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Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
| id | title | category | status | canonical_id | aliases | duplicate_of | source_trust_level | confidence_score | verification_status | tags | raw_sources | last_reinforced | github_commit | tech_stack | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| wiki-2026-0508-horizontal-and-vertical-logic | Horizontal and Vertical Logic | 10_Wiki/Topics | verified | self |
|
none | A | 0.9 | applied |
|
2026-05-10 | pending |
|
Horizontal and Vertical Logic
매 한 줄
"매 vertical logic = parent ↔ children Q&A coherence; horizontal logic = sibling MECE coherence". 매 1973 Barbara Minto (McKinsey) 의 Pyramid Principle 의 dual axis. 매 2026 의 LLM-assisted argument structuring, executive-summary generation, audit findings 의 modern instances.
매 핵심
매 Vertical logic (Q&A 추적)
- Top-down: 매 main idea → child supports through Why?/How?/What?
- Bottom-up: 매 children 의 grouping → parent emergence.
- Test: 매 each child 의 "answers a Q raised by parent" 의 verify.
매 Horizontal logic (sibling coherence)
- MECE: 매 mutually exclusive + collectively exhaustive.
- Inductive: 매 same-type observations → conclusion.
- Deductive: 매 premise 1 + premise 2 → conclusion (max 4 levels).
- Test: 매 sibling reorder 시 meaning preserved + no overlap + complete.
매 SCQA (Situation-Complication-Question-Answer)
- Situation: 매 audience-known background.
- Complication: 매 disrupting force.
- Question: 매 implicit reader question.
- Answer: 매 main idea (top of pyramid).
매 응용
- McKinsey/BCG client deck.
- Executive memo.
- Audit / financial reporting.
- Engineering RFC.
- LLM 의 reasoning trace structuring.
💻 패턴
Pyramid node tree
from dataclasses import dataclass, field
from typing import Literal
@dataclass
class PyramidNode:
statement: str
logic_type: Literal["inductive", "deductive"] = "inductive"
children: list["PyramidNode"] = field(default_factory=list)
def vertical_test(self) -> bool:
"""Each child must answer Why/How/What raised by self."""
return all(self.statement and c.statement for c in self.children)
def horizontal_test(self) -> bool:
"""MECE: same logical category, no overlap (heuristic check)."""
return len({type(c.statement) for c in self.children}) == 1
MECE category check
def is_mece(items: list[str], categories: dict[str, set[str]]) -> dict:
covered = set().union(*categories.values())
overlaps = [
(a, b) for a in categories for b in categories
if a != b and categories[a] & categories[b]
]
return {
"exhaustive": set(items) <= covered,
"exclusive": not overlaps,
"uncovered": set(items) - covered,
"overlaps": overlaps,
}
Inductive vs deductive selector
def choose_argument_form(num_premises: int, audience_familiarity: float) -> str:
"""Minto: deductive ≤4 levels, only when audience already accepts premises."""
if num_premises <= 3 and audience_familiarity > 0.7:
return "deductive"
return "inductive" # safer default — group similar evidence
SCQA scaffolder
def scqa(situation: str, complication: str, question: str, answer: str) -> str:
return (f"Situation: {situation}\n"
f"Complication: {complication}\n"
f"Question: {question}\n"
f"Answer (main idea): {answer}")
Reorder sibling test (horizontal robustness)
import itertools
def reorder_robust(siblings: list[str], judge_meaning: callable) -> bool:
"""If meaning unchanged across permutations → horizontal logic holds."""
perms = list(itertools.permutations(siblings))[:6]
meanings = {judge_meaning(p) for p in perms}
return len(meanings) == 1
LLM critique pass
from anthropic import Anthropic
client = Anthropic()
def minto_critique(pyramid_yaml: str) -> str:
return client.messages.create(
model="claude-opus-4-7",
max_tokens=2000,
system=("Audit a Minto pyramid. Flag: (1) children that don't answer the "
"Q raised by parent, (2) sibling overlaps (not ME), (3) gaps (not CE), "
"(4) deductive chains beyond 4 levels."),
messages=[{"role": "user", "content": pyramid_yaml}],
).content[0].text
매 결정 기준
| 상황 | Approach |
|---|---|
| Executive deck | top-down + SCQA opener |
| Bottom-up synthesis | group findings → emerge top |
| Diagnostic argument | deductive (≤4 levels) |
| Survey / audit findings | inductive (group similar) |
| Confused audience | start with main idea (top) |
기본값: 매 top-down 의 vertical structure + inductive 의 horizontal grouping. 매 SCQA 의 opening.
🔗 Graph
- 부모: Pyramid Principle
- 변형: Horizontal and Vertical Logic (alias) · (alias) · MECE
- Adjacent: SCQA · Issue Tree
🤖 LLM 활용
언제: 매 draft pyramid 의 critique, 매 MECE gap 의 surface, 매 SCQA 의 opening 작성, 매 inductive grouping 의 candidate. 언제 X: 매 final audience-specific framing — 매 cultural / political nuance, 매 stakeholder dynamics 의 human judgment 필수.
❌ 안티패턴
- Bottom of pyramid 부터 발표: 매 audience 의 main idea 도달 전 fatigue.
- Mixed inductive + deductive 의 same level: 매 horizontal coherence 깨짐.
- 5+ siblings: 매 cognitive overload — 매 7±2 의 lower bound (3-5).
- MECE 만 의 추구: 매 forced taxonomy 의 distortion — 매 90% 의 MECE 의 sometimes acceptable.
- Deductive 의 5+ levels: 매 reader cognitive load 폭증 — 매 Minto 의 4-level cap.
🧪 검증 / 중복
- Verified (Minto "The Pyramid Principle" 3rd ed, McKinsey communication training, Booz Allen 의 SCQA).
- 신뢰도 A.
🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — vertical/horizontal axes, MECE, SCQA, Minto pyramid 패턴 추가 |