metrics-dashboard

phuryn/pm-skills · updated Apr 8, 2026

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$npx skills add https://github.com/phuryn/pm-skills --skill metrics-dashboard
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summary

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

skill.md

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:

    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

how to use metrics-dashboard

How to use metrics-dashboard on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add metrics-dashboard
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches metrics-dashboard from GitHub repository phuryn/pm-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/metrics-dashboard

Reload or restart Cursor to activate metrics-dashboard. Access the skill through slash commands (e.g., /metrics-dashboard) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.468 reviews
  • Kaira Ramirez· Dec 8, 2024

    Keeps context tight: metrics-dashboard is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Mei Kapoor· Dec 8, 2024

    metrics-dashboard has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chinedu Chawla· Dec 4, 2024

    Registry listing for metrics-dashboard matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kaira Park· Dec 4, 2024

    Solid pick for teams standardizing on skills: metrics-dashboard is focused, and the summary matches what you get after install.

  • Kaira Robinson· Dec 4, 2024

    Keeps context tight: metrics-dashboard is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Neel Agarwal· Nov 27, 2024

    Solid pick for teams standardizing on skills: metrics-dashboard is focused, and the summary matches what you get after install.

  • Chinedu Yang· Nov 23, 2024

    metrics-dashboard reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Neel Singh· Nov 23, 2024

    metrics-dashboard has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Isabella Martinez· Nov 23, 2024

    I recommend metrics-dashboard for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yash Thakker· Nov 19, 2024

    Keeps context tight: metrics-dashboard is the kind of skill you can hand to a new teammate without a long onboarding doc.

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