---
id: wiki-2026-0508-codebase-onboarding
title: Codebase Onboarding
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Developer Onboarding, Code Onboarding, New Hire Ramp-up]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [engineering-management, devex, documentation, llm-tools, productivity]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python / TypeScript
  framework: Claude Code / Cursor / Sourcegraph
---

# 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
1. New hire ramp-up 의 5-day → 1.5-day 로 의 acceleration (LLM-assisted).
2. Acquisition integration — acquired team 의 codebase 의 onboard.
3. Open-source contributor 의 first-time contributor experience.
4. Inner-source — cross-team contribution friction 의 reduce.

## 💻 패턴

### CLAUDE.md / AGENTS.md (LLM context primer)
```markdown
# 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)
```makefile
.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)
```markdown
# 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
```python
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
```python
@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
```python
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 |
