metrics-dashboard

name	metrics-dashboard
description	Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.

Product Metrics Dashboard

Design a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds.

Context

You are designing a metrics dashboard for $ARGUMENTS.

If the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first.

Domain Context

Metrics vs KPIs vs NSM: Metrics = all measurable things. KPIs = a few key quantitative metrics tracked over a longer period. North Star Metric = a single customer-centric KPI that is a leading indicator of business success.

4 criteria for a good metric (Lean Analytics): (1) Understandable — creates a common language. (2) Comparative — over time, not a snapshot. (3) Ratio or Rate — more revealing than whole numbers. (4) Behavior-changing — the Golden Rule: "If a metric won't change how you behave, it's a bad metric."

8 metric types: Vanity vs Actionable (only actionable metrics change behavior), Qualitative vs Quantitative (WHAT vs WHY — you need both; never stop talking to customers), Exploratory vs Reporting (explore data to uncover unexpected insights), Lagging vs Leading (leading indicators enable faster learning cycles, e.g. customer complaints predict churn).

5 action steps: (1) Audit metrics against the 4 good-metric criteria. (2) Update dashboards — ensure all key metrics are good ones. (3) Identify vanity metrics — be careful how you use them. (4) Classify leading vs lagging indicators. (5) Pick one problem and dig deep into the data.

For case studies and more detail: Are You Tracking the Right Metrics? from metrics literature

Instructions

1. Identify the metrics framework — organize metrics into layers:

   North Star Metric: The single metric that best captures core value delivery

   Input Metrics (3-5): The levers that drive the North Star

   Health Metrics: Guardrails that ensure overall product health

   Business Metrics: Revenue, cost, and unit economics

2. For each metric, define:

   Metric	Definition	Data Source	Visualization	Target	Alert Threshold
   [Name]	[Exact calculation: numerator/denominator, time window]	[Where the data comes from]	[Line chart / Bar / Number / Funnel]	[Goal value]	[When to trigger an alert]

3. Design the dashboard layout:

   ┌─────────────────────────────────────────────┐
   │  NORTH STAR: [Metric] — [Current Value]     │
   │  Trend: [↑/↓ X% vs last period]             │
   ├──────────────────┬──────────────────────────┤
   │  Input Metric 1  │  Input Metric 2          │
   │  [Sparkline]     │  [Sparkline]             │
   ├──────────────────┼──────────────────────────┤
   │  Input Metric 3  │  Input Metric 4          │
   │  [Sparkline]     │  [Sparkline]             │
   ├──────────────────┴──────────────────────────┤
   │  HEALTH: [Latency] [Error Rate] [NPS]       │
   ├─────────────────────────────────────────────┤
   │  BUSINESS: [MRR] [CAC] [LTV] [Churn]        │
   └─────────────────────────────────────────────┘
   

4. Set review cadence:

   • Daily: Operational health (errors, latency, critical flows)
   • Weekly: Input metrics and engagement trends
   • Monthly: North Star, business metrics, OKR progress
   • Quarterly: Strategic review and metric recalibration

5. Define alerts:

   • What thresholds trigger investigation?
   • Who gets alerted and through what channel?
   • What's the expected response time?

6. Recommend tools based on the user's context:

   • Amplitude, Mixpanel, PostHog for product analytics
   • Looker, Metabase, Mode for SQL-based dashboards
   • Datadog, Grafana for operational health

Think step by step. Save the dashboard specification as a markdown document.

---

Further Reading

• The Ultimate List of Product Metrics
• The North Star Framework 101
• The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
• AARRR (Pirate) Metrics: The 5-Stage Framework for Growth
• The Google HEART Framework: Your Guide to Measuring User-Centric Success
• Funnel Analysis 101: How to Track and Optimize Your User Journey
• Are You Tracking the Right Metrics?
• Continuous Product Discovery Masterclass (CPDM) (video course)

是什么

metrics-dashboard 用来把 数据分析师 场景里的任务输入转成可执行的流程、检查清单和交付物。

Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.

它的价值在于让 数据AI职能线 在 Claude Code、Codex、Gemini、Hermes 或 OpenClaw 中复用同一套岗位能力，而不是依赖一次性的聊天提示词。

怎么用

1. 明确当前任务目标、输入材料、约束和期望交付物，再加载 metrics-dashboard。
2. 按 skill 文档中的流程、检查清单或工具建议执行，优先复用仓库已有规范与真实命令。
3. 把关键判断、风险、验证命令和产出路径记录到当前任务文档或交付说明中。
4. 用最小可证明的检查确认结果有效；发现缺口时回到 skill 清单补齐。

架构图

flowchart LR
  A[任务输入] --> B[加载 metrics-dashboard]
  B --> C[执行流程与检查清单]
  C --> D[生成交付物与风险记录]
  D --> E[验证结果并沉淀复盘]