Themes
by kpbray
Creates Power BI report themes for consistent styling. Use for colors, fonts, visual defaults, and corporate branding.
Skill Details
Repository Files
4 files in this skill directory
name: themes description: "Creates Power BI report themes for consistent styling. Use for colors, fonts, visual defaults, and corporate branding."
Themes Skill
This skill helps create Power BI report themes for consistent visual styling and branding.
When to Use This Skill
- Creating custom color palettes
- Setting default fonts and sizes
- Applying corporate branding
- Configuring visual defaults
- Creating dark/light mode themes
- Standardizing conditional formatting
Theme File Basics
File Format
Themes are JSON files with .json extension. They can be:
- Imported into Power BI Desktop (View > Themes > Browse for themes)
- Embedded in PBIR reports
- Shared across an organization
Basic Structure
{
"name": "Custom Theme",
"dataColors": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2"],
"background": "#FFFFFF",
"foreground": "#252423",
"tableAccent": "#118DFF"
}
Theme Properties
Core Colors
| Property | Description |
|---|---|
dataColors |
Array of colors for data series |
background |
Default background color |
foreground |
Default text color |
tableAccent |
Accent color for tables/matrices |
hyperlink |
Link color |
good |
Positive/good indicator |
neutral |
Neutral indicator |
bad |
Negative/bad indicator |
Typography
{
"textClasses": {
"label": {
"fontFace": "Segoe UI",
"fontSize": 12,
"color": "#252423"
},
"title": {
"fontFace": "Segoe UI Semibold",
"fontSize": 14,
"color": "#252423"
},
"header": {
"fontFace": "Segoe UI Semibold",
"fontSize": 12,
"color": "#252423"
},
"callout": {
"fontFace": "Segoe UI Light",
"fontSize": 28,
"color": "#252423"
},
"largeTitle": {
"fontFace": "Segoe UI Light",
"fontSize": 40,
"color": "#252423"
}
}
}
Text Classes
| Class | Used For |
|---|---|
label |
Data labels, axis labels |
title |
Visual titles |
header |
Table/matrix headers |
callout |
Card values, KPI values |
largeTitle |
Large display values |
Complete Theme Structure
{
"name": "Corporate Theme",
"dataColors": [
"#0078D4",
"#00BCF2",
"#00B294",
"#FFB900",
"#E81123",
"#5C2D91",
"#B4009E",
"#107C10"
],
"background": "#FFFFFF",
"foreground": "#323130",
"tableAccent": "#0078D4",
"hyperlink": "#0078D4",
"good": "#107C10",
"neutral": "#FFB900",
"bad": "#E81123",
"maximum": "#107C10",
"center": "#FFB900",
"minimum": "#E81123",
"textClasses": {
"label": {
"fontFace": "Segoe UI",
"fontSize": 11,
"color": "#605E5C"
},
"title": {
"fontFace": "Segoe UI Semibold",
"fontSize": 14,
"color": "#323130"
},
"header": {
"fontFace": "Segoe UI Semibold",
"fontSize": 12,
"color": "#323130"
},
"callout": {
"fontFace": "Segoe UI",
"fontSize": 28,
"color": "#323130"
}
},
"visualStyles": {
"*": {
"*": {
"background": [{
"color": { "solid": { "color": "#FFFFFF" } },
"transparency": 0
}],
"border": [{
"show": false
}],
"dropShadow": [{
"show": false
}]
}
}
}
}
Visual Styles
Structure
{
"visualStyles": {
"<visualType>": {
"<propertyGroup>": {
"<property>": [{ <settings> }]
}
}
}
}
Wildcards
| Wildcard | Meaning |
|---|---|
"*" |
All visuals / all properties |
Common Visual Types
| Type | Description |
|---|---|
"*" |
All visuals |
"page" |
Report page |
"card" |
Card visual |
"multiRowCard" |
Multi-row card |
"columnChart" |
Column chart |
"barChart" |
Bar chart |
"lineChart" |
Line chart |
"pieChart" |
Pie chart |
"donutChart" |
Donut chart |
"tableEx" |
Table |
"pivotTable" |
Matrix |
"slicer" |
Slicer |
"kpi" |
KPI visual |
"gauge" |
Gauge |
"map" |
Map |
"shape" |
Shape |
"textbox" |
Text box |
Common Property Groups
| Group | Properties |
|---|---|
background |
Visual background |
border |
Visual border |
dropShadow |
Shadow effect |
title |
Visual title |
subTitle |
Subtitle |
legend |
Legend settings |
categoryAxis |
X-axis |
valueAxis |
Y-axis |
labels |
Data labels |
dataPoint |
Data point colors |
Setting Defaults for All Visuals
{
"visualStyles": {
"*": {
"*": {
"background": [{
"color": { "solid": { "color": "#FFFFFF" } },
"transparency": 0
}],
"border": [{
"show": true,
"color": { "solid": { "color": "#E0E0E0" } },
"radius": 5
}],
"dropShadow": [{
"show": true,
"color": { "solid": { "color": "#000000" } },
"position": "Outer",
"preset": "BottomRight",
"transparency": 80
}],
"title": [{
"show": true,
"fontColor": { "solid": { "color": "#252423" } },
"fontSize": 14,
"fontFamily": "Segoe UI Semibold",
"alignment": "left"
}]
}
}
}
}
Card Visual Styling
{
"visualStyles": {
"card": {
"*": {
"labels": [{
"color": { "solid": { "color": "#0078D4" } },
"fontSize": 32
}],
"categoryLabels": [{
"show": true,
"color": { "solid": { "color": "#605E5C" } },
"fontSize": 12
}],
"background": [{
"color": { "solid": { "color": "#F3F2F1" } }
}]
}
}
}
}
Chart Styling
{
"visualStyles": {
"columnChart": {
"*": {
"categoryAxis": [{
"show": true,
"labelColor": { "solid": { "color": "#605E5C" } },
"fontSize": 11,
"gridlineShow": false
}],
"valueAxis": [{
"show": true,
"labelColor": { "solid": { "color": "#605E5C" } },
"fontSize": 11,
"gridlineShow": true,
"gridlineColor": { "solid": { "color": "#E0E0E0" } }
}],
"legend": [{
"show": true,
"position": "Top",
"fontSize": 11,
"fontColor": { "solid": { "color": "#605E5C" } }
}],
"labels": [{
"show": false
}]
}
}
}
}
Table Styling
{
"visualStyles": {
"tableEx": {
"*": {
"grid": [{
"gridVertical": true,
"gridVerticalColor": { "solid": { "color": "#E0E0E0" } },
"gridHorizontal": true,
"gridHorizontalColor": { "solid": { "color": "#E0E0E0" } },
"rowPadding": 4
}],
"columnHeaders": [{
"fontColor": { "solid": { "color": "#FFFFFF" } },
"backColor": { "solid": { "color": "#0078D4" } },
"fontSize": 12,
"bold": true
}],
"values": [{
"fontColor": { "solid": { "color": "#323130" } },
"fontSize": 11
}],
"total": [{
"fontColor": { "solid": { "color": "#323130" } },
"backColor": { "solid": { "color": "#E0E0E0" } },
"bold": true
}]
}
}
}
}
Slicer Styling
{
"visualStyles": {
"slicer": {
"*": {
"header": [{
"show": true,
"fontColor": { "solid": { "color": "#252423" } },
"fontSize": 14
}],
"items": [{
"fontColor": { "solid": { "color": "#323130" } },
"fontSize": 11
}],
"selection": [{
"selectAllCheckboxEnabled": true,
"singleSelect": false
}]
}
}
}
}
Page Background
{
"visualStyles": {
"page": {
"*": {
"background": [{
"color": { "solid": { "color": "#F3F2F1" } },
"transparency": 0
}],
"wallpaper": [{
"color": { "solid": { "color": "#F3F2F1" } }
}]
}
}
}
}
Color Palettes
Microsoft Default
{
"dataColors": [
"#118DFF",
"#12239E",
"#E66C37",
"#6B007B",
"#E044A7",
"#744EC2",
"#D9B300",
"#D64550"
]
}
Corporate Blue
{
"dataColors": [
"#0078D4",
"#00BCF2",
"#004578",
"#5C2D91",
"#B4009E",
"#008272",
"#00B294",
"#002050"
]
}
Earth Tones
{
"dataColors": [
"#5B5EA6",
"#9B2335",
"#00A4CC",
"#E4712B",
"#3A4D39",
"#8B6914",
"#6B4226",
"#4A5859"
]
}
Colorblind-Friendly
{
"dataColors": [
"#000000",
"#E69F00",
"#56B4E9",
"#009E73",
"#F0E442",
"#0072B2",
"#D55E00",
"#CC79A7"
]
}
Dark Theme Colors
{
"dataColors": [
"#00BCF2",
"#00CC6A",
"#FFB900",
"#E81123",
"#B4009E",
"#10893E",
"#FF8C00",
"#6B69D6"
],
"background": "#1E1E1E",
"foreground": "#FFFFFF",
"tableAccent": "#00BCF2"
}
Conditional Formatting Colors
Diverging (Bad to Good)
{
"bad": "#E81123",
"neutral": "#FFB900",
"good": "#107C10"
}
Sequential
{
"minimum": "#FFFFFF",
"center": "#00BCF2",
"maximum": "#0078D4"
}
Using Themes in PBIR
Reference in report.json
{
"$schema": "https://developer.microsoft.com/json-schemas/fabric/item/report/definition/report/1.0.0/schema.json",
"themeCollection": {
"baseTheme": {
"name": "CY24SU06",
"reportVersionAtImport": "5.55",
"type": "SharedResources"
},
"customTheme": {
"name": "CustomTheme",
"reportVersionAtImport": "5.55",
"type": "Custom"
}
}
}
Embed Theme in Report
Store custom theme file in StaticResources/ folder of the report.
Best Practices
Color Guidelines
- Use 6-8 data colors - Enough for variety, not overwhelming
- Ensure contrast - 4.5:1 minimum for accessibility
- Consistent semantics - Red=bad, Green=good across report
- Consider colorblindness - Use patterns or labels too
Typography Guidelines
- Limit font families - 1-2 maximum
- Use system fonts - Segoe UI, Arial for consistency
- Establish hierarchy - Titles > Headers > Body
- Readable sizes - Minimum 10pt for data labels
Visual Defaults
- Remove unnecessary elements - Hide borders, shadows by default
- Consistent spacing - Same padding/margins
- Align to grid - Use consistent positions
- Default titles on - Help users understand visuals
Boundaries and Constraints
DO
- Test themes across all visual types
- Verify accessibility (contrast ratios)
- Document color meanings
- Keep color count manageable
- Test on different screen sizes
DO NOT
- Don't use too many colors (max 8-10)
- Avoid low contrast combinations
- Don't mix font families excessively
- Avoid theme-breaking individual visual formats
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
