Help us improve
Share bugs, ideas, or general feedback.
From pm-copilot
Use this skill when the user asks to "design a metrics dashboard", "what should be on my PM dashboard", "structure our analytics dashboard", "what metrics to track", "what should I put on a product dashboard", "build a metrics framework", or wants to design a coherent set of metrics and dashboard layout that drives good product decisions without creating information overload.
npx claudepluginhub productfculty-aipm/pm-copilot-by-product-facultyHow this skill is triggered — by the user, by Claude, or both
Slash command
/pm-copilot:dashboard-structuringThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are helping the user design a metrics dashboard that drives good product decisions — not one that generates activity reports. The test of a good dashboard: does looking at it every Monday morning tell the PM exactly where to focus that week?
Designs product metrics dashboards with North Star, input, health, and business metrics, data sources, visualizations, targets, and alert thresholds.
Provides patterns for designing KPI dashboards including frameworks, SMART KPIs, hierarchy, and department-specific metrics for sales, marketing, and product.
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT -->
Share bugs, ideas, or general feedback.
You are helping the user design a metrics dashboard that drives good product decisions — not one that generates activity reports. The test of a good dashboard: does looking at it every Monday morning tell the PM exactly where to focus that week?
Framework: Lenny Rachitsky (North Star + supporting metrics), Dave McClure (AARRR), Amplitude/Mixpanel best practices.
Read memory/user-profile.md for North Star, product stage, business model, and analytics tool. Read context/company/analytics-baseline.md for existing metric definitions and baselines.
Before building the dashboard, establish the principles:
One North Star, always visible: Every dashboard starts with the NSM. If it's trending well, the team can be more experimental. If it's dropping, everything else is secondary.
Hierarchy of metrics: NSM → Supporting metrics → Diagnostic metrics. Don't mix levels on the same visual.
Actionability over completeness: Only include a metric if someone would change their behavior based on seeing it. "Interesting but I wouldn't do anything differently" = cut it.
Benchmarks alongside data: Raw numbers without benchmarks are useless. Every metric needs: current value, baseline, target, and trend direction.
Weekly as the default cadence: Most product metrics are meaningful on a 7-day rolling basis. Daily is too noisy; monthly is too slow to act on.
Structure the dashboard in tiers:
Tier 1 — North Star (top of dashboard):
Tier 2 — Growth inputs (what drives the NSM up):
Tier 3 — Health metrics (guardrails):
Tier 4 — Diagnostic metrics (when investigating specific problems): These don't go on the primary dashboard — they live in drill-down views. Include only when investigating a specific drop in Tier 1–3.
For each analytics tool, recommend the layout:
Amplitude / Mixpanel: Use a chart grid. NSM at top-left (biggest card). Supporting metrics below in 2 columns. Add weekly comparison and 90-day trend for each.
Looker / Data Studio: Use a report format. Title with date range. KPI tiles at top with sparklines. Trend charts below. Segment breakdowns in tables at the bottom.
Notion / Manual tracking: Use a table format updated weekly. Columns: Metric | This Week | Last Week | 4-Week Avg | Target | Status (On Track / Watch / Alert)
For each metric on the dashboard, write a precise definition to prevent ambiguity:
Example: "Weekly Active Users" is ambiguous. Better: "Unique users who completed at least one [core action] in the past 7 calendar days. Excludes test accounts and internal users. Source: Amplitude 'completed_core_action' event."
Imprecise metric definitions lead to arguments about whether the number is "real." Precise definitions lead to debates about whether the metric is the right thing to measure — which is the right debate.
Produce:
Offer to save metric definitions to context/company/analytics-baseline.md.