A Python-based utility for converting multi-set Venn diagrams into clean, matrix-based Upset Plots for better data visualization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install upset-plot-converter
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install upset-plot-converter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Upset Plot Converter?
The Upset Plot Converter is a specialized tool within the Openclaw Skills library designed to address the limitations of traditional Venn diagrams. When dealing with five or more sets, Venn diagrams become visually cluttered and difficult to interpret. This skill provides a structured way to generate Upset Plots, which use a matrix layout to represent intersections and bar charts to show subset sizes, ensuring clarity regardless of set count.
Built for data scientists and researchers, this skill emphasizes reproducibility and structured execution. It allows users to take complex overlapping data and generate publication-quality PNG visualizations while maintaining a clear audit trail of assumptions and execution steps.
Upset Plot Converter Use Cases
- Converting complex Venn diagrams with more than 4 sets into readable Upset Plots.
- Analyzing gene set overlaps or biological pathways in scientific research.
- Visualizing customer segments and overlapping audience data for marketing analytics.
- Creating reproducible data visualizations for technical reports and academic papers.
How Upset Plot Converter Works
- The user provides set data in the form of dictionaries or lists along with an output path.
- The skill validates the objective and ensures all necessary Python dependencies like matplotlib and pandas are present.
- The packaged main.py script processes the data to calculate intersections between the provided sets.
- A matrix-based visualization is generated, automatically sorting intersections by size for better readability.
- The final PNG artifact is saved to the workspace, accompanied by a structured summary of the workflow and any assumptions made during execution.
Upset Plot Converter Setup
To use this component of Openclaw Skills, navigate to the skill directory and install the requirements:
cd "scientific-skills/Data Analytics/upset-plot-converter"
pip install -r requirements.txt
Verify the script can be parsed before running:
python -m py_compile scripts/main.py
Upset Plot Converter Data Schema & Taxonomy
The skill organizes data based on set membership and intersection counts. It supports two primary input formats:
- Sets Dictionary: A mapping of set names to sets or lists of elements.
- Lists and Names: A separate list of set names and a list of lists containing the elements.
| Parameter | Type | Description |
|---|---|---|
| sets | dict | Dictionary of set names to element collections |
| output_path | string | The file path where the PNG will be saved |
| min_subset_size | int | Minimum size of intersection to display (default: 1) |
| max_intersections | int | Limit of intersections shown (default: 30) |
Upset Plot Converter Advanced Features
- Multi-format input support for both dictionary-based and list-based set data.
- Automatic intersection sorting to highlight the most significant data overlaps first.
- Configurable visualization thresholds including minimum subset size and maximum intersection count.
- Audit-ready command structure for integration into automated data pipelines within Openclaw Skills.
SKILL.md
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