Which data sources does this skill support?

It supports a wide range of sources including PostgreSQL, DuckDB, SQLite, CSV, Parquet, Excel, and REST APIs.

Is it possible to add custom data transformations?

Yes, Lumen includes a pipeline system where you can define transforms like SQL queries, aggregations, and column filtering directly in the YAML configuration.

Do I need to know Python to use Lumen Dashboards?

No, Lumen is designed for no-code dashboard creation via YAML specifications, though a Python API is available for advanced developers who need it.

What is the main advantage of using Lumen over Panel?

Lumen uses a declarative YAML approach which is faster for standard dashboard layouts and easier for AI or non-programmers to maintain, whereas Panel is imperative and better for highly custom logic.

Can I use this for real-time data monitoring?

Yes, Lumen supports live updates and auto-reloading, making it suitable for monitoring real-time data streams and key performance indicators.

Lumen Dashboards

Name: Lumen Dashboards
Author: uw-ssec

byuw-ssec

•

Analytics & Monitoring

Build interactive, declarative data dashboards using YAML specifications without writing complex Python code.

The Lumen Dashboards skill enables Claude to architect and generate complete data exploration tools through the Lumen framework's declarative YAML syntax. It streamlines the creation of complex data pipelines—connecting various sources like SQL databases and REST APIs to interactive visualizations and filters—making it ideal for rapid prototyping, standard reporting, and collaborative dashboard design where maintainability and simplicity are prioritized over custom imperative logic. By using structured YAML, this skill allows for reproducible dashboard configurations that can be easily deployed and shared across teams.

Key Features

01Declarative YAML-based dashboard configuration

02Built-in data transformation pipelines for aggregation and querying

0318 GitHub stars

04Responsive layout management with indicators and HoloViews charts

05Multi-source data integration for SQL, DuckDB, Parquet, and APIs

06Interactive cross-filtering and widget-based data exploration

Use Cases

01Rapidly prototyping data exploration tools for business stakeholders

02Creating interactive reports that connect multiple disparate data sources

03Building standard KPI dashboards with automated data refreshes

Key Features

01Declarative YAML-based dashboard configuration

02Built-in data transformation pipelines for aggregation and querying

0318 GitHub stars

04Responsive layout management with indicators and HoloViews charts

05Multi-source data integration for SQL, DuckDB, Parquet, and APIs

06Interactive cross-filtering and widget-based data exploration

Use Cases

01Rapidly prototyping data exploration tools for business stakeholders

02Creating interactive reports that connect multiple disparate data sources

03Building standard KPI dashboards with automated data refreshes