design-comparison

Compare, rank, and batch-visual QC protein design candidates in PyMOL.

3|Updated Jan 28, 2026
One-click install
npx skills add https://github.com/ANaka/claudemol --skill design-comparison
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: design-comparison
Source: https://github.com/ANaka/claudemol/tree/main/claude-plugin/skills/design-comparison
Command: npx skills add https://github.com/ANaka/claudemol --skill design-comparison

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Compare, rank, and batch-visual QC multiple protein design candidates in PyMOL.

Core Features & Use Cases

  • Batch loading and side-by-side visualization of multiple designs
  • Ranking designs by metrics (mean pLDDT, RMSD, length)
  • Iteration tracking across design rounds and overlays with a template or AF2 predictions
  • Design vs. template/AF2 overlays and quality checks

Quick Start

Load your designs into PyMOL and run the built-in ranking workflow to see the top candidates.

Frequently Asked Questions about design-comparison

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I rank and compare multiple protein designs in PyMOL?▼

You can batch load multiple protein design PDBs into PyMOL and run a ranking workflow to compare candidates by mean pLDDT, RMSD, and length, exporting a ranked list of top designs.

Can I overlay AF2 predictions with a template for visual QC of protein designs?▼

Yes, you can overlay template structures or AlphaFold predictions with your design candidates in PyMOL to perform side-by-side visual quality control and check structural alignment.

What is the best way to evaluate RFdiffusion or ProteinMPNN design iterations?▼

Evaluating RFdiffusion and ProteinMPNN design iterations is done by tracking design rounds, overlaying candidates with templates, and computing pLDDT-based scores and RMSD to rank quality.

Does this protein design ranking workflow require external dependencies?▼

No external dependencies are required. The workflow operates entirely within PyMOL to load PDBs, compute structural metrics like RMSD and pLDDT, and export the ranked design results.

How do I calculate RMSD and pLDDT scores for batch-loaded protein structures?▼

Loading multiple protein structures into PyMOL allows the workflow to automatically compute RMSD against templates and calculate mean pLDDT scores to generate a ranked comparison of all candidates.

Why use PyMOL for batch visual QC of protein design candidates instead of other tools?▼

Using PyMOL for batch visual QC enables direct side-by-side visualization, template overlays, and integrated metric calculation like RMSD and pLDDT, streamlining the ranking of multiple design variants.