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Metrics Track

核心功能:基于精益创业原则,为iOS/Web/跨平台项目生成适配PostHog的事件漏斗、KPI基准,制定kill/iterate/scale数据驱动决策阈值,配套代码实现片段。典型场景:产品启动/迭代阶段需搭建科学指标体系、明确数据决策依据时使用。输入为项目名称(信息不足会主动询问),输出完整的metrics-plan.md文档及关键指标摘要,助力每周数据复盘与产品决策。

fortunto2
fortunto2
热度 74.71
最近更新 2026-05-11 17:26:34
分类:开发依赖:读写文件 / MCP 服务 / 内部调用大模型应用场景:代码生成 / 数据处理 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add solo-metrics-track
Metadata
name
solo-metrics-track
description
Set up PostHog metrics plan with event funnel, KPI benchmarks, and kill/iterate/scale decision thresholds. Use when user says "set up metrics", "track KPIs", "PostHog events", "funnel analysis", "when to kill or scale", or "success metrics". Do NOT use for SEO metrics (use /seo-audit).
license
MIT
metadata
{"author":"fortunto2","version":"1.1.1","openclaw":{"emoji":"📈"}}
allowed-tools
Read, Grep, Glob, Write, AskUserQuestion, mcp__solograph__kb_search
argument-hint
<project-name>

/metrics-track

Set up a metrics tracking plan for a project. Defines PostHog event funnel, KPI benchmarks, and kill/iterate/scale decision thresholds based on lean startup principles.

MCP Tools (use if available)

  • kb_search(query) — find PostHog methodology, analytics patterns

If MCP tools are not available, fall back to Grep + Read.

Methodology Reference

This skill implements metrics tracking based on lean startup principles:

  • Relative metrics vs niche benchmarks — compare against your own trajectory, not vanity averages
  • Kill/iterate/scale decision rules — data-driven thresholds for product decisions (see step 7 below)

Steps

  1. Parse project from $ARGUMENTS.

    • Read PRD for features, ICP, monetization model.
    • Read CLAUDE.md for stack (iOS/Web/both).
    • If empty: ask via AskUserQuestion.
  2. Detect platform:

    • iOS app → PostHog iOS SDK events
    • Web app → PostHog JS SDK events
    • Both → cross-platform identity (shared user ID across platforms)
  3. Load PostHog methodology:

    • If MCP available: kb_search("PostHog analytics events funnel identity")
    • Otherwise: check project docs for existing analytics configuration
    • Extract: event naming conventions, identity resolution, funnel pattern
  4. Define event funnel based on PRD features:

    Standard funnel stages (adapt per product):

    Awareness → Acquisition → Activation → Revenue → Retention → Referral
    

    Map to concrete events:

    Stage Event Name Trigger Properties
    Awareness page_viewed Landing page visit source, utm_*
    Acquisition app_installed or signed_up First install/signup platform, source
    Activation core_action_completed First key action feature, duration_ms
    Revenue purchase_completed First payment plan, amount, currency
    Retention session_started Return visit (D1/D7/D30) session_number, days_since_install
    Referral invite_sent Shared or referred channel, referral_code
  5. Forced reasoning — metrics selection: Before defining KPIs, write out:

    • North Star Metric: The ONE number that matters most (e.g., "weekly active users who completed core action")
    • Leading indicators: What predicts the North Star? (e.g., "activation rate D1")
    • Lagging indicators: What confirms success? (e.g., "MRR", "retention D30")
    • Vanity metrics to AVOID: (e.g., total downloads without activation)
  6. Set KPI benchmarks per stage:

    KPI Target Kill Threshold Scale Threshold Source
    Landing → Signup 3-5% < 1% > 8% Industry avg
    Signup → Activation 20-40% < 10% > 50% Product benchmark
    D1 Retention 25-40% < 15% > 50% Mobile avg
    D7 Retention 10-20% < 5% > 25% Mobile avg
    D30 Retention 5-10% < 2% > 15% Mobile avg
    Trial → Paid 2-5% < 1% > 8% SaaS avg

    Adjust based on product type (B2C vs B2B, free vs paid, mobile vs web).

  7. Define decision rules (lean startup kill/iterate/scale):

    ## Decision Framework
    
    **Review cadence:** Weekly (Fridays)
    
    ### KILL signals (any 2 = kill)
    - [ ] Activation rate < {kill_threshold} after 2 weeks
    - [ ] D7 retention < {kill_threshold} after 1 month
    - [ ] Zero organic signups after 2 weeks of distribution
    - [ ] CAC > 3x LTV estimate
    
    ### ITERATE signals
    - [ ] Metrics between kill and scale thresholds
    - [ ] Qualitative feedback suggests product-market fit issues
    - [ ] One stage of funnel is dramatically worse than others
    
    ### SCALE signals (all 3 = scale)
    - [ ] Activation rate > {scale_threshold}
    - [ ] D7 retention > {scale_threshold}
    - [ ] Organic growth > 10% week-over-week
    
  8. Generate PostHog implementation snippets:

    For iOS (Swift):

    // Event tracking examples
    PostHogSDK.shared.capture("core_action_completed", properties: [
        "feature": "scan_receipt",
        "duration_ms": elapsed
    ])
    

    For Web (TypeScript):

    // Event tracking examples
    posthog.capture('signed_up', {
        source: searchParams.get('utm_source') ?? 'direct',
        plan: 'free'
    })
    
  9. Write metrics plan to docs/metrics-plan.md:

    # Metrics Plan: {Project Name}
    
    **Generated:** {YYYY-MM-DD}
    **Platform:** {iOS / Web / Both}
    **North Star:** {north star metric}
    
    ## Event Funnel
    
    | Stage | Event | Properties |
    |-------|-------|------------|
    {event table from step 4}
    
    ## KPIs & Thresholds
    
    | KPI | Target | Kill | Scale |
    |-----|--------|------|-------|
    {benchmark table from step 6}
    
    ## Decision Rules
    
    {framework from step 7}
    
    ## Implementation
    
    ### PostHog Setup
    - Project: {project name} (EU region)
    - SDK: {posthog-ios / posthog-js}
    - Identity: {anonymous → identified on signup}
    
    ### Code Snippets
    {snippets from step 8}
    
    ## Dashboard Template
    - Funnel: {stage1} → {stage2} → ... → {stageN}
    - Retention: D1 / D7 / D30 cohort chart
    - Revenue: MRR trend + trial conversion
    
    ---
    *Generated by /metrics-track. Implement events, then review weekly.*
    
  10. Output summary — North Star metric, key thresholds, first event to implement.

Notes

  • PostHog EU hosting for privacy compliance
  • Use $set for user properties, capture for events
  • Identity: start anonymous, identify() on signup with user ID
  • Cross-platform: same PostHog project, same user ID → unified journey
  • Review dashboard weekly, make kill/iterate/scale decision monthly

Common Issues

Wrong platform detected

Cause: Project has both web and iOS indicators. Fix: Skill checks package manifests. If both exist, it generates cross-platform identity setup. Verify the detected platform in the output.

KPI thresholds too aggressive

Cause: Default thresholds are industry averages. Fix: Adjust thresholds in docs/metrics-plan.md based on your niche. B2B typically has lower volume but higher conversion.

PostHog SDK not in project

Cause: Metrics plan generated but SDK not installed. Fix: This skill generates the PLAN only. Install PostHog SDK separately: pnpm add posthog-js (web) or add posthog-ios via SPM (iOS).