dashboard

name	dashboard
description	Generate a metrics dashboard using notebookmd with KPIs, charts, and data tables. Use when the user wants a dashboard, scorecard, or metrics overview saved as Markdown.
argument-hint	[metrics source or description]
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]

Metrics Dashboard Generator

Generate a KPI dashboard as a structured Markdown report using notebookmd.

Input

Dashboard: $ARGUMENTS

Instructions

1. Setup

from notebookmd import nb, NotebookConfig

cfg = NotebookConfig(max_table_rows=20)
n = nb("dist/dashboard.md", title="<Dashboard Title>", cfg=cfg)

2. Dashboard Layout

# Hero Metrics Row
n.section("Key Performance Indicators")
n.metric_row([
    {"label": "Revenue", "value": "$4.2M", "delta": "+18%"},
    {"label": "Users", "value": "34.5K", "delta": "+2,100"},
    {"label": "Conversion", "value": "3.2%", "delta": "+0.4%"},
    {"label": "Churn", "value": "1.8%", "delta": "-0.3%", "delta_color": "inverse"},
])

# Status Badges
n.badge("ON TRACK", style="success")
n.badge("Q4 2026", style="info")

# Trend Charts
n.section("Trends")
n.line_chart(df, x="date", y="revenue", title="Revenue Trend")
n.area_chart(df, x="date", y="users", title="User Growth")
n.bar_chart(df, x="category", y="count", title="Distribution")

# Detailed Metrics
n.section("Detailed Metrics")
n.kv({
    "MRR": "$350K",
    "ARR": "$4.2M",
    "LTV": "$1,200",
    "CAC": "$180",
    "LTV/CAC": "6.7x",
}, title="Unit Economics")

# Period Comparison
n.section("Period Comparison")
n.change("Revenue", current=4_200_000, previous=3_560_000, fmt=",.0f", pct=True)
n.change("Users", current=34_521, previous=32_421, fmt=",d", pct=True)
n.change("Churn", current=0.018, previous=0.021, fmt=".1%", pct=True)

# Rankings
n.section("Top Performers")
n.ranking("Product A", value="$1.2M", rank=1, total=15)
n.ranking("Product B", value="$890K", rank=2, total=15)
n.ranking("Product C", value="$650K", rank=3, total=15)

# Data Tables
n.section("Data Tables")
n.dataframe(summary_df, name="Summary by Segment")

# Alerts
n.section("Alerts & Actions")
n.success("Revenue target exceeded by 12%")
n.warning("Churn rate increasing in Enterprise segment")
n.info("New pricing tier launching next quarter")

# Export
n.export_csv(df, "dashboard_data.csv", name="Dashboard data export")
n.save()

3. Design Principles

• Lead with the most important metrics at the top (metric_row)
• Use n.change() for period-over-period comparisons
• Use n.ranking() for leaderboards and rankings
• Use n.badge() for status indicators
• Use n.kv() for grouped metrics (unit economics, configuration)
• Charts go in a dedicated "Trends" section
• End with alerts, actions, and data exports
• Keep tables concise — use max_table_rows to truncate