refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-g-stack-principles
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title: G-Stack Principles
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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: [G-Stack, growth stack, growth engineering, growth marketing]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.82
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verification_status: applied
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tags: [growth, growth-stack, marketing, analytics, experimentation]
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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: TypeScript / Python
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framework: GA4 / Mixpanel / GrowthBook / Amplitude
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---
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# G-Stack Principles (Growth Stack)
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## 매 한 줄
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> **"매 acquisition + activation + retention + revenue + referral 의 의 의 의 modern data + tooling stack"**. 매 product-led growth (PLG), 매 experimentation, 매 personalization. 매 stack: GA4 + Mixpanel/Amplitude + GrowthBook + Segment + Customer.io.
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## 매 핵심
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### 매 AARRR (Pirate metrics)
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- **Acquisition**.
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- **Activation**: 매 'aha' moment.
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- **Retention**: 매 D1, D7, D30.
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- **Revenue**.
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- **Referral**.
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### 매 stack tier
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- **Analytics**: GA4, Mixpanel, Amplitude, PostHog.
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- **CDP**: Segment, RudderStack.
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- **Experimentation**: GrowthBook, Optimizely, LaunchDarkly.
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- **Marketing automation**: Customer.io, Braze, Iterable.
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- **CRM**: Salesforce, HubSpot.
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- **Reverse ETL**: Hightouch, Census.
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- **Warehouse**: Snowflake, BigQuery.
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### 매 응용
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1. **Onboarding optimize**.
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2. **Retention email / push**.
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3. **Pricing test**.
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4. **Feature flag rollout**.
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5. **Personalization**.
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## 💻 패턴
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### Event tracking (Segment)
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```typescript
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import { Analytics } from '@segment/analytics-next';
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const analytics = new Analytics({ writeKey });
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analytics.track('Sign Up Completed', {
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plan: 'pro',
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source: 'landing',
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});
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```
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### A/B test (GrowthBook)
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```typescript
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import { GrowthBook } from '@growthbook/growthbook';
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const gb = new GrowthBook({ apiHost, clientKey });
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await gb.loadFeatures();
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gb.setAttributes({ id: userId, country });
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if (gb.isOn('new_pricing_page')) showNewPricing();
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const variation = gb.getValue('hero_headline', 'default');
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```
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### Funnel analysis (Mixpanel)
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```python
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import mixpanel
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mp = mixpanel.Mixpanel(token)
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mp.track(distinct_id, 'Funnel Step', {'step': 1, 'name': 'view_landing'})
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```
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### Cohort retention
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```python
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import pandas as pd
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def retention_curve(events_df):
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events_df['cohort'] = events_df.groupby('user_id').date.transform('min').dt.to_period('M')
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events_df['period'] = (events_df.date.dt.to_period('M') - events_df.cohort).apply(lambda x: x.n)
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return events_df.groupby(['cohort', 'period']).user_id.nunique().unstack()
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```
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### Activation prediction
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```python
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import xgboost as xgb
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def predict_activation(user_features):
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"""매 user 의 의 의 activate?"""
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return xgb.XGBClassifier().fit(X_train, y_train).predict_proba(user_features)[:, 1]
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```
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### Segment + reverse ETL
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```typescript
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// 매 warehouse → tool sync
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// 매 Hightouch config:
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{
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source: 'snowflake',
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query: 'SELECT email, ltv FROM users WHERE ltv > 1000',
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destination: 'customer_io',
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mode: 'upsert',
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on: 'email',
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}
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```
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### Email automation (Customer.io)
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```python
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import requests
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requests.post(f'https://api.customer.io/v1/campaigns/{cid}/triggers',
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json={'recipients': {'segment': {'id': 1}}, 'data': {'name': '{{first_name}}'}})
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```
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### Cohort-based feature flag
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```typescript
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gb.setAttributes({
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id: user.id,
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cohort: user.cohort,
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isPro: user.plan === 'pro',
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});
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if (gb.isOn('new_dashboard')) showNewDashboard();
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```
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### LTV prediction
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```python
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def predict_ltv(user_features):
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"""매 first 30-day signal → 매 12-month LTV."""
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return xgb.XGBRegressor().fit(X_train, y_train).predict(user_features)
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```
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### Churn prediction
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```python
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def churn_probability(user_features):
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return classifier.predict_proba(user_features)[:, 1]
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def trigger_winback(users):
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high_churn = users[churn_probability(user_features) > 0.7]
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customer_io.send_campaign('winback', high_churn)
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```
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### Multi-touch attribution
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```python
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def first_touch_attribution(touchpoints):
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return touchpoints[0]
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def linear_attribution(touchpoints):
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return [(tp, 1/len(touchpoints)) for tp in touchpoints]
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def time_decay_attribution(touchpoints, half_life_days=7):
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weights = [0.5 ** ((now - tp.date).days / half_life_days) for tp in touchpoints]
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total = sum(weights)
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return [(tp, w/total) for tp, w in zip(touchpoints, weights)]
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```
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### Eval metric
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```python
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def growth_metrics(users, period_days=30):
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return {
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'D1_retention': retention(users, day=1),
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'D7_retention': retention(users, day=7),
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'D30_retention': retention(users, day=30),
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'avg_LTV': mean([u.ltv for u in users]),
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'CAC': sum(u.acquisition_cost for u in users) / len(users),
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'NRR': net_revenue_retention(users, period_days),
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}
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```
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### Onboarding flow optimize
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```python
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def onboarding_funnel(events):
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steps = ['signup', 'verify_email', 'create_first_project', 'invite_teammate', 'first_action']
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drop_offs = []
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for i in range(1, len(steps)):
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rate = events[steps[i]].nunique() / events[steps[i-1]].nunique()
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drop_offs.append((steps[i-1], steps[i], 1 - rate))
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return drop_offs
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```
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## 매 결정 기준
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| 상황 | Tool |
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|---|---|
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| Product analytics | Mixpanel / Amplitude / PostHog |
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| Pipeline | Segment + warehouse + Hightouch |
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| Experimentation | GrowthBook / Optimizely |
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| Email | Customer.io / Braze |
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| Open-source | PostHog + GrowthBook |
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| Enterprise | Amplitude + LaunchDarkly + Braze |
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**기본값**: 매 Segment + 매 warehouse + 매 PostHog/Amplitude + 매 GrowthBook + 매 Customer.io + 매 cohort retention focus.
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## 🔗 Graph
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- 응용: [[E-commerce-Optimization]] · [[Dynamic-Creative-Optimization]]
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- Adjacent: [[Feature-Flag]]
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## 🤖 LLM 활용
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**언제**: 매 SaaS / consumer product. 매 PLG.
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**언제 X**: 매 enterprise sales-led only.
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## ❌ 안티패턴
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- **Vanity metrics**: 매 page views, signups.
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- **No experimentation discipline**: 매 noise as truth.
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- **Tool sprawl**: 매 5+ overlapping.
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- **No cohort analysis**: 매 average misleading.
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## 🧪 검증 / 중복
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- Verified (Reforge, GrowthBook docs, Segment docs, Pirate metrics McClure).
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- 신뢰도 B.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — AARRR + 매 Segment / GrowthBook / cohort / LTV / churn code |
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