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-big-picture
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title: Big Picture
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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: [Big Picture Thinking, System-Level View, Holistic View]
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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: [meta, systems-thinking, architecture, decision-making]
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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: python
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framework: general
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---
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# Big Picture
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## 매 한 줄
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> **"매 zoom out before you zoom in"**. Big Picture thinking 매 system-level perspective 의 prioritization — local optimization 매 global suboptimum 의 lead 가능. 2026 LLM 시대 매 context window 1M+ tokens 매 entire codebase 의 single prompt 의 fit 가능 — Big Picture 매 finally tractable computationally.
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## 매 핵심
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### 매 Levels of abstraction
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- **L0 (atom)**: single function, single line.
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- **L1 (module)**: file, class, single concern.
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- **L2 (subsystem)**: service, package, bounded context.
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- **L3 (system)**: full application, deployment topology.
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- **L4 (ecosystem)**: organization, market, regulation.
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- 매 mistake: L0 의 stuck — never L3 까지 zoom out.
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### 매 When to zoom out
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- 매 stuck 30+ min 의 single bug → L2 의 zoom out.
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- 매 architectural decision → L3 mandatory.
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- 매 hiring / team structure → L4.
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- 매 PR review → L1 + L2 mix.
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### 매 응용
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1. Architecture review (data flow diagram).
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2. Incident postmortem (5 whys → systemic cause).
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3. Roadmap planning (quarter-level priorities).
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4. Code review (cross-cutting concerns).
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## 💻 패턴
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### Pattern 1: Context map (L3 view)
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```python
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# Visualize bounded contexts (DDD-style)
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contexts = {
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"auth": {"depends_on": [], "exposes": ["user_id", "session"]},
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"billing": {"depends_on": ["auth"], "exposes": ["invoice", "subscription"]},
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"notification": {"depends_on": ["auth", "billing"], "exposes": []},
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}
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def find_critical_path(contexts):
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"""매 highest fan-in 의 service 의 SPOF candidate."""
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fan_in = {ctx: 0 for ctx in contexts}
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for ctx, info in contexts.items():
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for dep in info["depends_on"]:
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fan_in[dep] += 1
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return sorted(fan_in.items(), key=lambda x: -x[1])
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```
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### Pattern 2: Zoom-out checklist
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```python
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ZOOM_OUT_QUESTIONS = [
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"Who else is affected by this change?",
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"What breaks if this fails at 3am?",
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"Is this the right problem to solve right now?",
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"What does success look like in 6 months?",
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"Who owns this when I leave?",
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]
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def review_pr(pr_diff: str) -> list[str]:
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return [q for q in ZOOM_OUT_QUESTIONS if not answered_in(pr_diff, q)]
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```
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### Pattern 3: Pre-mortem (L4 thinking)
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```python
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def premortem(project: str) -> dict:
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"""매 launch 전 의 'imagine it failed' exercise."""
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return {
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"tech_failure": "What technical assumption was wrong?",
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"market_failure": "Why did users not adopt?",
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"team_failure": "What organizational dynamic killed it?",
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"regulation": "What law/policy blocked it?",
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}
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```
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### Pattern 4: Dependency graph (L2 → L3)
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```python
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import networkx as nx
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def build_dep_graph(modules: dict[str, list[str]]) -> nx.DiGraph:
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g = nx.DiGraph()
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for mod, deps in modules.items():
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for d in deps:
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g.add_edge(mod, d)
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cycles = list(nx.simple_cycles(g))
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if cycles:
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print(f"매 architecture smell: {len(cycles)} cycles detected")
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return g
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```
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### Pattern 5: LLM-assisted big picture (2026)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def architecture_summary(repo_dump: str) -> str:
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"""매 1M context 의 entire repo 의 fit — 2026 standard."""
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msg = client.messages.create(
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model="claude-opus-4-7-1m",
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max_tokens=4000,
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messages=[{
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"role": "user",
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"content": f"""다음 repo 의 architecture 를 L3 perspective 의 summarize.
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Identify: (1) bounded contexts, (2) critical path, (3) tech debt hotspots.
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{repo_dump}"""
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}],
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)
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return msg.content[0].text
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```
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### Pattern 6: Tradeoff matrix
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```python
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def tradeoff_matrix(options: list[str], criteria: list[str], scores: dict) -> str:
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rows = []
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for opt in options:
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row = [opt] + [str(scores[(opt, c)]) for c in criteria]
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rows.append(" | ".join(row))
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return "\n".join(rows)
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# Usage
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options = ["monolith", "microservices", "modular monolith"]
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criteria = ["dev_speed", "ops_cost", "scalability", "team_autonomy"]
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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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| Bug fix < 1h | L0/L1 만 — zoom out 의 X. |
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| Recurring bug | L2 zoom out — systemic cause. |
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| New feature | L2 + L3 — fit 의 architecture. |
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| Postmortem | L3 + L4 mandatory. |
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| Quarterly planning | L4 only. |
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**기본값**: 매 task 의 start 의 30 sec 의 L3 sketch — bounded contexts, data flow, failure modes.
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## 🔗 Graph
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- 부모: [[Systems_Thinking|Systems-Thinking]] · [[Architecture]]
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- 응용: [[Architecture-Review]] · [[Postmortem]]
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- Adjacent: [[Bounded Context]] · [[Domain-Driven-Design]]
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## 🤖 LLM 활용
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**언제**: Architecture review, repo onboarding, postmortem synthesis, roadmap drafting. 매 1M context 의 entire codebase 의 fit 가능 — 매 truly novel 2026 capability.
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**언제 X**: Tactical bug fix (L0/L1), perf tuning of single function. 매 LLM 매 generic advice 의 emit — local context 의 lose.
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## ❌ 안티패턴
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- **Premature zoom-out**: 매 every bug 의 L4 의 escalate — 매 paralysis.
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- **Ivory tower architecture**: L3 만 — implementation reality 의 ignore.
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- **Big-picture-only PR review**: 매 nitpick 의 miss.
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- **Solo big-picture**: 매 architect 매 single person — bus factor 1.
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## 🧪 검증 / 중복
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- Verified: Donella Meadows "Thinking in Systems" (2008), Eric Evans "DDD" (2003), Nicole Forsgren "Accelerate" (2018).
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- 신뢰도 A.
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- 중복: [[Systems_Thinking|Systems-Thinking]] 매 strict superset — Big Picture 매 daily-practice variant 의 framing.
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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 — full content with L0-L4 levels, zoom-out patterns, LLM 1M context architecture summary |
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