What is the primary advantage of using Lumen over Panel?

Lumen is declarative, meaning you describe 'what' you want in the dashboard using YAML, whereas Panel is imperative, requiring you to write Python code to specify 'how' to build every interaction.

Which databases are supported by the Lumen Dashboards skill?

It supports a wide range of databases including PostgreSQL, DuckDB, SQLite, and any other database compatible with SQLAlchemy.

Does this skill support real-time data updates?

Yes, Lumen supports live data updates in production and features auto-reload capabilities during the development process.

Can I create interactive filters without writing Python code?

Yes, Lumen allows you to define widget-based filters (like sliders and dropdowns) and cross-filtering behavior directly within the YAML specification.

What visualization libraries can I use with this skill?

Lumen primarily uses hvPlot (built on HoloViews), but it also provides native support for Plotly and Altair views within the dashboard layout.

Lumen Dashboards

Name: Lumen Dashboards
Author: cdcore09

bycdcore09

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分析与监控

Builds declarative, no-code data dashboards using Lumen YAML specifications for rapid data visualization and exploration.

关于

Lumen Dashboards is a specialized skill that empowers Claude to architect sophisticated data applications using the Lumen framework. By leveraging a declarative approach, this skill allows users to define complete data pipelines—from connecting to sources like PostgreSQL, DuckDB, and REST APIs to applying complex transforms and rendering interactive visualizations—entirely through YAML configuration. It is ideal for rapidly prototyping data exploration tools, creating live monitoring dashboards, and building reproducible data interfaces without the need for imperative Python programming.

主要功能

Interactive filtering and advanced cross-filtering capabilities
Automated data pipelines for cleaning and transformation
Declarative YAML-based dashboard configuration
2 GitHub stars
Multi-source data integration including SQL, Parquet, and REST APIs
Responsive layout design with indicators, tables, and hvPlot charts

使用场景

Building reproducible and shareable data reports with interactive widgets
Creating live monitoring dashboards connected to production databases
Rapidly prototyping data exploration tools for business stakeholders

关于

主要功能

Interactive filtering and advanced cross-filtering capabilities
Automated data pipelines for cleaning and transformation
Declarative YAML-based dashboard configuration
2 GitHub stars
Multi-source data integration including SQL, Parquet, and REST APIs
Responsive layout design with indicators, tables, and hvPlot charts

使用场景

Building reproducible and shareable data reports with interactive widgets
Creating live monitoring dashboards connected to production databases
Rapidly prototyping data exploration tools for business stakeholders