Metrics Dashboard
Define KPIs, dashboard layout, data sources, and alert thresholds before you wire up product analytics in your stack.
Overview
metrics-dashboard is an agent skill most often used in Grow (also Validate, Operate) that defines KPIs, visualizations, data sources, and alert thresholds for a product metrics dashboard.
Install
npx skills add https://github.com/phuryn/pm-skills --skill metrics-dashboardWhat is this skill?
- Frames metrics vs KPIs vs North Star Metric with Lean Analytics-style good-metric criteria
- Covers eight metric types (vanity vs actionable, lagging vs leading, qualitative vs quantitative)
- Produces dashboard spec: key metrics, data sources, visualization types, and alert thresholds
- Accepts existing OKRs, strategy docs, or analytics exports as input context
- Behavior-changing filter: drops metrics that would not change decisions
- 4 criteria for a good metric (Lean Analytics)
- 8 metric types called out in the skill framework
Adoption & trust: 1.1k installs on skills.sh; 12.3k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You are about to instrument the product but only have a vague list of numbers that will not change what you ship next week.
Who is it for?
Solo founders defining NSM/KPIs and a first exec-style product dashboard after launch or before a growth experiment.
Skip if: Teams that only need a one-off SQL query or already have a frozen enterprise BI template with no product decisions left.
When should I use this skill?
Creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.
What do I get? / Deliverables
You get a structured dashboard and monitoring plan—metrics, sources, viz types, and thresholds—ready to implement in your analytics stack.
- Metrics dashboard specification (metrics list, sources, viz types, alerts)
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Grow because the artifact is a living metrics dashboard and monitoring plan aimed at tracking product health after launch. Analytics is where solo builders choose what to measure, how to visualize it, and when to alert—not just which vendor to plug in.
Where it fits
Lock success metrics and NSM candidates before building an MVP so instrumentation is built in.
Design the weekly product review dashboard with leading indicators for activation and retention.
Add alert thresholds for error-rate and revenue-adjacent metrics alongside infra dashboards.
Pick launch funnel metrics and comparison windows for a release campaign.
How it compares
Planning artifact for what to measure—not a replacement for PostHog, Mixpanel, or warehouse ETL skills.
Common Questions / FAQ
Who is metrics-dashboard for?
Indie and solo builders who own product and growth and need a disciplined KPI and dashboard spec without hiring a data team.
When should I use metrics-dashboard?
Use it when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan—often in Grow/analytics after launch, in Validate when scoping what success looks like, or in Operate when tightening production monitoring.
Is metrics-dashboard safe to install?
Review the Security Audits panel on this Prism page and inspect the skill package in your repo before granting agent permissions.
SKILL.md
READMESKILL.md - Metrics Dashboard
## 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 - Looke