AutoMD-Viz

Generate publication-quality molecular dynamics visualizations including structures, data plots, trajectory projections, and full reports with journal-specif...

Security Scan

AutoMD-Viz appears internally consistent with its stated purpose (publication-quality MD visualizations); it asks for no unrelated secrets or installs and its runtime instructions match the description, though the shell script has a few minor robustness/safety issues you should review before running on untrusted data.

Purpose & Capability

Name/description (MD visualization, journal styles) align with the provided SKILL.md, README, and the included automd-viz.sh. Required tools (Python, PyMOL, VMD, Matplotlib/Seaborn) are exactly what a visualization tool needs. The repository reference and examples consistently target MD workflows and AutoMD-GROMACS integration.

Instruction Scope

SKILL.md and automd-viz.sh instruct the agent to run local command-line tools and Python code to read local structure/trajectory/data files and produce images/reports — all within the stated scope. The only scope notes: it expects precomputed projection files (projection_pca.xvg) and integration with external analysis tools (AutoMD-GROMACS/advanced-analysis); it does not attempt to read unrelated system files or exfiltrate data. However, the shell script embeds user-supplied filenames/parameters directly into here-docs and inline Python code without explicit sanitization, which is a robustness/security consideration if used with untrusted filenames/inputs.

Install Mechanism

No install specification is present; this is instruction-only plus a shell entrypoint. SKILL.md suggests installing Python packages via pip (a standard public registry) or cloning from GitHub. No downloads from obscure URLs or archive extraction instructions are present.

Credentials

The skill declares no required environment variables, credentials, or config paths. Runtime behavior uses only local files and standard tool invocation; there are no requests for unrelated secrets or cloud credentials.

Persistence & Privilege

Flags: always is false and the skill is user-invocable; it does not request persistent elevated privileges or modify other skills' configuration. Autonomous invocation is allowed by default but is not combined with other concerning factors.

Assessment

This skill looks coherent for MD visualization and doesn't ask for secrets. Before installing/running: 1) Verify the upstream GitHub repo and author if you need provenance (SKILL.md points at a GitHub URL but the package source was listed as unknown). 2) Inspect automd-viz.sh yourself (included) — it writes and executes PyMOL scripts and runs inline Python; filenames and parameters are interpolated into scripts, so avoid passing untrusted filenames (a malicious filename could break quoting). 3) Install Python dependencies in a virtualenv/conda environment to avoid affecting system packages. 4) Run first on sample or sandboxed data to ensure behavior matches expectations. If you need higher assurance, request the upstream repo and compare the packaged files to its canonical source and check for any additional commits or network calls not present in these files.

AutoMD-Viz - Publication-Quality Visualization for Molecular Dynamics

Version: 1.0.0
Author: Xuan Guo ([e-mail])
License: MIT
Repository: https://github.com/Billwanttobetop/automd-viz

---

📖 Overview

AutoMD-Viz is a standalone visualization toolkit for generating publication-quality figures from molecular dynamics simulation data. It supports multiple visualization types and journal-specific styles (Nature, Science, Cell).

Key Features:

• 🎨 Molecular structure visualization (PyMOL)
• 📊 Data plotting (Matplotlib/Seaborn)
• 🎬 Trajectory visualization (PCA/t-SNE/UMAP)
• 📦 Automated report generation
• 🎯 Journal-specific styles (Nature/Science/Cell)
• 🔧 High-resolution output (300-600 DPI, SVG/PDF/EPS)

---

🚀 Quick Start

Installation

# Via ClawHub
clawhub install automd-viz

# Or manual installation
git clone https://github.com/Billwanttobetop/automd-viz.git
cd automd-viz
chmod +x automd-viz.sh

Basic Usage

# Generate protein structure figure
./automd-viz.sh --type structure --structure protein.pdb --style nature

# Plot RMSD/RMSF data
./automd-viz.sh --type data --input rmsd.xvg --style science

# Trajectory visualization (PCA)
./automd-viz.sh --type trajectory --structure protein.pdb --trajectory md.xtc

# Generate complete report
./automd-viz.sh --type report --structure protein.pdb --trajectory md.xtc --style nature

---

📋 Visualization Types

1. Structure Visualization (--type structure)

Generate high-quality molecular structure figures using PyMOL.

Options:

• --structure <file> - Input structure (PDB/GRO)
• --style <nature|science|cell> - Journal style
• --representation <cartoon|surface|sticks> - Display style
• --color <spectrum|chain|secondary> - Coloring scheme
• --resolution <300|600> - Output DPI

Example:

./automd-viz.sh --type structure \
  --structure protein.pdb \
  --style nature \
  --representation cartoon \
  --color spectrum \
  --resolution 600

Output:

• structure_nature.png (high-resolution raster)
• structure_nature.pse (PyMOL session)

---

2. Data Plotting (--type data)

Plot time-series data (RMSD, RMSF, energy, etc.) with journal-quality formatting.

Options:

• --input <file> - Input data file (XVG format)
• --style <nature|science|cell> - Journal style
• --xlabel <text> - X-axis label
• --ylabel <text> - Y-axis label
• --title <text> - Plot title

Example:

./automd-viz.sh --type data \
  --input rmsd.xvg \
  --style science \
  --xlabel "Time (ns)" \
  --ylabel "RMSD (nm)"

Output:

• data_plot.pdf (vector graphics)
• data_plot.png (raster graphics)

---

3. Trajectory Visualization (--type trajectory)

Visualize trajectory in reduced dimensionality space (PCA/t-SNE/UMAP).

Options:

• --structure <file> - Reference structure
• --trajectory <file> - Trajectory file (XTC/TRR)
• --method <pca|tsne|umap> - Dimensionality reduction method
• --style <nature|science|cell> - Journal style

Example:

./automd-viz.sh --type trajectory \
  --structure protein.pdb \
  --trajectory md.xtc \
  --method pca \
  --style nature

Output:

• trajectory_pca_2d.pdf (2D projection)
• trajectory_pca_3d.pdf (3D projection)
• free_energy_landscape.pdf (FEL)

---

4. Automated Report (--type report)

Generate a complete set of publication-ready figures.

Options:

• --structure <file> - Reference structure
• --trajectory <file> - Trajectory file
• --input <dir> - Analysis results directory
• --style <nature|science|cell> - Journal style

Example:

./automd-viz.sh --type report \
  --structure protein.pdb \
  --trajectory md.xtc \
  --input analysis-results/ \
  --style nature

Output:

• figures/ directory with all figures
• VISUALIZATION_REPORT.md (summary)

---

🎨 Journal Styles

Nature Style

• Font: Arial
• Font size: 7-9 pt
• Line width: 0.5-1.0 pt
• Color: Colorblind-friendly palette
• Format: PDF/EPS (vector)

Science Style

• Font: Helvetica
• Font size: 8-10 pt
• Line width: 0.75-1.25 pt
• Color: High-contrast palette
• Format: PDF/EPS (vector)

Cell Style

• Font: Arial
• Font size: 8-12 pt
• Line width: 1.0-1.5 pt
• Color: Vibrant palette
• Format: PDF/EPS (vector)

---

🔧 Dependencies

Required:

• Python 3.7+
• NumPy
• Matplotlib
• Seaborn

Optional (for advanced features):

• PyMOL (structure visualization)
• scikit-learn (PCA/t-SNE)
• umap-learn (UMAP)
• MDAnalysis (trajectory processing)

Auto-install:

pip install numpy matplotlib seaborn scikit-learn umap-learn MDAnalysis

---

📚 Integration with AutoMD-GROMACS

AutoMD-Viz is designed to work seamlessly with AutoMD-GROMACS analysis results.

After running analysis:

# Run analysis
advanced-analysis -s md.tpr -f md.xtc

# Visualize results
automd-viz --type report --input advanced-analysis/ --style nature

Supported analysis outputs:

• RMSD/RMSF/Rg (from analysis.sh)
• PCA/Clustering (from advanced-analysis.sh)
• Binding analysis (from binding-analysis.sh)
• Trajectory analysis (from trajectory-analysis.sh)
• Property analysis (from property-analysis.sh)

---

🐛 Troubleshooting

See publication-viz-errors.md for common issues and solutions.

Quick fixes:

• PyMOL not found → Install PyMOL or use --no-structure
• Font issues → Install required fonts or use --font-fallback
• Memory errors → Reduce trajectory frames with --stride

---

📖 Examples

See examples/ directory for complete workflows:

• example_protein/ - Protein structure visualization
• example_ligand/ - Protein-ligand complex
• example_membrane/ - Membrane protein system
• example_trajectory/ - Trajectory analysis

---

🤝 Contributing

Contributions welcome! Please submit issues and pull requests on GitHub.

---

📄 License

MIT License - see LICENSE file for details.

---

📧 Contact

• Author: Xuan Guo
• Email: [e-mail]
• GitHub: @Billwanttobetop