# Statistical Visualization

> Statistical result visualization with Matplotlib and Plotly. For publication-ready figure palettes, venue figure QA, and manuscript figure styling, use the scientific-visualization plugin.

- Skill: `yuukias/statistical-visualization` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add yuukias/statistical-visualization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yuukias/statistical-visualization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: YuukiAS (https://skillmd.com/u/yuukias)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/yuukias/statistical-visualization

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# statistical-visualization

## Trigger Boundary

Statistical result visualization with Matplotlib and Plotly. For publication-ready figure palettes, venue figure QA, and manuscript figure styling, use the scientific-visualization plugin.

Use this aggregate Codex App skill when the task matches one of the source workflows below.

## Source Workflows

- `matplotlib`: Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Reference: `_src/mpl/source.md`
- `plotly`: Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization. Reference: `_src/plotly/source.md`

## Workflow

1. Choose the source workflow whose trigger boundary best matches the user request.
2. Read that source workflow's `source.md` before acting.
3. Load only the needed files under that workflow's copied references, scripts, assets, or evals.
4. Follow the source workflow unless the current project gives stricter instructions.