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Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
One skill from agentic-awesome-skills .
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Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. 📦 Ships with agentic-awesome-skills
</> SKILL.md
analytics-product.SKILL.md
---
name : analytics-product
description : "Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto."
risk : none
source : community
date_added : '2026-03-06'
author : renat
tags:
- analytics
- product
- metrics
- posthog
- mixpanel
tools:
- claude-code
- antigravity
- cursor
- gemini-cli
- codex-cli
---
# ANALYTICS-PRODUCT — Decida com Dados
## Overview
Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. Ativar para: configurar tracking de eventos, criar funil de conversao, analise de cohort, retencao, DAU/MAU, feature flags, A/B testing, north star metric, OKRs, dashboard de produto.
## When to Use This Skill
- When you need specialized assistance with this domain
## Do Not Use This Skill When
- The task is unrelated to analytics product
- A simpler, more specific tool can handle the request
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
- The user needs general-purpose assistance without domain expertise
## How It Works
```
[objeto]_[verbo_passado]
Correto: user_signed_up, conversation_started, upgrade_completed
Errado: signup, click, conversion
```
## Analytics-Product — Decida Com Dados
> "In God we trust. All others must bring data." — W. Edwards Deming
---
## Eventos Essenciais Da Auri
```python
AURI_EVENTS = {
# Aquisicao
"user_signed_up": {"props": ["source", "medium", "campaign"]},
"onboarding_started": {"props": ["step_count"]},
"onboarding_completed": {"props": ["time_to_complete", "steps_skipped"]},
# Ativacao
"first_conversation": {"props": ["intent", "response_time"]},
"aha_moment_reached": {"props": ["trigger", "session_number"]},
"feature_discovered": {"props": ["feature_name", "discovery_method"]},
# Retencao
"conversation_started": {"props": ["intent", "user_tier", "device"]},
"conversation_completed":{"props": ["messages_count", "duration", "rating"]},
"session_started": {"props": ["days_since_last", "platform"]},
# Receita
"upgrade_viewed": {"props": ["trigger", "current_tier"]},
"upgrade_started": {"props": ["target_tier", "trigger"]},
"upgrade_completed": {"props": ["tier", "plan", "revenue"]},
"subscription_canceled": {"props": ["reason", "tier", "tenure_days"]},
"payment_failed": {"props": ["attempt_count", "error_code"]},
}
```
## Implementacao Posthog (Python)
```python
from posthog import Posthog
import os
posthog = Posthog(
project_api_key=os.environ["POSTHOG_API_KEY"],
host=os.environ.get("POSTHOG_HOST", "https://app.posthog.com")
)
def track(user_id: str, event: str, properties: dict = None):
posthog.capture(
distinct_id=user_id,
event=event,
properties=properties or {}
)
def identify(user_id: str, traits: dict):
posthog.identify(
distinct_id=user_id,
properties=traits
)
## Uso:
track("user_123", "conversation_started", {
"intent": "business_advice",
"device": "alexa",
"user_tier": "pro"
})
```
---
## Funil De Ativacao Auri
```
Visita landing page (100%)
| [meta: 40%]
Clicou "Experimentar" (40%)
| [meta: 70%]
Completou cadastro (28%)
| [meta: 60%]
Fez primeira conversa (17%) <- AHA MOMENT
| [meta: 50%]
Voltou no dia seguinte (8.5%)
| [meta: 40%]
Usou 3+ dias na semana (3.4%)
| [meta: 20%]
Converteu para Pro (0.7%)
```
## Otimizando O Funil
```
Para cada drop-off > benchmark:
1. Identificar: onde exatamente o usuario sai?
2. Entender: por que? (session recordings, surveys)
3. Hipotese: qual mudanca poderia melhorar?
4. Testar: A/B test com amostra estatisticamente significante
5. Medir: 2 semanas minimo, p-value < 0.05
6. Aprender: mesmo se falhar, entende-se o usuario melhor
```
---
## Analise De Cohort (Retencao Semanal)
```python
def calculate_cohort_retention(events_df):
"""
events_df: DataFrame com colunas [user_id, event_date, event_name]
Retorna: matriz de retencao [cohort_week x week_number]
"""
import pandas as pd
first_session = events_df[events_df.event_name == "session_started"] \
.groupby("user_id")["event_date"].min() \
.dt.to_period("W")
sessions = events_df[events_df.event_name == "session_started"].copy()
sessions["cohort"] = sessions["user_id"].map(first_session)
sessions["weeks_since"] = (
sessions["event_date"].dt.to_period("W") - sessions["cohort"]
).apply(lambda x: x.n)
cohort_data = sessions.groupby(["cohort", "weeks_since"])["user_id"].nunique()
cohort_sizes = cohort_data.unstack().iloc[:, 0]
retention = cohort_data.unstack().divide(cohort_sizes, axis=0) * 100
return retention
```
## Benchmarks De Retencao (Assistentes De Voz)
| Semana | Pessimo | Ok | Bom | Excelente |
|--------|---------|-----|-----|-----------|
| W1 | <20% | 20-35% | 35-50% | >50% |
| W4 | <10% | 10-20% | 20-30% | >30% |
| W8 | <5% | 5-12% | 12-20% | >20% |
---
## Definindo A North Star Da Auri
```
Framework:
1. O que cria valor real para o usuario? -> Conversas que geram insight/acao
2. O que prediz crescimento de longo prazo? -> Usuarios com 3+ conv/semana
3. Como medir? -> "Weekly Active Conversationalists" (WAC)
North Star: WAC (Weekly Active Conversationalists)
Definicao: Usuarios com >= 3 conversas na semana que duraram >= 2 minutos
Meta Ano 1: 10.000 WAC
Meta Ano 2: 100.000 WAC
```
## Dashboard North Star
```python
def calculate_north_star(db):
wac = db.query("""
SELECT COUNT(DISTINCT user_id) as wac
FROM conversations
WHERE
created_at >= NOW() - INTERVAL '7 days'
AND duration_seconds >= 120
GROUP BY user_id
HAVING COUNT(*) >= 3
""").scalar()
return {
"wac": wac,
"wow_growth": calculate_wow_growth(db, "wac"),
"target": 10000,
"progress": f"{wac/10000*100:.1f}%"
}
```
---
## Feature Flags Com Posthog
```python
def is_feature_enabled(user_id: str, feature: str) -> bool:
return posthog.feature_enabled(feature, user_id)
if is_feature_enabled(user_id, "new-onboarding-v2"):
show_new_onboarding()
else:
show_old_onboarding()
```
## Calculadora De Significancia Estatistica
```python
from scipy import stats
import numpy as np
def ab_test_significance(
control_conversions: int,
control_visitors: int,
variant_conversions: int,
variant_visitors: int,
confidence: float = 0.95
) -> dict:
control_rate = control_conversions / control_visitors
variant_rate = variant_conversions / variant_visitors
lift = (variant_rate - control_rate) / control_rate * 100
_, p_value = stats.chi2_contingency([
[control_conversions, control_visitors - control_conversions],
[variant_conversions, variant_visitors - variant_conversions]
])[:2]
significant = p_value < (1 - confidence)
return {
"control_rate": f"{control_rate*100:.2f}%",
"variant_rate": f"{variant_rate*100:.2f}%",
"lift": f"{lift:+.1f}%",
"p_value": round(p_value, 4),
"significant": significant,
"recommendation": "Deploy variant" if significant and lift > 0 else "Keep control"
}
```
---
## 6. Comandos
| Comando | Acao |
|---------|------|
| `/event-taxonomy` | Define taxonomia de eventos |
| `/funnel-analysis` | Analisa funil de conversao |
| `/cohort-retention` | Calcula retencao por cohort |
| `/north-star` | Define ou revisa North Star Metric |
| `/ab-test` | Calcula significancia de A/B test |
| `/dashboard-setup` | Cria dashboard de produto |
| `/okr-template` | Template de OKRs para produto |
## Best Practices
- Provide clear, specific context about your project and requirements
- Review all suggestions before applying them to production code
- Combine with other complementary skills for comprehensive analysis
## Common Pitfalls
- Using this skill for tasks outside its domain expertise
- Applying recommendations without understanding your specific context
- Not providing enough project context for accurate analysis
## Related Skills
- `growth-engine` - Complementary skill for enhanced analysis
- `monetization` - Complementary skill for enhanced analysis
- `product-design` - Complementary skill for enhanced analysis
- `product-inventor` - Complementary skill for enhanced analysis
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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