name: data-viz-agent
description: |
Data visualization engineer agent using D3.js, Chart.js, Matplotlib, and Plotly.
Applies Cleveland-McGill perceptual hierarchy, WCAG AA contrast, and colorblind-safe
palettes. Selects the right chart type for the data relationship.
triggers:
- "data visualization agent"
- "chart agent"
- "visualization engineer"
- "Chart.js"
- "Plotly"
- "Matplotlib"
- "data chart"
das:
category: data-visualization
upstream: "https://github.com/rohitg00/awesome-claude-code-toolkit"
upstream_path: "agents/data-ai/data-visualization.md"
version: latest
install: true
data-viz-agent
Catalogue stub — full skill: rohitg00/awesome-claude-code-toolkit
Decision tree
-
Is the full skill already installed?
Check whether the skill at this location still has a das: block:
- Global:
grep -q "^das:" ~/.agents/skills/data-viz-agent/SKILL.md 2>/dev/null && echo "pointer" || echo "installed"
- Project:
grep -q "^das:" .agents/skills/data-viz-agent/SKILL.md 2>/dev/null && echo "pointer" || echo "installed"
- No
das: block → full skill installed, invoke it and proceed
das: block present → go to step 2
-
Detect scope, then install:
To detect scope:
[ -e ~/.agents/skills/data-viz-agent ] && echo "global" || echo "project"
Global (installed with -g):
npx skills add rohitg00/awesome-claude-code-toolkit --skill data-viz-agent -g -y
Project (installed without -g):
npx skills add rohitg00/awesome-claude-code-toolkit --skill data-viz-agent -y
Claude Code: send either command as a chat message starting with ! to run it without leaving the conversation.
Invoke after install
- Skill name:
data-viz-agent
- Trigger phrases: "data visualization agent", "Chart.js", "Plotly", "Matplotlib", "data chart"
What it does
Full data visualization engineering agent that works across D3.js, Chart.js, Matplotlib, and Plotly. Applies the Cleveland-McGill perceptual accuracy hierarchy to choose chart types, enforces WCAG AA contrast for accessibility, uses colorblind-safe palettes by default, and selects the right visualization for the underlying data relationship (comparison, distribution, composition, relationship).