → integration hell.
+- Middle-out: 매 anchor (가장 잘 아는 layer) 부터 매 outward expansion → matched uncertainty.
+
+### 매 anchor 선택 기준
+- **Highest leverage**: 매 한 decision 이 매 most downstream constraints 를 fix.
+- **Most certain**: 매 well-known domain / proven pattern.
+- **Bidirectional**: 매 위로 (abstraction)도 매 아래로 (implementation)도 expand 가능.
+
+### 매 응용
+1. **Product**: MVP feature 매 core user job 부터 → upward (positioning), downward (UI tech).
+2. **ML architecture**: 매 backbone (e.g., Transformer block) 매 anchor → upward (training loop), downward (kernel ops).
+3. **Writing**: 매 thesis sentence 매 middle → upward (intro/conclusion), downward (evidence).
+
+## 💻 패턴
+
+### Anchor identification
+```python
+# Middle-out planning helper
+def identify_anchor(problem: dict) -> str:
+    """매 highest-leverage + most-certain layer 찾기."""
+    candidates = problem["layers"]
+    scored = [
+        (layer, layer["leverage"] * layer["certainty"])
+        for layer in candidates
+    ]
+    scored.sort(key=lambda x: -x[1])
+    return scored[0][0]["name"]
+```
+
+### Bidirectional expansion
+```python
+def expand(anchor: str) -> dict:
+    upward = derive_abstractions(anchor)   # vision, goals
+    downward = derive_implementations(anchor)  # mechanisms
+    return {"up": upward, "down": downward, "anchor": anchor}
+```
+
+### Middle-out PR description
+```markdown
+## Anchor (middle)
+변경의 핵심: <one sentence>
+
+## Up (why)
+- Business / product reason
+- User-facing impact
+
+## Down (how)
+- Implementation detail 1
+- Implementation detail 2
+```
+
+### Architecture sketch (ML)
+```python
+# Anchor: TransformerBlock
+class TransformerBlock(nn.Module):
+    def __init__(self, d, h):
+        super().__init__()
+        self.attn = MultiHeadAttn(d, h)
+        self.ff = FeedForward(d)
+    def forward(self, x):
+        return self.ff(self.attn(x))
+
+# Up: stack into model
+# Down: choose attention kernel (FlashAttn vs naive)
+```
+
+### Document outline tool
+```python
+def middle_out_outline(thesis: str):
+    return {
+        "thesis": thesis,        # anchor
+        "intro": "[derive from thesis]",
+        "body": "[decompose thesis into 3 claims]",
+        "conclusion": "[restate + extend]",
+    }
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 vision 명확, 매 details 의 unknown | Middle-out (anchor at known mid layer) |
+| 매 details 의 강제 (HW constraint) | Bottom-up |
+| 매 brand-new domain, 매 nothing known | Top-down + spike |
+| 매 refactor existing system | Middle-out (anchor at stable interface) |
+
+**기본값**: Middle-out — 매 most realistic problems 에서 매 anchor 가 존재.
+
+## 🔗 Graph
+- 부모: [[Problem_Solving|Problem-Solving]] · [[Design Thinking]]
+- 변형: [[Top-Down-Design]] · [[Bottom-Up-Design]]
+- 응용: [[Minimal-Viable-Product]]
+- Adjacent: [[Pyramid Principle]]
+
+## 🤖 LLM 활용
+**언제**: 매 ambiguous spec 에서 매 LLM 에게 "what's the anchor?" 질문 → 매 most leveraged decision 부터 elaborate.
+**언제 X**: 매 trivial well-defined task — 매 직접 implementation 이 빠름.
+
+## ❌ 안티패턴
+- **Anchor too high**: 매 vision-level anchor → 매 bottom-up과 동일하게 details 폭발.
+- **Anchor too low**: 매 implementation-level anchor → 매 top-down 부재로 coherence 상실.
+- **Multiple anchors**: 매 simultaneous 의 multiple middle 선택 → 매 expansion conflict.
+
+## 🧪 검증 / 중복
+- Verified (Pyramid Principle, Minto 1987; Architectural Decision Records practice).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — anchor-first reasoning methodology with bidirectional expansion |