ANALYTICS-PRODUCT — Decida com Dados

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/analytics-product

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

ANALYTICS-PRODUCT — Decida com Dados

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: How It Works, Analytics-Product — Decida Com Dados, Eventos Essenciais Da Auri, Implementacao Posthog (Python), Uso:, Funil De Ativacao Auri.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• When you need specialized assistance with this domain
• 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
• Use when the request clearly matches the imported source intent: Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: 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.

Imported: How It Works

[objeto]_[verbo_passado]

Correto:   user_signed_up, conversation_started, upgrade_completed
Errado:    signup, click, conversion

Examples

Example 1: Ask for the upstream workflow directly

Use @analytics-product-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @analytics-product-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @analytics-product-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @analytics-product-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.

Imported Operating Notes

Imported: 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/analytics-product

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @10-andruia-skill-smith-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @20-andruia-niche-intelligence-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @2d-games

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Analytics-Product — Decida Com Dados

"In God we trust. All others must bring data." — W. Edwards Deming

Imported: 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"]},
}

Imported: 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
    )

#### Imported: Uso:

track("user_123", "conversation_started", {
    "intent": "business_advice",
    "device": "alexa",
    "user_tier": "pro"
})

Imported: 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%)

Imported: 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

Imported: Analise De Cohort (Retencao Semanal)

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

Imported: Benchmarks De Retencao (Assistentes De Voz)

Imported: 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

Imported: Dashboard North Star

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}%"
    }

Imported: 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()

Imported: 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 < (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"
    }

Imported: 6. Comandos

/event-taxonomy

/funnel-analysis

/cohort-retention

/north-star

/ab-test

/dashboard-setup

/okr-template

Imported: 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

Imported: 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.