rfdiffusion
About
RFDiffusion generates novel protein backbone structures using a diffusion model for tasks like binder design and motif scaffolding. It's ideal for creating de novo backbones, specifying binding interfaces, or designing symmetric oligomers. After generation, use ProteinMPNN for sequence design and AlphaFold/Chai for validation.
Quick Install
Claude Code
Recommendednpx skills add NeverSight/skills_feed -a claude-code/plugin add https://github.com/NeverSight/skills_feedgit clone https://github.com/NeverSight/skills_feed.git ~/.claude/skills/rfdiffusionCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the rfdiffusion skill?
rfdiffusion is a Claude Skill by NeverSight. Skills package instructions and resources that Claude loads on demand, so Claude can perform rfdiffusion-related tasks without extra prompting.
How do I install rfdiffusion?
Use the install commands on this page: add rfdiffusion to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does rfdiffusion belong to?
rfdiffusion is in the design-tools category, tagged structure-design, diffusion, backbone and binder.
Is rfdiffusion free to use?
Yes. rfdiffusion is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
Boltz provides open-source biomolecular structure prediction using Boltz-1/Boltz-2 models, serving as an alternative to AlphaFold2. It specializes in predicting protein complexes, validating designed binders, and handling protein-ligand interactions. This skill is particularly useful when you need open-source structure prediction or want to leverage local GPU resources.
The alphafold skill uses AlphaFold2 to validate protein designs by predicting structures and calculating confidence metrics. It supports single-chain validation, binder-target complexes, and multi-chain predictions with AlphaFold-Multimer. For faster single-chain predictions, developers should use the esm skill instead.
BoltzGen is an all-atom diffusion model for protein design that generates both backbone and side-chain coordinates simultaneously. It is particularly suited for designing proteins around small molecules or ligands where precise binding geometries are required. Use this skill when you need side-chain-aware design from the start and are working with a YAML-based configuration.
BindCraft provides end-to-end protein binder design with joint backbone and sequence optimization and built-in AlphaFold2 validation. It's ideal for production-quality binder campaigns offering different speed protocols to balance design quality and computational cost. Use this skill when you need high experimental success rates for binder design rather than just backbone generation.
