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.
/plugin marketplace add uw-ssec/rse-plugins/plugin install uw-ssec-holoviz-visualization-community-plugins-holoviz-visualization-2@uw-ssec/rse-pluginsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
Lumen is a declarative framework for creating data dashboards through YAML specifications. Build interactive data exploration dashboards without writing code - just configuration.
Lumen provides a declarative approach to building data dashboards:
| 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:
Use Panel when:
Use Lumen AI when:
pip install lumen
File: dashboard.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:
lumen serve dashboard.yaml --show
Data sources provide tables for your dashboard.
Supported sources:
Quick example:
sources:
mydata:
type: file
tables:
sales: ./data/sales.csv
See: Data Sources Reference for comprehensive source configuration.
Pipelines define data flows: Source → Filters → Transforms → Views
Basic pipeline:
pipelines:
sales_pipeline:
source: mydata
table: sales
filters:
- type: widget
field: region
transforms:
- type: aggregate
by: ['category']
aggregate:
total_sales: {revenue: sum}
Components:
Add interactive controls:
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
Process data in pipelines:
Common transforms:
columns: Select specific columnsquery: Filter rows with pandas queryaggregate: Group and aggregatesort: Sort datasql: Custom SQL queriesExample:
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.
Visualize data:
View types:
hvplot: Interactive plots (line, scatter, bar, etc.)table: Data tablesindicator: KPI metricsvega: Vega-Lite specificationsaltair: Altair chartsplotly: Plotly chartsExample:
views:
- type: hvplot
pipeline: main
kind: line
x: date
y: revenue
by: category
- type: indicator
pipeline: main
field: total_revenue
title: Total Sales
format: '${value:,.0f}'
See: Views Reference for all view types and options.
Arrange views on the page:
layouts:
- title: Overview
layout: [[0, 1, 2], [3], [4, 5]] # Grid positions
views:
- type: indicator
# View 0 config...
- type: indicator
# View 1 config...
- type: hvplot
# View 2 config...
Layout types:
[[0, 1], [2, 3]]See: Layouts Reference for advanced layout patterns.
sources:
metrics:
type: file
tables:
data: ./metrics.csv
pipelines:
kpis:
source: metrics
table: data
transforms:
- type: aggregate
aggregate:
total_revenue: {revenue: sum}
total_orders: {orders: sum}
avg_order_value: {revenue: mean}
layouts:
- title: KPIs
layout: [[0, 1, 2]]
views:
- type: indicator
pipeline: kpis
field: total_revenue
format: '${value:,.0f}'
- type: indicator
pipeline: kpis
field: total_orders
format: '{value:,.0f}'
- type: indicator
pipeline: kpis
field: avg_order_value
format: '${value:.2f}'
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
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
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
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
While Lumen is designed for YAML, you can also use 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.
config:
title: My Dashboard
theme: dark # or 'default', 'material'
sizing_mode: stretch_width
logo: ./logo.png
favicon: ./favicon.ico
layout: column # or 'grid', 'tabs'
config:
theme: material
theme_json:
palette:
primary: '#00aa41'
secondary: '#616161'
# Serve with auth
lumen serve dashboard.yaml \
--oauth-provider=generic \
--oauth-key=${OAUTH_KEY} \
--oauth-secret=${OAUTH_SECRET}
# Local with auto-reload
lumen serve dashboard.yaml --autoreload --show
# Specific port
lumen serve dashboard.yaml --port 5007
# Production server
panel serve dashboard.yaml \
--port 80 \
--num-procs 4 \
--allow-websocket-origin=analytics.company.com
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY dashboard.yaml data/ ./
CMD ["lumen", "serve", "dashboard.yaml", "--port", "5006", "--address", "0.0.0.0"]
See: Deployment Guide for production deployment best practices.
# ✅ Good: Descriptive names
sources:
sales_database:
type: postgres
tables: [orders, customers]
inventory_files:
type: file
tables:
stock: ./inventory.csv
# ❌ Bad: Generic names
sources:
db1:
type: postgres
file1:
type: file
# Define reusable pipelines
pipelines:
base_sales:
source: data
table: sales
filters:
- type: widget
field: region
summary_sales:
pipeline: base_sales # Extends base_sales
transforms:
- type: aggregate
by: ['category']
aggregate:
total: {revenue: sum}
# Limit data size for large tables
sources:
bigdata:
type: postgres
tables:
events: "SELECT * FROM events WHERE date >= '2024-01-01' LIMIT 100000"
# Provide clear labels and formatting
filters:
- type: widget
field: region
label: "Sales Region" # Clear label
views:
- type: indicator
field: revenue
title: "Total Revenue"
format: '${value:,.0f}' # Formatted display
# Check YAML syntax
python -c "import yaml; yaml.safe_load(open('dashboard.yaml'))"
# Run with debug logging
lumen serve dashboard.yaml --log-level=debug
See: Troubleshooting Guide for common issues.
Resources:
Resources:
Resources:
Resources:
Lumen enables rapid dashboard development through declarative YAML specifications.
Strengths:
Ideal for:
Consider alternatives when:
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