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>
This commit is contained in:
Antigravity Agent
2026-07-11 11:05:56 +09:00
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---
id: wiki-2026-0508-5r-structure
title: 5R Structure
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [5Rs Framework, Five Rs, 5R Communication, Replication 5R]
duplicate_of: none
source_trust_level: B
confidence_score: 0.8
verification_status: applied
tags: [framework, communication, project-management, structuring]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
language: na
framework: na
---
# 5R Structure
## 매 한 줄
> **"매 5R 은 매 communication / project / data lifecycle 을 매 5 phase 로 grouping 하는 mnemonic"**. 매 context 별로 다른 5R 이 존재 — 매 가장 widely cited 는 (Reception → Recognition → Recall → Response → Reaction) 의 communication 모델 + (Reproducible Research) 5R 등. 매 2026 에서 매 LLM agent design (RAG → 매 5R 변형) 에도 적용된다.
## 매 핵심
### 매 가장 흔한 5R 변형
- **Communication 5R** (Schramm 후속): Reception, Recognition, Recall, Response, Reaction.
- **Reproducibility 5R** (Goble 2014): Re-run, Repeat, Reproduce, Reuse, Replicate.
- **Waste hierarchy 5R**: Refuse, Reduce, Reuse, Repurpose, Recycle.
- **Project 5R**: Right thing, Right time, Right way, Right resources, Right result.
- **Customer-relationship 5R**: Reach, Relate, Retain, Reward, Refer.
### 매 공통 구조
- 매 sequential 또는 매 layered.
- 매 mnemonic 이 핵심 — 매 R 알파벳 시작 단어 force.
- 매 framework, not theory — 매 prescriptive checklist.
### 매 응용
1. **Research project**: 매 reproducibility 5R 로 매 paper review.
2. **Customer onboarding**: 매 Reach→Refer 5R.
3. **AI agent design**: 매 RAG (Retrieve, Rerank, Read, Reason, Respond) — 매 modern 5R.
## 💻 패턴
### Pattern 1: Reproducibility 5R 검증
```python
# Goble 2014 — 5R reproducibility checklist
checklist = {
"rerun": lambda paper: paper.has("docker_image"),
"repeat": lambda paper: paper.has("seeds_fixed"),
"reproduce": lambda paper: paper.has("data_open"),
"reuse": lambda paper: paper.has("license_permissive"),
"replicate": lambda paper: paper.has("methods_section_complete"),
}
score = sum(fn(paper) for fn in checklist.values()) / 5
```
### Pattern 2: 매 RAG 5R agent
```python
def rag_5r(query: str, kb) -> str:
docs = retrieve(query, kb, k=20) # Retrieve
docs = rerank(query, docs, k=5) # Rerank
chunks = read_full(docs) # Read
plan = reason(query, chunks) # Reason
return respond(plan) # Respond
```
### Pattern 3: 매 Customer 5R funnel
```sql
SELECT stage,
COUNT(*) AS users,
LAG(COUNT(*)) OVER (ORDER BY stage_order) AS prev,
COUNT(*)::float / NULLIF(LAG(COUNT(*)) OVER (ORDER BY stage_order), 0) AS conv
FROM (
SELECT user_id, 'reach' AS stage, 1 AS stage_order FROM impressions
UNION ALL SELECT user_id, 'relate', 2 FROM signups
UNION ALL SELECT user_id, 'retain', 3 FROM active_30d
UNION ALL SELECT user_id, 'reward', 4 FROM rewards_claimed
UNION ALL SELECT user_id, 'refer', 5 FROM referrals
)
GROUP BY stage, stage_order
ORDER BY stage_order;
```
### Pattern 4: Waste 5R audit
```python
items = [...] # household / office items
for item in items:
if can_refuse(item): act = "refuse"
elif can_reduce(item): act = "reduce"
elif can_reuse(item): act = "reuse"
elif can_repurpose(item):act = "repurpose"
else: act = "recycle"
```
### Pattern 5: 매 Communication 5R review
```markdown
- Reception: was the message received? (delivery confirmation)
- Recognition: was sender / topic identified?
- Recall: can the receiver recall key points 24h later?
- Response: did receiver respond?
- Reaction: was behavior changed?
```
## 매 결정 기준
| 상황 | Which 5R |
|---|---|
| 매 research paper review | Reproducibility 5R |
| 매 sustainability audit | Waste 5R |
| 매 customer growth | Reach→Refer |
| 매 LLM agent | RAG 5R (Retrieve/Rerank/Read/Reason/Respond) |
| 매 communication training | Reception→Reaction |
**기본값**: 매 context 명시 — 매 "5R" 단독 의 ambiguous. 매 always qualify (예: "RAG 5R", "Waste 5R").
## 🔗 Graph
- 부모: [[Pyramid Principle]] · [[MECE + Pyramid Principle--]]
- 변형: [[Rule of Three]]
- 응용: [[Knowledge synthesis]] · [[Process_Reflection_Template]]
- Adjacent: [[Working-Backwards]] · [[Outside-Thinking]]
## 🤖 LLM 활용
**언제**: 매 structured output template 생성 — 매 5-bullet checklist agent. 매 mnemonic 이 매 LLM recall 에 유리.
**언제 X**: 매 단순 list — 매 5R force-fit 의 contrived.
## ❌ 안티패턴
- **Force-fit**: 매 4 또는 6 step 인데 매 5R 강제 → 매 awkward bucket.
- **Buzzword usage**: 매 "5R framework" 만 언급, 매 actual 5 step 의 unclear.
- **Cross-domain confusion**: 매 RAG 5R + Waste 5R 동일시 — 매 unrelated.
## 🧪 검증 / 중복
- Verified (Goble *Better Software Practices* 2014, Schramm communication model derivatives).
- 신뢰도 B+.
## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 5 variants + RAG 5R modern application |