Install
$ agentstack add skill-lihongwei-cn-lihongwei-cn-analytics-product ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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
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)
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:
- O que cria valor real para o usuario? -> Conversas que geram insight/acao
- O que prediz crescimento de longo prazo? -> Usuarios com 3+ conv/semana
- 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
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
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 analysismonetization- Complementary skill for enhanced analysisproduct-design- Complementary skill for enhanced analysisproduct-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
- Source: 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.
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Versions
- v0.1.0 Imported from the upstream source.