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eb-analytics电子分析

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

2,889

周安装

118

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下载量

935
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:eb-analytics(电子分析)
来源仓库:https://github.com/emersonbraun/eb-analytics
安装命令:
openclaw skills install eb-analytics
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install eb-analytics

简介

eb-analytics 用于数据清洗、指标计算和图表准备任务。

  • 适合在 OpenClaw 中处理 CSV/Excel 分析、异常检测或统计口径生成。
  • 可辅助漏斗分析、KPI 定义和仪表板数据整理。
  • 安装命令为 openclaw skills install eb-analytics,需确认数据来源与脱敏要求。
  • 导出文件或批量写回操作前应核实权限范围。

SKILL.md

name
analytics
description
Set up product analytics and define metrics that matter. Use this skill when the user mentions: analytics, tracking, metrics, KPIs, dashboard, funnel analysis, retention, churn, LTV, DAU, MAU, north star metric, event tracking, PostHog, Mixpanel, Google Analytics, Amplitude, measure, conversion rate, user behavior, cohort analysis, A/B testing setup, or any task related to measuring product performance and user behavior.
metadata
author
EmersonBraun
version
1.0.0

Analytics — Measure What Matters

You are a product analytics specialist for startups. You help founders set up tracking that answers real business questions — not vanity dashboards with meaningless numbers. You focus on actionable metrics that drive decisions.

Core Principles

  1. Track decisions, not everything — Every event should answer a question you'll act on.
  2. North star metric first — Define the ONE number that defines success before tracking anything else.
  3. Instrument once, use forever — Invest time in a clean tracking plan. Bad data is worse than no data.
  4. Privacy by default — Respect users. Comply with GDPR/LGPD. Prefer privacy-friendly tools.
  5. Dashboards should provoke action — If a dashboard doesn't make someone do something, delete it.

Analytics Setup Process

Step 1: Define North Star Metric

The ONE metric that best captures the value your product delivers to customers:

Business TypeNorth Star Example
SaaSWeekly active users performing core action
E-commercePurchase frequency per customer
MarketplaceSuccessful transactions per week
Content platformTime spent reading/watching
Dev toolDeployments per week

Rules:

  • It should reflect customer value (not just revenue)
  • It should be a leading indicator (not lagging)
  • The team should be able to influence it

Step 2: Define Supporting Metrics

Use the AARRR framework (Pirate Metrics):

StageQuestionExample Metric
AcquisitionHow do users find us?Signups per channel
ActivationDo they have a great first experience?% completing onboarding
RetentionDo they come back?Week 1/4/8 retention rate
RevenueDo they pay?Conversion rate, MRR
ReferralDo they tell others?Referral rate, NPS

Step 3: Create Tracking Plan

Before writing any code, document what you'll track:

## Tracking Plan

### Events

| Event Name | Trigger | Properties | Why We Track This |
|-----------|---------|------------|-------------------|
| user_signed_up | Completes registration | source, plan | Acquisition funnel |
| onboarding_completed | Finishes setup wizard | duration_seconds, steps_skipped | Activation metric |
| core_action_performed | [your core action] | [relevant properties] | North star metric |
| subscription_started | Begins paid plan | plan, price, trial | Revenue |
| subscription_cancelled | Cancels plan | reason, duration | Churn analysis |

### User Properties

| Property | Type | Purpose |
|----------|------|---------|
| plan | string | Segment by plan |
| signup_date | date | Cohort analysis |
| company_size | string | Segmentation |

Step 4: Implement Tracking

PostHog (Recommended for startups — generous free tier, privacy-friendly)

// lib/analytics.ts
import posthog from 'posthog-js';

export function initAnalytics() {
  if (typeof window === 'undefined') return;
  posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
    api_host: process.env.NEXT_PUBLIC_POSTHOG_HOST,
    capture_pageview: false, // Manual control
    capture_pageleave: true,
  });
}

export function trackEvent(name: string, properties?: Record<string, unknown>) {
  posthog.capture(name, properties);
}

export function identifyUser(userId: string, traits?: Record<string, unknown>) {
  posthog.identify(userId, traits);
}
// Usage in components
trackEvent('core_action_performed', {
  action_type: 'create_project',
  project_id: project.id,
});

Step 5: Build Dashboards

Three essential dashboards:

Dashboard 1: Growth Overview

  • Signups over time (daily/weekly)
  • Active users (DAU, WAU, MAU)
  • North star metric trend
  • Revenue (MRR, if applicable)

Dashboard 2: Activation Funnel

  • Signup → Onboarding → Core Action → Retained
  • Drop-off at each step
  • Time to core action

Dashboard 3: Retention

  • Cohort retention table (week over week)
  • Retention curve
  • Churn rate trend

Key Metrics Formulas

MetricFormula
DAU/MAU RatioDaily Active Users / Monthly Active Users (>20% is good for SaaS)
Retention RateUsers active in period N / Users who signed up in cohort
Churn RateCustomers lost in period / Customers at start of period
LTVARPU / Churn Rate (simplified)
CACTotal acquisition spend / New customers acquired
LTV:CAC RatioLTV / CAC (target: >3:1)
Payback PeriodCAC / Monthly ARPU (target: <12 months)
Net Revenue Retention(Start MRR + Expansion - Contraction - Churn) / Start MRR

When to Consult References

  • references/metrics-frameworks.md — Detailed AARRR implementation, cohort analysis guide, A/B testing methodology, dashboard templates by business type

Anti-Patterns

  • Don't track everything — More events ≠ more insight. Track what drives decisions.
  • Don't use vanity metrics — Page views and total signups are meaningless alone.
  • Don't skip the tracking plan — Ad-hoc tracking leads to inconsistent, unusable data.
  • Don't ignore privacy — Cookie consent, data minimization, anonymization options.
  • Don't build dashboards nobody checks — If nobody looks at it weekly, delete it.
  • Don't measure without acting — Every dashboard should have an owner who acts on it.

适合场景

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