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home / skills / jeremylongshore / claude-code-plugins-plus-skills / visualization-best-practices

visualization-best-practices skill

/skills/12-data-analytics/visualization-best-practices

This skill guides you through visualization best practices, generates production-ready code, and validates results to boost data analytics quality.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill visualization-best-practices

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

Files (1)
SKILL.md
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---
name: "visualization-best-practices"
description: |
  Manage visualization best practices operations. Auto-activating skill for Data Analytics.
  Triggers on: visualization best practices, visualization best practices
  Part of the Data Analytics skill category. Use when working with visualization best practices functionality. Trigger with phrases like "visualization best practices", "visualization practices", "visualization".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Visualization Best Practices

## Overview

This skill provides automated assistance for visualization best practices tasks within the Data Analytics domain.

## When to Use

This skill activates automatically when you:
- Mention "visualization best practices" in your request
- Ask about visualization best practices patterns or best practices
- Need help with data analytics skills covering sql queries, data visualization, statistical analysis, and business intelligence.

## Instructions

1. Provides step-by-step guidance for visualization best practices
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with visualization best practices"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of data analytics concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **Data Analytics** skill category.
Tags: sql, analytics, visualization, statistics, bi

Overview

This skill provides automated assistance for visualization best practices within Data Analytics. It helps teams design clear, effective charts and dashboards, generate production-ready code and configurations, and validate visual outputs against common standards. Use it to speed adoption of consistent visualization patterns and reduce misinterpretation risk.

How this skill works

The skill inspects visualization goals, data shapes, and user personas to recommend chart types, encoding rules, and layout patterns. It generates example code (Python plotting libraries, Vega-Lite, or dashboard config) and performs validation checks for accessibility, scale, and labeling. It also flags common issues like overplotting, misleading axes, and inappropriate color use.

When to use it

  • Designing a new dashboard or report and choosing chart types
  • Reviewing existing visualizations for clarity, accessibility, or consistency
  • Generating production-ready plotting code or dashboard configuration
  • Validating visuals for data accuracy, scale, and labeling
  • Standardizing visualization patterns across a team or product

Best practices

  • Start with the question: identify the decision or insight the visual must support
  • Choose the simplest chart that conveys the message; avoid decorative complexity
  • Label axes, units, and sources clearly; annotate significant values or trends
  • Use perceptually uniform color palettes and ensure sufficient contrast for accessibility
  • Prefer aggregated summaries for large data and sample/zoom patterns for detail
  • Include validation checks for axis scaling, legend clarity, and data consistency

Example use cases

  • Generate a Vega-Lite spec and Python example for a time-series dashboard
  • Audit an executive report to remove misleading dual axes and normalize scales
  • Convert dense tables into clear small-multiple charts for comparison
  • Produce colorblind-safe palettes and accessibility notes for a BI dashboard
  • Create reproducible visualization code snippets for a data platform

FAQ

Which libraries or formats does the skill produce?

It generates examples for common formats such as matplotlib/seaborn, plotly, Vega-Lite, and dashboard config snippets depending on your environment.

Does it check accessibility and color contrast?

Yes. The skill validates color contrast, recommends palettes for colorblind users, and flags issues that impair readability.