2nd/10_Wiki/Topic_General/General Knowledge

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit
wiki-2026-0508-readme	README — General Knowledge	10_Wiki/Topics	verified	self	P-REINFORCE-AUTO-698D8B	none	A	0.95	applied	readme index meta		2026-05-10	pending

README — General Knowledge

매 한 줄

"매 cross-domain knowledge 의 hub". 매 General Knowledge folder 는 narrowly-scoped 도메인에 fit 하지 않은 wiki note 의 catch-all index — game design, web platform, ML theory, neuroscience 가 cross-pollinate 한다.

매 핵심

매 폴더 목적

• 매 cross-domain note 의 home — 매 specific topic folder (AI_and_ML, Programming) 에 fit 하지 않은 entry.
• 매 case study + concept primer 의 mix.
• 매 canonical 문서 + redirect 문서 의 coexist.

매 분류 체계

• 매 status: verified (canonical), duplicate (redirect), merged (filename-level redirect), needs_review (pending cleanup).
• 매 canonical_id: self (own canonical) or external canonical slug.
• 매 frontmatter 의 일관된 schema — id, title, category, status, canonical_id, aliases, source_trust_level.

매 응용

1. Game design knowledge base — Albion Online, Clash Royale, Diablo 2 의 case study.
2. Web platform primer — OffscreenCanvas, SharedArrayBuffer 의 깊이 있는 reference.
3. Cognitive science index — Dopamine Signaling, Mycological Horror 의 cross-cut topic.

💻 패턴

패턴 1: Frontmatter linting

import yaml
import frontmatter
from pathlib import Path

REQUIRED = {"id", "title", "category", "status", "canonical_id"}

def lint_folder(folder: Path):
    issues = []
    for md in folder.glob("*.md"):
        post = frontmatter.load(md)
        missing = REQUIRED - set(post.metadata.keys())
        if missing:
            issues.append((md.name, f"missing: {missing}"))
    return issues

for name, issue in lint_folder(Path("./General Knowledge")):
    print(f"{name}: {issue}")

패턴 2: Duplicate detection (title similarity)

from rapidfuzz import fuzz
from pathlib import Path
import frontmatter

def find_dupes(folder: Path, threshold=85):
    titles = []
    for md in folder.glob("*.md"):
        post = frontmatter.load(md)
        titles.append((md.name, post.metadata.get("title", "")))

    pairs = []
    for i, (n1, t1) in enumerate(titles):
        for n2, t2 in titles[i+1:]:
            score = fuzz.ratio(t1, t2)
            if score >= threshold:
                pairs.append((n1, n2, score))
    return pairs

패턴 3: Wikilink graph build

import re
import networkx as nx
from pathlib import Path

LINK_RE = re.compile(r"\[\[([^\]]+)\]\]")

def build_graph(folder: Path) -> nx.DiGraph:
    g = nx.DiGraph()
    for md in folder.glob("*.md"):
        text = md.read_text()
        for target in LINK_RE.findall(text):
            g.add_edge(md.stem, target.split("|")[0])
    return g

g = build_graph(Path("./General Knowledge"))
print(f"nodes={g.number_of_nodes()} edges={g.number_of_edges()}")
print("orphans:", [n for n in g.nodes if g.in_degree(n) == 0])

패턴 4: Redirect resolution

def resolve(slug: str, index: dict[str, dict]) -> str:
    seen = set()
    cur = slug
    while cur in index and index[cur].get("status") in ("duplicate", "merged"):
        if cur in seen:
            raise ValueError(f"redirect cycle at {cur}")
        seen.add(cur)
        cur = index[cur].get("canonical_id") or index[cur].get("redirect_to")
    return cur

패턴 5: Reinforcement scheduler

from datetime import date, timedelta

def needs_reinforcement(meta: dict, today: date = date.today()) -> bool:
    last = date.fromisoformat(meta["last_reinforced"])
    score = float(meta.get("confidence_score", 0.9))
    interval = timedelta(days=30 if score >= 0.9 else 14)
    return today - last > interval

매 결정 기준

상황	Approach
새 note 의 fit folder 가 명확	specific folder 에 add (not General Knowledge)
cross-domain note	General Knowledge
Korean title duplicate	REDIRECT to English canonical
stub / placeholder	redirect to README

기본값: domain-specific folder 우선, fallback 만 General Knowledge.

🔗 Graph

• 부모: Wiki Index · 10_Wiki/Topics
• 변형: AI_and_ML/README · Programming & Language/README

🤖 LLM 활용

언제: cross-domain question 의 routing, knowledge graph 구축, reinforcement scheduling. 언제 X: 매 specific domain 의 deep query — domain folder 의 직접 lookup 우선.

❌ 안티패턴

• Catch-all dumping: 매 note 가 specific folder 의 candidate 인데 General Knowledge 에 dump — graph 의 fragmentation.
• Redirect chain: 매 redirect → redirect → canonical 의 multi-hop. 매 single-hop 으로 flatten.
• Stale frontmatter: 매 last_reinforced 의 90+일 미갱신 — reinforcement loop 의 break.

🧪 검증 / 중복

• Verified (folder ls + frontmatter lint).
• 신뢰도 A (meta-doc, self-describing).

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — README 의 substantive content 화