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metrics-dashboard

phuryn/pm-skills

Design a comprehensive product metrics dashboard with KPIs, data sources, visualizations, and alert thresholds.

What is metrics-dashboard?

This skill guides you through building a metrics dashboard by identifying your North Star metric, input metrics, health metrics, and business metrics. Use it when defining KPIs, setting up product analytics, or establishing a data monitoring plan for your product.

  • Organize metrics into a layered framework (North Star, Input, Health, Business)
  • Define each metric with calculation, data source, visualization type, target, and alert threshold
  • Design dashboard layout with visual hierarchy and metric groupings
  • Establish review cadence (daily operational, weekly engagement, monthly strategic, quarterly recalibration)
  • Classify metrics as vanity vs actionable, leading vs lagging, qualitative vs quantitative
  • Audit metrics against four criteria: understandable, comparative, ratio/rate-based, behavior-changing

How to install metrics-dashboard

npx skills add https://github.com/phuryn/pm-skills --skill metrics-dashboard
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How to use metrics-dashboard

  1. 1.Identify your North Star metric—the single metric that best captures core value delivery
  2. 2.Define 3-5 input metrics that are the levers driving your North Star
  3. 3.List health metrics (guardrails for product health) and business metrics (revenue, cost, unit economics)
  4. 4.For each metric, create a row in a table with: name, exact definition/calculation, data source, visualization type, target value, and alert threshold
  5. 5.Design your dashboard layout with North Star prominently displayed, input metrics in a grid, and health/business metrics below
  6. 6.Set a review cadence: daily for operational health, weekly for input metrics, monthly for North Star and business metrics, quarterly for strategic recalibration
  7. 7.Define alert rules: what thresholds trigger investigation, who gets notified, and expected response time
  8. 8.Save the complete dashboard specification as a markdown document

Use cases

Good for
  • Creating a new product analytics dashboard from scratch
  • Defining OKRs and KPIs for a product team
  • Setting up operational health monitoring (errors, latency, critical flows)
  • Establishing alert thresholds and escalation procedures for metric anomalies
  • Auditing existing dashboards to eliminate vanity metrics and focus on actionable data
Who it's for
  • Product managers defining metrics strategy
  • Analytics engineers building dashboards
  • Data teams establishing monitoring systems
  • Product leaders setting up OKR tracking
  • Startups establishing their first metrics framework

metrics-dashboard FAQ

What's the difference between a metric, KPI, and North Star metric?

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

How do I know if a metric is good?

A good metric meets four criteria: (1) Understandable—creates common language, (2) Comparative—measured over time not as a snapshot, (3) Ratio or Rate—more revealing than whole numbers, (4) Behavior-changing—the metric must change how you behave.

What's the difference between leading and lagging indicators?

Leading indicators enable faster learning cycles and predict future outcomes (e.g., customer complaints predict churn). Lagging indicators measure past results. You need both, but leading indicators help you course-correct faster.

How often should I review my metrics?

Daily for operational health (errors, latency), weekly for input metrics and engagement trends, monthly for North Star and business metrics, and quarterly for strategic review and metric recalibration.

What tools should I use to build my dashboard?

Choose based on your needs: Amplitude, Mixpanel, PostHog for product analytics; Looker, Metabase, Mode for SQL-based dashboards; Datadog, Grafana for operational health monitoring.

Full instructions (SKILL.md)

Source of truth, from phuryn/pm-skills.


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 (Ben Yoskovitz, 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? by Ben Yoskovitz

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:

    MetricDefinitionData SourceVisualizationTargetAlert 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