plotly-visualization
Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.
Best use case
plotly-visualization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.
Teams using plotly-visualization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/plotly-visualization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How plotly-visualization Compares
| Feature / Agent | plotly-visualization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate interactive Plotly and Matplotlib visualizations from DataFrames with configurable templates and multi-format support.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```yaml
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
1. **Line Plot from DataFrame**: Load CSV/Excel → Configure columns → Generate plot
2. **Multi-Series Visualization**: Prepare df_array → Set plt_settings → Render combined plot
3. **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)Related Skills
json-visualization-dev
Develop and maintain the JSON Visualization web application - a Next.js tool for visualizing JSON/YAML/CSV/XML data as interactive graphs. Use when working with this codebase, adding features, fixing bugs, or understanding the graph visualization, data conversion, or type generation systems.
2000s-visualization-expert
Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.
bgo
Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.
u09955-decision-journal-maintenance-for-accessibility-services
Operate the "Decision Journal Maintenance for accessibility services" capability in production for accessibility services workflows. Use when mission execution explicitly requires this capability and outcomes must be reproducible, policy-gated, and handoff-ready.
u0225-oversight-uncertainty-communicator
Operate the "Oversight Uncertainty Communicator" capability in production for Human Oversight and Operator UX workflows. Use when mission execution explicitly requires this capability and outcomes must be reproducible, policy-gated, and handoff-ready.
u01482-constraint-compilation-for-healthcare-operations
Operate the "Constraint Compilation for healthcare operations" capability in production for healthcare operations workflows. Use when mission execution explicitly requires this capability and outcomes must be reproducible, policy-gated, and handoff-ready.
tzurot-council-mcp
Best practices for using the Council MCP server in Tzurot v3 development - When to consult external AI, how to structure prompts, model selection, and multi-turn conversations. Use when planning major changes or needing a second opinion.
typespec-m365-copilot-typespec-create-agent
Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot Use when: the task directly matches typespec create agent responsibilities within plugin typespec-m365-copilot. Do not use when: a more specific framework or task-focused skill is clearly a better match.
typespec-create-agent
Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot
type-inference-validation
Static type inference and validation for navigation paths
twitter-intel
Real-time X/Twitter intelligence - analyze accounts, track topics, and monitor keywords using live data. Use when you need current social media insights, competitor monitoring, or audience research.
twelve-data-automation
Automate Twelve Data tasks via Rube MCP (Composio). Always search tools first for current schemas.