MCPInstall

metrics-dashboard

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.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/phuryn/pm-skills --skill metrics-dashboard
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads metrics-dashboard automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

FAQ

What does the metrics-dashboard skill do?

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.

How do I install metrics-dashboard?

Run npx skills add https://github.com/phuryn/pm-skills --skill metrics-dashboard in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/metrics-dashboard/ (personal) or .claude/skills/metrics-dashboard/ (project).

Which AI agents can use metrics-dashboard?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is metrics-dashboard safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains metrics-dashboard?

It is published by phuryn in phuryn/pm-skills. It has 3,008 recorded installs.

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