AutoMD-Viz is an automated toolkit for creating high-resolution, journal-specific molecular dynamics visualizations and comprehensive analysis reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install automd-viz
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 automd-viz 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 AutoMD-Viz?
AutoMD-Viz is a professional-grade visualization engine designed to bridge the gap between raw molecular dynamics (MD) data and publication-ready graphics. As a standout entry in the Openclaw Skills library, it automates the tedious process of formatting molecular structures, time-series plots, and dimensionality reduction maps to meet the strict aesthetic requirements of top-tier scientific journals like Nature, Science, and Cell.
By leveraging industry-standard backends like PyMOL for 3D structures and Matplotlib/Seaborn for data plotting, AutoMD-Viz ensures that every output is both scientifically accurate and visually compelling. Whether you are visualizing protein folds or complex free energy landscapes, this Openclaw Skills tool provides a streamlined CLI interface to generate high-DPI raster images and scalable vector graphics (SVG/PDF/EPS) effortlessly.
AutoMD-Viz Use Cases
- Creating high-resolution (300-600 DPI) protein structure figures for manuscript submissions.
- Plotting RMSD, RMSF, and energy data with journal-specific font and color palettes.
- Executing dimensionality reduction (PCA, t-SNE, UMAP) on trajectory files to visualize conformational space.
- Generating automated, consolidated MD visualization reports from raw GROMACS analysis output.
- Standardizing figure styles across a research team to maintain consistent publication branding.
How AutoMD-Viz Works
- The user provides raw simulation data such as PDB/GRO structures, XTC/TRR trajectories, or XVG data files.
- The Openclaw Skills execution engine parses the input parameters, including the desired visualization type (structure, data, trajectory, or report).
- AutoMD-Viz applies the selected journal style (Nature, Science, or Cell), adjusting fonts, line widths, and color schemes automatically.
- Advanced algorithms perform dimensionality reduction or structural rendering using integrated backends like scikit-learn or PyMOL.
- The tool generates final outputs in both vector and raster formats, alongside session files for further manual refinement.
AutoMD-Viz Setup
To install AutoMD-Viz through the Openclaw Skills registry, use the following commands:
# Via ClawHub
clawhub install automd-viz
# Manual installation
git clone https://github.com/Billwanttobetop/automd-viz.git
cd automd-viz
chmod +x automd-viz.sh
# Install python dependencies
pip install numpy matplotlib seaborn scikit-learn umap-learn MDAnalysis
AutoMD-Viz Data Schema & Taxonomy
AutoMD-Viz organizes outputs systematically to ensure reproducibility. Below is the primary output taxonomy:
| Output Type | File Extensions | Description |
|---|---|---|
| Structures | .png, .pse | High-res renders and PyMOL session files. |
| Data Plots | .pdf, .png | Vector and raster plots of time-series data. |
| Trajectories | 2D/3D projections and Free Energy Landscapes. | |
| Reports | .md, /figures/ | Comprehensive markdown summary and figure directory. |
Data is processed using XVG (Gromacs), PDB/GRO (Structure), and XTC/TRR (Trajectory) formats.
AutoMD-Viz Advanced Features
- Seamless integration with AutoMD-GROMACS for end-to-end simulation-to-publication pipelines.
- Support for multiple dimensionality reduction methods including PCA, t-SNE, and UMAP.
- Automated Free Energy Landscape (FEL) generation from trajectory data.
- Customizable journal style presets that automate font (Arial/Helvetica) and line weight configurations.
- Batch processing mode for generating complete report directories from multi-step analysis results.
SKILL.md
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