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Dashboard

Create persistent dashboards from SQL queries, outputs, and layout files.

Updated Aug 23, 2026
Search Tags:#metrics#dashboard#sql#filters#plotly#snapshot#dataframe
One-click install
npx skills add https://github.com/josca42/varro --skill dashboard-josca42
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Dashboard
Source: https://github.com/josca42/varro/tree/main/user_workspace/skills/dashboard
Command: npx skills add https://github.com/josca42/varro --skill dashboard-josca42

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables analysts and product teams to create, iterate, and persist interactive visual analyses built from SQL queries, outputs, and layout files, eliminating repetitive manual chart assembly and ad-hoc export workflows.

Core Features & Use Cases

  • Structured dashboard folders that include query SQL files, an outputs.py with @output functions, and a dashboard.md layout to compose metrics, tables, and charts.
  • Filter binding using :param and optional-filter patterns, options queries for dropdowns, and dependency injection of query results into output functions.
  • Snapshotting and validation tools for end-to-end checks and exporting rendered figures, tables, and metrics for reporting and review.
  • Use case: build a regional sales dashboard with trend charts, a totals metric, and a region dropdown populated from an options query, then Snapshot for stakeholder delivery.

Quick Start

Create a new dashboard at /dashboard/sales/ by adding queries/, an outputs.py with @output functions, and a dashboard.md layout with filters and component tags.

Frequently Asked Questions about Dashboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a persistent interactive dashboard from SQL queries?▼

Build persistent dashboards by setting up a structured folder with SQL query files, an outputs.py containing @output functions, and a dashboard.md layout to compose metrics, tables, and charts for reuse and iteration.

How do SQL filters and parameters work in a Plotly dashboard?▼

SQL filters use :param binding and optional-filter patterns to pass values into queries. Options queries populate dropdown menus, and the resulting query data is injected directly into output functions to render dynamic Plotly charts and pandas tables.

Can I snapshot and validate dashboard outputs for stakeholder reporting?▼

Yes, you can snapshot and validate dashboard outputs. The Snapshot and ValidateDashboard tools perform end-to-end checks and export rendered figures, tables, and metrics, allowing you to deliver static reporting snapshots to stakeholders.

What is the best way to assemble multi-query visual analyses without manual chart exports?▼

The best way to eliminate manual chart assembly is using a persistent dashboard folder structure. By defining queries, outputs, and a markdown layout, you compose interactive metrics and charts that update dynamically rather than relying on ad-hoc exports.

Do I need pandas or Plotly to build dataframes and charts in these dashboards?▼

You need Plotly and pandas to render visual outputs. The dashboard framework injects SQL query results into your output functions, which use these libraries to generate the charts and dataframes displayed in the final interactive view.

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