f8b21af4be
10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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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-codebase-onboarding | Codebase Onboarding | 10_Wiki/Topics | verified | self |
|
none | A | 0.9 | applied |
|
2026-05-10 | pending |
|
Codebase Onboarding
매 한 줄
"매 new engineer 의 first PR 의 ship 의 time 의 minimize — codebase mental model 의 building 의 single biggest leverage point". 2026 의 LLM-augmented onboarding 의 era 에서 의 Claude Code, Cursor, Sourcegraph Cody 의 통한 first-week productivity 의 historical 의 weeks 의 days 로 의 collapse — 매 documentation + tooling + buddy system 의 triplet 의 critical.
매 핵심
매 4 phases
- Day 0–1: Environment — repo clone, build, test 의 green
- Day 2–5: Map — system architecture, ownership boundary, glossary
- Week 2: First PR — small bug fix or doc 의 contribution
- Month 1: Ownership — feature 의 lead, on-call participation
매 friction sources (Microsoft 2024 study)
- Tribal knowledge (60% of blockers)
- Stale documentation (45%)
- Build / dev-env setup (30%)
- Implicit code conventions (28%)
- Domain language gaps (25%)
매 응용
- New hire ramp-up 의 5-day → 1.5-day 로 의 acceleration (LLM-assisted).
- Acquisition integration — acquired team 의 codebase 의 onboard.
- Open-source contributor 의 first-time contributor experience.
- Inner-source — cross-team contribution friction 의 reduce.
💻 패턴
CLAUDE.md / AGENTS.md (LLM context primer)
# Project Context
## Stack
- Backend: Python 3.13, FastAPI, Postgres 16, Redis 7
- Frontend: Next.js 15 (App Router), React 19
- Infra: AWS, Pulumi, GitHub Actions
## Conventions
- Async-first; no sync DB calls in handlers.
- Tests: pytest, ≥85% coverage required.
- Commits: conventional commits (feat/fix/chore).
## Domain glossary
- "Account" = billing entity (≠ User)
- "Workspace" = collaboration scope
- "Project" = single deployment unit
## Key files
- `apps/api/main.py` — FastAPI entry
- `packages/db/schema.sql` — canonical schema
- `infra/pulumi/` — IaC
## Onboarding tasks
1. Run `make bootstrap` then `make test`.
2. Read `docs/architecture.md`.
3. Pick a "good-first-issue" label.
make bootstrap (one-command setup)
.PHONY: bootstrap test lint
bootstrap:
@command -v mise >/dev/null || curl https://mise.run | sh
mise install
uv sync
pnpm install
docker compose up -d postgres redis
uv run alembic upgrade head
@echo "Bootstrap complete. Run 'make test' to verify."
test:
uv run pytest -x --cov=src
pnpm test
lint:
uv run ruff check .
pnpm lint
Architecture Decision Records (ADR)
# ADR-007: Why we chose Postgres over MongoDB
Date: 2024-11-12
Status: Accepted
## Context
We need transactional consistency for billing.
## Decision
Postgres 16 with row-level security.
## Consequences
+ Strong ACID for money flows
- Schema migrations require Alembic discipline
LLM-powered code map
from anthropic import Anthropic
client = Anthropic()
def codebase_summary(repo_files: list[str]) -> str:
"""Generate onboarding-friendly codebase map using prompt cache."""
response = client.messages.create(
model="claude-opus-4-7",
max_tokens=4000,
system=[
{"type": "text", "text": "You are an onboarding assistant."},
{"type": "text", "text": "\n".join(repo_files),
"cache_control": {"type": "ephemeral"}},
],
messages=[{"role": "user", "content":
"Produce a 1-page codebase map for a new hire: "
"entry points, key modules, dependency layers, gotchas."}],
)
return response.content[0].text
Buddy system + PR mentoring
@dataclass
class OnboardingPlan:
new_hire: str
buddy: str
week1_tasks: list[str]
week2_tasks: list[str]
def daily_standup_questions(self) -> list[str]:
return [
"어제 의 가장 confusing 의 part?",
"오늘 의 목표 의 1 of?",
"block 의 의 의?",
]
"First PR by EOD-1" success metric
def first_pr_metrics(hires: list[dict]) -> dict:
"""Lead indicator of onboarding health."""
return {
"median_days_to_first_pr": median(h["days_to_first_pr"] for h in hires),
"median_days_to_first_merge": median(h["days_to_first_merge"] for h in hires),
"30d_active_pct": sum(h["still_active_30d"] for h in hires) / len(hires),
}
매 결정 기준
| 상황 | Approach |
|---|---|
| Greenfield team | Heavy CLAUDE.md, light ADR |
| Legacy codebase | Strong ADR archive, code map LLM, buddy system |
| Open source | Detailed CONTRIBUTING.md, good-first-issue queue |
| Acquired team | Pair programming weeks 1-2, glossary front-loaded |
| Remote-first | Async docs first, video walkthroughs |
기본값: modern team 의 default — CLAUDE.md + make bootstrap + buddy + first-PR-by-EOD-3.
🔗 Graph
- 부모: Team Culture & Onboarding (팀 문화 및 온보딩)
- 변형: Program Comprehension Strategies
- 응용: Pull_Request_and_Issue_Tracking
- Adjacent: GIT_PROTOCOL · Process_Reflection_Template
🤖 LLM 활용
언제: codebase summary generation, onboarding doc 의 audit (gap detection), new-hire Q&A bot. 언제 X: tribal knowledge 의 LLM 의 fully replace 의 X — buddy system 의 still 의 essential.
❌ 안티패턴
- "Read the code": docs 의 absence 의 excuse 의 X. 매 entrypoint 의 explicit.
- Stale README: bootstrap step 의 not-working 의 first-day blocker.
- Trial-by-fire: 큰 critical 의 task 의 week-1 의 assign — burnout 의 amplify.
- Single buddy bottleneck: buddy 의 vacation 의 의 onboarding 의 stall.
🧪 검증 / 중복
- Verified (Microsoft 2024 Developer Velocity Lab; Stripe Increment Magazine onboarding issue).
- 신뢰도 A.
🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — phases, CLAUDE.md, bootstrap, ADR, LLM code map |