metrics-dashboard
This Claude Code skill creates a structured framework for designing product metrics dashboards by organizing metrics into layers (North Star Metric, Input Metrics, Health Metrics, Business Metrics) and defining each metric's calculation, data source, visualization type, target, and alert threshold. Use it when establishing KPIs for a new product, auditing existing analytics to identify actionable metrics, setting up data monitoring systems, or aligning teams around customer-centric measurement frameworks.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/metrics-dashboard && cp -r /tmp/metrics-dashboard/pm-product-discovery/skills/metrics-dashboard ~/.claude/skills/metrics-dashboardSKILL.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?](https://www.productcompass.pm/p/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 - [The Ultimate List of Product Metrics](https://www.productcompass.pm/p/the-ultimate-list-of-product-metrics) - [The North Star Framework 101](https://www.productcompass.pm/p/the-north-star-framework-101) - [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https://www.productcompass.pm/p/the-product-analytics-playbook-aarrr) - [AARRR (Pirate) Metrics: The 5-Stage Framework for Growth](https://www.productcompass.pm/p/aarrr-pirate-metrics) - [The Google HEART Framework: Your Guide to Measuring User-Centric Success](https://www.productcompass.pm/p/the-google-heart-framework) - [Funnel Analysis 101: How to Track and Optimize Your User Journey](https://www.productcompass.pm/p/funnel-analysis) - [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics) - [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Analyze and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.
Generate an Ansoff Matrix analysis mapping growth strategies across market penetration, market development, product development, and diversification. Use when considering growth options, planning market expansion, or evaluating strategic growth paths.
Identify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry strategy.
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
Brainstorm product ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints. Use when generating new feature ideas, brainstorming solutions for an identified opportunity, or ideating with a product trio.
Brainstorm feature ideas for a new product in initial discovery from PM, Designer, and Engineer perspectives. Use when starting product discovery for a new product, exploring features for a startup idea, or doing initial ideation.