How do I install the Plotly Visualization skill?

Run npx skillmds@latest add diegosouzapw/plotly-visualization in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Plotly Visualization safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Plotly Visualization?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Plotly Visualization free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Plotly Visualization?

diegosouzapw (@diegosouzapw) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Plotly Visualization by diegosouzapw

Name: Plotly Visualization
Rating: 4.2 (5 reviews)
Author: diegosouzapw

File contents

Plotly Visualization Skill

Overview

This skill provides comprehensive visualization capabilities using both Plotly (interactive) and Matplotlib (static) backends. It enables generation of line plots, scatter plots, polar plots, bar charts, timelines, and multi-series visualizations from pandas DataFrames with YAML-driven configuration.

Key Components

Visualization Class (visualizations.py)

Main matplotlib-based visualization engine:

generate_time_line(data, plt_settings) - Create timeline visualizations from DataFrame
from_df_array(df_array, plt_settings) - Plot multiple DataFrames as array
from_df_columns(df, plt_settings) - Generate line, scatter, polar, or bar plots from DataFrame columns

VisualizationTemplatesPlotly (visualization_templates_plotly.py)

Plotly template generator for interactive charts:

get_xy_line_df(custom_analysis_dict) - XY line plot templates
get_x_datetime_input_plotly(custom_analysis_dict) - DateTime-based plot templates

Specialized Modules

visualization_xy.py - XY coordinate plotting
visualization_polar.py - Polar coordinate systems
visualization_common.py - Shared utilities

Usage Patterns

YAML Configuration Structure

visualization:
  type: line  # line, scatter, polar, bar
  x_column: timestamp
  y_columns:
    - value1
    - value2
  title: "Analysis Results"
  interactive: true  # Use Plotly vs Matplotlib

Common Workflows

Line Plot from DataFrame: Load CSV/Excel → Configure columns → Generate plot
Multi-Series Visualization: Prepare df_array → Set plt_settings → Render combined plot
Timeline Generation: DataFrame with dates → generate_time_line() → Export

Module Location

Primary: src/assetutilities/common/visualizations.py
Templates: src/assetutilities/common/visualization/visualization_templates_plotly.py
XY Plots: src/assetutilities/common/visualization/visualization_xy.py
Polar Plots: src/assetutilities/common/visualization/visualization_polar.py

Dependencies

matplotlib (static plots)
plotly (interactive plots)
pandas (DataFrame handling)
numpy (numerical operations)

---name: plotly-visualizationdescription: Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.---# Plotly Visualization Skill## OverviewThis skill provides comprehensive visualization capabilities using both Plotly (interactive) and Matplotlib (static) backends. It enables generation of line plots, scatter plots, polar plots, bar charts, timelines, and multi-series visualizations from pandas DataFrames with YAML-driven configuration.## Key Components### Visualization Class (visualizations.py)Main matplotlib-based visualization engine:- `generate_time_line(data, plt_settings)` - Create timeline visualizations from DataFrame- `from_df_array(df_array, plt_settings)` - Plot multiple DataFrames as array- `from_df_columns(df, plt_settings)` - Generate line, scatter, polar, or bar plots from DataFrame columns### VisualizationTemplatesPlotly (visualization_templates_plotly.py)Plotly template generator for interactive charts:- `get_xy_line_df(custom_analysis_dict)` - XY line plot templates- `get_x_datetime_input_plotly(custom_analysis_dict)` - DateTime-based plot templates### Specialized Modules- `visualization_xy.py` - XY coordinate plotting- `visualization_polar.py` - Polar coordinate systems- `visualization_common.py` - Shared utilities## Usage Patterns### YAML Configuration Structure```yamlvisualization: type: line # line, scatter, polar, bar x_column: timestamp y_columns: - value1 - value2 title: "Analysis Results" interactive: true # Use Plotly vs Matplotlib```### Common Workflows1. **Line Plot from DataFrame**: Load CSV/Excel → Configure columns → Generate plot2. **Multi-Series Visualization**: Prepare df_array → Set plt_settings → Render combined plot3. **Timeline Generation**: DataFrame with dates → generate_time_line() → Export## Module Location- Primary: `src/assetutilities/common/visualizations.py`- Templates: `src/assetutilities/common/visualization/visualization_templates_plotly.py`- XY Plots: `src/assetutilities/common/visualization/visualization_xy.py`- Polar Plots: `src/assetutilities/common/visualization/visualization_polar.py`## Dependencies- matplotlib (static plots)- plotly (interactive plots)- pandas (DataFrame handling)- numpy (numerical operations)

Frequently asked questions

Plotly Visualization