# Analytics Product

> Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

- **Type:** Skill
- **Install:** `agentstack add skill-lihongwei-cn-lihongwei-cn-analytics-product`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [LiHongwei-cn](https://agentstack.voostack.com/s/lihongwei-cn)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [LiHongwei-cn](https://github.com/LiHongwei-cn)
- **Source:** https://github.com/LiHongwei-cn/lihongwei-cn/tree/main/mundo-cloud/skills/aas-data-analytics/analytics-product
- **Website:** https://lihongwei-cn.github.io/lihongwei-cn/

## Install

```sh
agentstack add skill-lihongwei-cn-lihongwei-cn-analytics-product
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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
- 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%)   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 50% |
| W4 | 30% |
| W8 | 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  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.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [LiHongwei-cn](https://github.com/LiHongwei-cn)
- **Source:** [LiHongwei-cn/lihongwei-cn](https://github.com/LiHongwei-cn/lihongwei-cn)
- **License:** MIT
- **Homepage:** https://lihongwei-cn.github.io/lihongwei-cn/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-lihongwei-cn-lihongwei-cn-analytics-product
- Seller: https://agentstack.voostack.com/s/lihongwei-cn
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.