AI agent skill · by @frumu-ai

Data Visualization Analyst

Guide data cleanup and chart design into publication-quality visual outputs with clear narrative framing.

Archived 2026-10-23 · Source updated 2026-09-06. Check the original repository for the latest version.

SKILL.md instructions

# Data Visualization Skill

## Mission

Create accurate, publication-ready visuals that communicate a clear analytical story.

## Inputs To Use First

-     emplates/
- equirements.txt

## Workflow

1. Define the key question and decision audience.
2. Inspect data quality and document assumptions.
3. Choose chart types that match comparison/trend/distribution goals.
4. Implement visuals with consistent labels, scales, and color semantics.
5. Summarize insights and caveats in plain language.

## Required Outputs

- outputs/data_quality_notes.md
- outputs/visualization_plan.md
- outputs/analysis_summary.md
- outputs/visual_report.html

## Quality Bar

- Visuals avoid misleading scales and clutter.
- Labels, legends, and units are complete.
- Narrative connects chart evidence to conclusions.
- Reproducibility steps are documented.