home / skills / pluginagentmarketplace / custom-plugin-data-analyst / visualization

visualization skill

safe

This skill helps you design and communicate data insights through visual storytelling, choosing effective charts, colors, and dashboards using modern tools.

npx playbooks add skill pluginagentmarketplace/custom-plugin-data-analyst --skill visualization

Review the files below or copy the command above to add this skill to your agents.

Files (7)

SKILL.md

2.6 KB

---
name: visualization
description: Data visualization design, tools, and storytelling for impactful analytics presentations
version: "2.0.0"
sasmp_version: "2.0.0"
bonded_agent: 04-visualization-architect
bond_type: PRIMARY_BOND

# Skill Configuration
config:
  atomic: true
  retry_enabled: true
  max_retries: 3
  backoff_strategy: exponential

# Parameter Validation
parameters:
  tool_focus:
    type: string
    required: true
    enum: [tableau, powerbi, python, r, all]
    default: all
  skill_level:
    type: string
    required: true
    enum: [beginner, intermediate, advanced]
    default: beginner
  audience_type:
    type: string
    required: false
    enum: [executive, technical, business, public]
    default: business

# Observability
observability:
  logging_level: info
  metrics: [dashboard_views, interaction_rate, load_time]
---

# Data Visualization Skill

## Overview
Master the art and science of data visualization to communicate insights effectively using modern tools and design principles.

## Core Topics

### Visualization Principles
- Chart selection guidelines
- Color theory for data visualization
- Visual hierarchy and attention
- Accessibility in visualization

### Tools & Platforms
- Tableau (dashboards, calculated fields, LOD expressions)
- Power BI (DAX, data modeling, reports)
- Python (Matplotlib, Seaborn, Plotly)
- R (ggplot2, Shiny)

### Chart Types
- Comparison charts (bar, column, dot plot)
- Trend charts (line, area, slope)
- Distribution charts (histogram, box plot, violin)
- Relationship charts (scatter, bubble, heatmap)
- Composition charts (pie, treemap, stacked bar)

### Data Storytelling
- Narrative structure for data presentations
- Annotation and callout techniques
- Interactive dashboard design
- Executive presentation best practices

## Learning Objectives
- Select appropriate visualization for data and audience
- Create professional dashboards in Tableau and Power BI
- Design effective data stories
- Apply visualization best practices

## Error Handling

| Error Type | Cause | Recovery |
|------------|-------|----------|
| Data connection failed | Source unavailable | Check connection, use cached data |
| Slow dashboard | Too much data | Aggregate, filter, or use extracts |
| Chart unreadable | Poor design choice | Apply chart selection guidelines |
| Accessibility issue | Color/contrast | Use colorblind-safe palette |
| Mobile display broken | Non-responsive | Redesign for mobile-first |

## Related Skills
- statistics (for data to visualize)
- programming (for programmatic visualization)
- career (for presenting to stakeholders)

Overview

This skill teaches practical data visualization design, tool usage, and storytelling to make analytics clear and actionable. It covers chart selection, color and accessibility best practices, and hands-on use of popular tools like Tableau, Power BI, Python, and R. The goal is to help analysts build dashboards and presentations that drive decisions.

How this skill works

The skill inspects your data and guides you to appropriate chart types and layout patterns based on purpose, audience, and dataset characteristics. It offers tool-specific techniques (calculated fields, DAX, Plotly/ggplot2 syntax) and prescriptive fixes for common issues like slow dashboards, unreadable charts, and accessibility problems. It emphasizes narrative structure and interactive design to turn visualizations into persuasive stories.

When to use it

When choosing the right chart to answer a specific question or compare groups
When building dashboards for executives, analysts, or public audiences
When converting analysis into a concise, visual data story for presentations
When improving accessibility, responsiveness, or performance of visualizations
When learning tool-specific techniques in Tableau, Power BI, Python, or R

Best practices

Start with the question and audience, then pick the simplest chart that answers it
Use visual hierarchy and annotation to guide attention to key insights
Prefer colorblind-safe palettes and sufficient contrast for accessibility
Aggregate or filter large datasets to improve dashboard performance
Test visualizations on target devices and simplify layouts for mobile

Example use cases

Designing an executive dashboard in Tableau that highlights KPIs and trends
Creating interactive Power BI reports with DAX measures and drill-through paths
Producing publication-quality charts in Python using Seaborn or Plotly
Crafting a data story slide deck with annotated visuals and clear takeaways
Fixing a slow, cluttered dashboard by aggregating data and redesigning charts

FAQ

How do I choose between bar, line, and scatter charts?

Choose bar charts for categorical comparisons, line charts for time trends, and scatter plots for relationships between two continuous variables.

What are quick fixes for an unreadable chart?

Increase contrast, reduce clutter, label axes clearly, remove unnecessary gridlines, and annotate the key insight.

How can I make dashboards faster with large datasets?

Use extracts or aggregated tables, apply server-side filters, limit visuals that query raw rows, and pre-compute heavy calculations.