Check whether a dashboard can be trusted before a decision. — Claude Skill
Um Skill Claude para Claude Code por Paweł Huryn — executar /metrics-dashboard no Claude·Atualizado em 13 de jun. de 2026·vphuryn/pm-skills@metrics-dashboard
Reviews dashboard definitions, freshness, filters, owners, source systems, and obvious contradictions so teams know whether the numbers are safe to use.
- Explains whether dashboard numbers are decision-ready, stale, inconsistent, or missing context.
- Checks metric definitions, filters, date ranges, owner, refresh time, source system, and known caveats.
- Separates cosmetic dashboard issues from problems that can change a business decision.
- Returns a trust summary, risks, questions for the owner, and fixes to make before sharing.
A team opens a dashboard in a meeting, notices a number looks odd, and spends the discussion arguing about whether it is trustworthy.
Run /dashboard-audit before the meeting to identify stale data, unclear definitions, risky filters, and questions for the owner.
Para quem é
Review dashboard trust, definitions, source freshness, and blocking issues.
Ver skills para esta funçãoKnow whether product metrics are safe for launch or roadmap decisions.
Ver skills para esta funçãoCheck revenue dashboards before forecast, pipeline, or leadership reviews.
Ver skills para esta funçãoO que faz
Check whether a dashboard is safe to use in a weekly business review.
Find why numbers look wrong, stale, or inconsistent with another source.
Review definitions, filters, and owners before a new dashboard goes live.
Como funciona
Share dashboard screenshots, metric definitions, filters, SQL snippets, or exported numbers.
State the decision the dashboard is supposed to support.
The skill checks freshness, definitions, filters, source alignment, and contradictions.
A human confirms the source owner, fixes blocking issues, and decides whether the dashboard can be used.
Opções de entrada
Screenshots, exported table, metric definitions, filters, refresh timestamp, or SQL.
Exemplo
Product: B2B workspace app Goal: improve activation this quarter Current metrics: activation 38%, invite completion 41%, first project created 52%, setup tickets 210/month Need: dashboard spec with metric definitions, layout, alerts, and review cadence.
| Metric | Definition | Data Source | Visualization | Target | Alert Threshold | |---|---|---|---|---|---| | Activation rate | Accounts with 2+ users and first project within 7 days / new accounts | Product events | Line + cohort table | 55% | Drops below 35% weekly | | Invite completion | Accepted invites / sent invites within 7 days | Product events | Funnel | 65% | Drops below 45% | | First project created | New accounts with first project in 24h | Product events | Line | 60% | Drops below 50% | | Setup ticket rate | Setup tickets / new accounts | Zendesk | Bar | -30% | Rises 15% WoW |
┌─────────────────────────────────────────────┐ │ NORTH STAR: Activated accounts - 38% │ │ Trend: +3 pts vs last month │ ├──────────────────┬──────────────────────────┤ │ Invite completion│ First project created │ │ 41% │ 52% │ ├──────────────────┼──────────────────────────┤ │ Setup tickets │ Time to first value │ │ 210/month │ 4.0 days │ ├──────────────────┴──────────────────────────┤ │ HEALTH: errors, latency, support escalations │ └─────────────────────────────────────────────┘
| Cadence | Metrics | Owner | Action | |---|---|---|---| | Daily | errors, invite send failures | Engineering | Fix broken flow | | Weekly | invite completion, first project | Product | Decide experiment follow-up | | Monthly | activation, retention, setup tickets | PM + Support | Reprioritize roadmap |
Activation is useful only if the event definition is stable. Lock the event names and source of truth before using the dashboard in leadership updates.
Métricas que melhora
Funciona com
Em qualquer lugar
Paste the notes, exports, screenshots, or summaries you already have. The skill works without a connected system.
Connect the relevant support, analytics, CRM, or data tool when you want fresher source evidence.
Quer usar Metrics Dashboard?
Escolha como começar.
Instale e execute este skill localmente no seu computador.
Abra um terminal no seu computador e cole este comando:
Isto descarrega o skill com todos os ficheiros para o seu computador:
Adicione -g no fim para o tornar disponível em todos os seus projetos.
Inicie o Claude Code, depois escreva o comando:
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
-
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
-
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] -
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] │ └─────────────────────────────────────────────┘ -
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
-
Define alerts:
- What thresholds trigger investigation?
- Who gets alerted and through what channel?
- What's the expected response time?
-
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
- The North Star Framework 101
- The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
- AARRR (Pirate) Metrics: The 5-Stage Framework for Growth
- The Google HEART Framework: Your Guide to Measuring User-Centric Success
- Funnel Analysis 101: How to Track and Optimize Your User Journey
- Are You Tracking the Right Metrics?
- Continuous Product Discovery Masterclass (CPDM) (video course)
Documentos de referência
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
-
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
-
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] -
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] │ └─────────────────────────────────────────────┘ -
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
-
Define alerts:
- What thresholds trigger investigation?
- Who gets alerted and through what channel?
- What's the expected response time?
-
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
- The North Star Framework 101
- The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
- AARRR (Pirate) Metrics: The 5-Stage Framework for Growth
- The Google HEART Framework: Your Guide to Measuring User-Centric Success
- Funnel Analysis 101: How to Track and Optimize Your User Journey
- Are You Tracking the Right Metrics?
- Continuous Product Discovery Masterclass (CPDM) (video course)
Source marketplace page: https://github.com/phuryn/pm-skills/blob/HEAD/pm-product-discovery/skills/metrics-dashboard/SKILL.md
Install command: npx skills add phuryn/pm-skills@metrics-dashboard