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Antigravity Agent
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id: wiki-2026-0508-codebase-onboarding
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title: Codebase Onboarding
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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: [Developer Onboarding, Code Onboarding, New Hire Ramp-up]
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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: [engineering-management, devex, documentation, llm-tools, productivity]
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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 / TypeScript
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framework: Claude Code / Cursor / Sourcegraph
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---
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# Codebase Onboarding
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 4 phases
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- **Day 0–1: Environment** — repo clone, build, test 의 green
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- **Day 2–5: Map** — system architecture, ownership boundary, glossary
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- **Week 2: First PR** — small bug fix or doc 의 contribution
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- **Month 1: Ownership** — feature 의 lead, on-call participation
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### 매 friction sources (Microsoft 2024 study)
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- Tribal knowledge (60% of blockers)
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- Stale documentation (45%)
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- Build / dev-env setup (30%)
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- Implicit code conventions (28%)
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- Domain language gaps (25%)
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### 매 응용
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1. New hire ramp-up 의 5-day → 1.5-day 로 의 acceleration (LLM-assisted).
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2. Acquisition integration — acquired team 의 codebase 의 onboard.
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3. Open-source contributor 의 first-time contributor experience.
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4. Inner-source — cross-team contribution friction 의 reduce.
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## 💻 패턴
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### CLAUDE.md / AGENTS.md (LLM context primer)
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```markdown
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# Project Context
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## Stack
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- Backend: Python 3.13, FastAPI, Postgres 16, Redis 7
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- Frontend: Next.js 15 (App Router), React 19
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- Infra: AWS, Pulumi, GitHub Actions
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## Conventions
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- Async-first; no sync DB calls in handlers.
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- Tests: pytest, ≥85% coverage required.
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- Commits: conventional commits (feat/fix/chore).
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## Domain glossary
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- "Account" = billing entity (≠ User)
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- "Workspace" = collaboration scope
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- "Project" = single deployment unit
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## Key files
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- `apps/api/main.py` — FastAPI entry
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- `packages/db/schema.sql` — canonical schema
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- `infra/pulumi/` — IaC
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## Onboarding tasks
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1. Run `make bootstrap` then `make test`.
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2. Read `docs/architecture.md`.
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3. Pick a "good-first-issue" label.
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```
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### `make bootstrap` (one-command setup)
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```makefile
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.PHONY: bootstrap test lint
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bootstrap:
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@command -v mise >/dev/null || curl https://mise.run | sh
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mise install
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uv sync
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pnpm install
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docker compose up -d postgres redis
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uv run alembic upgrade head
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@echo "Bootstrap complete. Run 'make test' to verify."
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test:
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uv run pytest -x --cov=src
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pnpm test
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lint:
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uv run ruff check .
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pnpm lint
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```
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### Architecture Decision Records (ADR)
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```markdown
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# ADR-007: Why we chose Postgres over MongoDB
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Date: 2024-11-12
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Status: Accepted
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## Context
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We need transactional consistency for billing.
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## Decision
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Postgres 16 with row-level security.
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## Consequences
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+ Strong ACID for money flows
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- Schema migrations require Alembic discipline
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```
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### LLM-powered code map
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def codebase_summary(repo_files: list[str]) -> str:
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"""Generate onboarding-friendly codebase map using prompt cache."""
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response = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=4000,
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system=[
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{"type": "text", "text": "You are an onboarding assistant."},
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{"type": "text", "text": "\n".join(repo_files),
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"cache_control": {"type": "ephemeral"}},
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],
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messages=[{"role": "user", "content":
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"Produce a 1-page codebase map for a new hire: "
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"entry points, key modules, dependency layers, gotchas."}],
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)
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return response.content[0].text
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```
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### Buddy system + PR mentoring
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```python
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@dataclass
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class OnboardingPlan:
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new_hire: str
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buddy: str
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week1_tasks: list[str]
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week2_tasks: list[str]
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def daily_standup_questions(self) -> list[str]:
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return [
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"어제 의 가장 confusing 의 part?",
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"오늘 의 목표 의 1 of?",
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"block 의 의 의?",
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]
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```
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### "First PR by EOD-1" success metric
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```python
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def first_pr_metrics(hires: list[dict]) -> dict:
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"""Lead indicator of onboarding health."""
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return {
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"median_days_to_first_pr": median(h["days_to_first_pr"] for h in hires),
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"median_days_to_first_merge": median(h["days_to_first_merge"] for h in hires),
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"30d_active_pct": sum(h["still_active_30d"] for h in hires) / len(hires),
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}
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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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| Greenfield team | Heavy CLAUDE.md, light ADR |
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| Legacy codebase | Strong ADR archive, code map LLM, buddy system |
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| Open source | Detailed CONTRIBUTING.md, good-first-issue queue |
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| Acquired team | Pair programming weeks 1-2, glossary front-loaded |
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| Remote-first | Async docs first, video walkthroughs |
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**기본값**: modern team 의 default — CLAUDE.md + make bootstrap + buddy + first-PR-by-EOD-3.
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## 🔗 Graph
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- 부모: [[Team Culture & Onboarding (팀 문화 및 온보딩)]]
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- 변형: [[Program Comprehension Strategies]]
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- 응용: [[Pull_Request_and_Issue_Tracking]]
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- Adjacent: [[GIT_PROTOCOL]] · [[Process_Reflection_Template]]
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## 🤖 LLM 활용
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**언제**: codebase summary generation, onboarding doc 의 audit (gap detection), new-hire Q&A bot.
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**언제 X**: tribal knowledge 의 LLM 의 fully replace 의 X — buddy system 의 still 의 essential.
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## ❌ 안티패턴
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- **"Read the code"**: docs 의 absence 의 excuse 의 X. 매 entrypoint 의 explicit.
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- **Stale README**: bootstrap step 의 not-working 의 first-day blocker.
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- **Trial-by-fire**: 큰 critical 의 task 의 week-1 의 assign — burnout 의 amplify.
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- **Single buddy bottleneck**: buddy 의 vacation 의 의 onboarding 의 stall.
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## 🧪 검증 / 중복
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- Verified (Microsoft 2024 *Developer Velocity Lab*; Stripe *Increment Magazine* onboarding issue).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — phases, CLAUDE.md, bootstrap, ADR, LLM code map |
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