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SKILL.md
npx skills add wshobson/agents --skill kpi-dashboard-design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
npx skills add wshobson/agents --skill kpi-dashboard-design
Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions.
| Level | Focus | Update Frequency | Audience |
|---|---|---|---|
| Strategic | Long-term goals | Monthly/Quarterly | Executives |
| Tactical | Department goals | Weekly/Monthly | Managers |
| Operational | Day-to-day | Real-time/Daily | Teams |
Detailed sections (starting with ## Common KPIs by Department) live in references/details.md. Read that file when the navigation summary above is insufficient.
The most common cause is inconsistent treatment of annual plans. Finance may prorate to a daily rate while the dashboard normalizes to monthly. Align on a single formula and document it directly on the dashboard card:
The dashboard likely tracks system uptime (a lagging indicator) but not user-facing quality metrics. Add customer-perceived metrics alongside infrastructure metrics:
| Infrastructure (green) | User-perceived (add these) |
|---|---|
| API uptime 99.9% | P95 page load time |
| Error rate 0.1% | Task completion rate |
| Queue depth normal | Support ticket volume |
Check whether the cohort query is partitioning by signup month correctly. A common bug is using created_at::date instead of DATE_TRUNC('month', created_at), which groups by day and produces cohorts too small to show trends:
A live dashboard refreshing every 10 seconds with complex cohort SQL will degrade production query performance. Separate OLAP workloads from OLTP by writing pre-aggregated metrics to a summary table via a scheduled job, and have the dashboard read from that:
Static thresholds set once and never reviewed cause alert fatigue. Use dynamic thresholds based on rolling averages so alerts fire only when the metric deviates significantly from its own baseline:
data-storytelling - Turn dashboard findings into narratives that drive executive decisions-- Explicit formula shown in tooltip / data dictionary
-- Annual plans: divide total contract value by 12
-- Quarterly plans: divide by 3
-- Monthly plans: use as-is
CASE subscription_interval
WHEN 'monthly' THEN amount
WHEN 'quarterly' THEN amount / 3.0
WHEN 'yearly' THEN amount / 12.0
END AS normalized_mrr-- Wrong: too granular, cohorts are too small
DATE_TRUNC('day', created_at) AS cohort_date
-- Correct: monthly cohorts
DATE_TRUNC('month', created_at) AS cohort_month# Scheduled every 5 minutes via cron/Celery
def refresh_mrr_summary():
conn.execute("""
INSERT INTO kpi_snapshot (metric, value, snapshot_at)
SELECT 'mrr', SUM(...), NOW()
FROM subscriptions WHERE status = 'active'
ON CONFLICT (metric) DO UPDATE SET value = EXCLUDED.value
""")# Alert if current value is > 2 standard deviations from 30-day rolling mean
def is_anomalous(current: float, history: list[float]) -> bool:
mean = statistics.mean(history)
stdev = statistics.stdev(history)
return abs(current - mean) > 2 * stdev