القائمة
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.
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Generate a structured data analysis report using notebookmd. Use when the user asks to analyze data, a CSV file, a DataFrame, financial data, or any dataset and wants a readable Markdown report with tables, charts, metrics, and insights.
Reference guide for the notebookmd library API. Use when you need to look up notebookmd widget methods, check API signatures, or understand how to use a specific notebookmd feature. This skill provides the complete API reference.
Generate a structured Markdown report using notebookmd. Use when the user asks for a report, documentation, summary, or writeup that should be saved as a formatted Markdown file with sections, tables, metrics, and artifacts.
| 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"] |
Generate a KPI dashboard as a structured Markdown report using notebookmd.
Dashboard: $ARGUMENTS
from notebookmd import nb, NotebookConfig
cfg = NotebookConfig(max_table_rows=20)
n = nb("dist/dashboard.md", title="<Dashboard Title>", cfg=cfg)
# 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()
n.change() for period-over-period comparisonsn.ranking() for leaderboards and rankingsn.badge() for status indicatorsn.kv() for grouped metrics (unit economics, configuration)max_table_rows to truncate