Agent skill

lumen-dashboards

Master declarative, no-code data dashboards with Lumen YAML specifications. Use this skill when building standard data exploration dashboards, connecting multiple data sources (files, databases, APIs), creating interactive filters and cross-filtering, designing responsive layouts with indicators and charts, or enabling rapid dashboard prototyping without writing code.

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/lumen-dashboards

SKILL.md

Lumen Dashboards Skill

Overview

Lumen is a declarative framework for creating data dashboards through YAML specifications. Build interactive data exploration dashboards without writing code - just configuration.

What is Lumen?

Lumen provides a declarative approach to building data dashboards:

No-code dashboards: Define everything in YAML
Data pipelines: Sources → Transforms → Views
Interactive exploration: Built-in filters and cross-filtering
Component library: Reusable sources, transforms, views
Live updates: Auto-reload and real-time data

Lumen vs Panel vs Lumen AI

Feature	Lumen Dashboards	Panel	Lumen AI
Approach	Declarative YAML	Imperative Python	Conversational
Code Required	No	Yes	No
Use Case	Fixed dashboards	Custom apps	Ad-hoc exploration
Flexibility	Medium	High	High
Development Speed	Very fast	Medium	Very fast

Use Lumen when:

Building standard data exploration dashboards
Working with non-programmers
Want rapid prototyping with configuration
Need reproducible dashboard specifications

Use Panel when:

Need fine-grained control over components
Building custom application logic
Creating novel interactions

Use Lumen AI when:

Users need ad-hoc exploration
Questions vary unpredictably
Enabling self-service analytics

Quick Start

Installation

bash

pip install lumen

Your First Dashboard

File: dashboard.yaml

yaml

sources:
  data:
    type: file
    tables:
      penguins: https://datasets.holoviz.org/penguins/v1/penguins.csv

pipelines:
  main:
    source: data
    table: penguins

    filters:
      - type: widget
        field: species

layouts:
  - title: Penguin Explorer
    views:
      - type: hvplot
        pipeline: main
        kind: scatter
        x: bill_length_mm
        y: bill_depth_mm
        by: species
        title: Bill Dimensions

Launch:

bash

lumen serve dashboard.yaml --show

Core Concepts

1. Sources

Data sources provide tables for your dashboard.

Supported sources:

File: CSV, Parquet, Excel, JSON
Database: PostgreSQL, DuckDB, SQLite
REST API: JSON endpoints
Intake: Data catalogs

Quick example:

yaml

sources:
  mydata:
    type: file
    tables:
      sales: ./data/sales.csv

See: Data Sources Reference for comprehensive source configuration.

2. Pipelines

Pipelines define data flows: Source → Filters → Transforms → Views

Basic pipeline:

yaml

pipelines:
  sales_pipeline:
    source: mydata
    table: sales

    filters:
      - type: widget
        field: region

    transforms:
      - type: aggregate
        by: ['category']
        aggregate:
          total_sales: {revenue: sum}

Components:

Filters: Interactive widgets for user input
Transforms: Data manipulation (filter, aggregate, sort, SQL)
Views: Visualizations and tables

3. Filters

Add interactive controls:

yaml

filters:
  # Dropdown select
  - type: widget
    field: category

  # Multi-select
  - type: widget
    field: region
    multiple: true

  # Date range
  - type: widget
    field: date
    widget: date_range_slider

  # Numeric slider
  - type: param
    parameter: min_revenue
    widget_type: FloatSlider
    start: 0
    end: 100000

4. Transforms

Process data in pipelines:

Common transforms:

columns: Select specific columns
query: Filter rows with pandas query
aggregate: Group and aggregate
sort: Sort data
sql: Custom SQL queries

Example:

yaml

transforms:
  - type: columns
    columns: ['date', 'region', 'revenue']

  - type: query
    query: "revenue > 1000"

  - type: aggregate
    by: ['region']
    aggregate:
      total: {revenue: sum}
      avg: {revenue: mean}

See: Data Transforms Reference for all transform types.

5. Views

Visualize data:

View types:

hvplot: Interactive plots (line, scatter, bar, etc.)
table: Data tables
indicator: KPI metrics
vega: Vega-Lite specifications
altair: Altair charts
plotly: Plotly charts

Example:

yaml

views:
  - type: hvplot
    pipeline: main
    kind: line
    x: date
    y: revenue
    by: category

  - type: indicator
    pipeline: main
    field: total_revenue
    title: Total Sales
    format: '${value:,.0f}'

See: Views Reference for all view types and options.

6. Layouts

Arrange views on the page:

yaml

layouts:
  - title: Overview
    layout: [[0, 1, 2], [3], [4, 5]]  # Grid positions
    views:
      - type: indicator
        # View 0 config...

      - type: indicator
        # View 1 config...

      - type: hvplot
        # View 2 config...

Layout types:

Grid: [[0, 1], [2, 3]]
Tabs: Multiple layouts become tabs
Responsive: Adapts to screen size

See: Layouts Reference for advanced layout patterns.

Common Patterns

Pattern 1: KPI Dashboard

yaml

sources:
  metrics:
    type: file
    tables:
      data: ./metrics.csv

pipelines:
  kpis:
    source: metrics
    table: data
    transforms:
      - type: aggregate
        aggregate:
          total_revenue: {revenue: sum}
          total_orders: {orders: sum}
          avg_order_value: {revenue: mean}

layouts:
  - title: KPIs
    layout: [[0, 1, 2]]
    views:
      - type: indicator
        pipeline: kpis
        field: total_revenue
        format: '${value:,.0f}'

      - type: indicator
        pipeline: kpis
        field: total_orders
        format: '{value:,.0f}'

      - type: indicator
        pipeline: kpis
        field: avg_order_value
        format: '${value:.2f}'

Pattern 2: Filtered Exploration

yaml

pipelines:
  explorer:
    source: mydata
    table: sales

    filters:
      - type: widget
        field: region
        label: Region

      - type: widget
        field: category
        label: Category
        multiple: true

      - type: widget
        field: date
        widget: date_range_slider

    views:
      - type: hvplot
        kind: scatter
        x: price
        y: quantity
        by: category

      - type: table
        page_size: 20

Pattern 3: Multi-Source Dashboard

yaml

sources:
  sales_db:
    type: postgres
    connection_string: postgresql://localhost/sales
    tables: [orders, customers]

  inventory_file:
    type: file
    tables:
      stock: ./inventory.csv

pipelines:
  sales_pipeline:
    source: sales_db
    table: orders

  inventory_pipeline:
    source: inventory_file
    table: stock

Pattern 4: Cross-Filtering

yaml

pipelines:
  main:
    source: data
    table: sales

    filters:
      - type: widget
        field: region

layouts:
  - title: Analysis
    views:
      # Clicking bar filters other views
      - type: hvplot
        pipeline: main
        kind: bar
        x: category
        y: revenue
        selection_group: category_filter

      # Responds to selection above
      - type: hvplot
        pipeline: main
        kind: scatter
        x: price
        y: quantity
        selection_group: category_filter

Pattern 5: SQL Transform

yaml

transforms:
  - type: sql
    query: |
      SELECT
        region,
        category,
        SUM(revenue) as total_revenue,
        COUNT(*) as order_count,
        AVG(revenue) as avg_order_value
      FROM table
      WHERE date >= '2024-01-01'
      GROUP BY region, category
      HAVING total_revenue > 10000
      ORDER BY total_revenue DESC

Python API

While Lumen is designed for YAML, you can also use Python:

python

from lumen.sources import FileSource
from lumen.pipeline import Pipeline
from lumen.views import hvPlotView
from lumen.dashboard import Dashboard
import panel as pn

# Create source
source = FileSource(tables={'sales': './data/sales.csv'})

# Create pipeline
pipeline = Pipeline(source=source, table='sales')

# Create view
view = hvPlotView(
    pipeline=pipeline,
    kind='scatter',
    x='price',
    y='quantity'
)

# Create dashboard
dashboard = Dashboard(
    pipelines={'main': pipeline},
    layouts=[view]
)

# Serve
dashboard.servable()

See: Python API Reference for detailed API usage.

Configuration

Global Config

yaml

config:
  title: My Dashboard
  theme: dark  # or 'default', 'material'
  sizing_mode: stretch_width
  logo: ./logo.png
  favicon: ./favicon.ico
  layout: column  # or 'grid', 'tabs'

Themes

yaml

config:
  theme: material
  theme_json:
    palette:
      primary: '#00aa41'
      secondary: '#616161'

Authentication

bash

# Serve with auth
lumen serve dashboard.yaml \
  --oauth-provider=generic \
  --oauth-key=${OAUTH_KEY} \
  --oauth-secret=${OAUTH_SECRET}

Deployment

Development

bash

# Local with auto-reload
lumen serve dashboard.yaml --autoreload --show

# Specific port
lumen serve dashboard.yaml --port 5007

Production

bash

# Production server
panel serve dashboard.yaml \
  --port 80 \
  --num-procs 4 \
  --allow-websocket-origin=analytics.company.com

Docker

dockerfile

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY dashboard.yaml data/ ./
CMD ["lumen", "serve", "dashboard.yaml", "--port", "5006", "--address", "0.0.0.0"]

See: Deployment Guide for production deployment best practices.

Best Practices

1. Source Organization

yaml

# ✅ Good: Descriptive names
sources:
  sales_database:
    type: postgres
    tables: [orders, customers]

  inventory_files:
    type: file
    tables:
      stock: ./inventory.csv

# ❌ Bad: Generic names
sources:
  db1:
    type: postgres
  file1:
    type: file

2. Pipeline Reusability

yaml

# Define reusable pipelines
pipelines:
  base_sales:
    source: data
    table: sales
    filters:
      - type: widget
        field: region

  summary_sales:
    pipeline: base_sales  # Extends base_sales
    transforms:
      - type: aggregate
        by: ['category']
        aggregate:
          total: {revenue: sum}

3. Performance

yaml

# Limit data size for large tables
sources:
  bigdata:
    type: postgres
    tables:
      events: "SELECT * FROM events WHERE date >= '2024-01-01' LIMIT 100000"

4. User Experience

yaml

# Provide clear labels and formatting
filters:
  - type: widget
    field: region
    label: "Sales Region"  # Clear label

views:
  - type: indicator
    field: revenue
    title: "Total Revenue"
    format: '${value:,.0f}'  # Formatted display

Troubleshooting

Dashboard Won't Load

bash

# Check YAML syntax
python -c "import yaml; yaml.safe_load(open('dashboard.yaml'))"

# Run with debug logging
lumen serve dashboard.yaml --log-level=debug

Data Not Showing

Verify data source path/connection
Check table names match YAML config
Ensure columns referenced exist in data

Performance Issues

Limit query results (use SQL WHERE clauses)
Reduce number of rows displayed
Use aggregation before visualization

See: Troubleshooting Guide for common issues.

Progressive Learning Path

Level 1: Basics

Create simple file-based dashboard
Add filters
Create basic views

Resources:

Quick Start (this doc)
Data Sources Reference

Level 2: Transforms

Filter and aggregate data
Use SQL transforms
Chain multiple transforms

Resources:

Data Transforms Reference

Level 3: Advanced Layouts

Multi-page dashboards
Cross-filtering
Custom themes

Resources:

Layouts Reference
Views Reference

Level 4: Production

Database integration
Authentication
Deployment

Resources:

Deployment Guide

Additional Resources

Documentation

Data Sources Reference - All source types and configuration
Data Transforms Reference - Complete transform reference
Views Reference - All visualization types
Layouts Reference - Layout patterns and organization
Python API Reference - Programmatic dashboard creation
Deployment Guide - Production deployment
Examples - Complete dashboard examples
Troubleshooting Guide - Common issues

External Links

Use Cases

Business Intelligence

Executive dashboards
Sales analytics
Financial reporting
Operational metrics

Data Exploration

Dataset overview
Interactive filtering
Drill-down analysis
Comparative views

Real-Time Monitoring

Live data feeds
Alert dashboards
System metrics
Performance tracking

Reporting

Scheduled reports
Standardized views
Shareable dashboards
Embedded analytics

Summary

Lumen enables rapid dashboard development through declarative YAML specifications.

Strengths:

No Python code required
Fast development cycle
Reproducible specifications
Built-in interactivity
Standard dashboard patterns

Ideal for:

Fixed dashboard layouts
Standard data patterns
Non-programmer dashboard creators
Rapid prototyping

Consider alternatives when:

Need custom application logic → Panel Dashboards
Need ad-hoc exploration → Lumen AI
Building novel interactions → Panel Dashboards

Related Skills

Lumen AI - Conversational data exploration
Panel Dashboards - Custom Python dashboards
Plotting Fundamentals - Quick plotting with hvPlot

Maintainer

majiayu000 Core maintainer

Source details

Full Name: majiayu000/claude-skill-registry
Branch: main
Path in repo: skills/data/lumen-dashboards
License: MIT License

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Install this agent skill to your Project

SKILL.md

Lumen Dashboards Skill

Overview

What is Lumen?

Lumen vs Panel vs Lumen AI

Quick Start

Installation

Your First Dashboard

Core Concepts

1. Sources

2. Pipelines

3. Filters

4. Transforms

5. Views

6. Layouts

Common Patterns

Pattern 1: KPI Dashboard

Pattern 2: Filtered Exploration

Pattern 3: Multi-Source Dashboard

Pattern 4: Cross-Filtering

Pattern 5: SQL Transform

Python API

Configuration

Global Config

Themes

Authentication

Deployment

Development

Production

Docker

Best Practices

1. Source Organization

2. Pipeline Reusability

3. Performance

4. User Experience

Troubleshooting

Dashboard Won't Load

Data Not Showing

Performance Issues

Progressive Learning Path

Level 1: Basics

Level 2: Transforms

Level 3: Advanced Layouts

Level 4: Production

Additional Resources

Documentation

External Links

Use Cases

Business Intelligence

Data Exploration

Real-Time Monitoring

Reporting

Summary

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