rfdiffusion
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation.
Skill by BioTender-max
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/biotender-max-rfdiffusion
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/rfdiffusion
What's inside
- Skill name
rfdiffusion- Description
- Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
- License
- MIT
- Instructions
- 286 lines in SKILL.md · view on GitHub
Before installing
- Instructions call the network
Files (4)
- SKILL.md
- binder-walkthrough.md · examples/
- hotspot-selection.md · references/
- parameters.md · references/
SKILL.md (first lines)
# RFdiffusion Backbone Generation ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.9+ | 3.10 | | CUDA | 11.7+ | 12.0+ | | GPU VRAM | 16GB | 24GB (A10G) | | RAM | 16GB | 32GB | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal (recommended) ```bash # Clone biomodals git clone https://github.com/hgbrian/biomodals && cd biomodals # Basic binder design modal run modal_rfdiffusion.py \ --pdb target.pdb \ --contigs "A1-150/0 70-100" \ --hotspot "A45,A67,A89" \ --num-designs 100 # With custom GPU/timeout GPU=A100 TIMEOUT=60 modal run modal_rfdiffusion.py \ --pdb target.pdb \ --contigs "A1-150/0 70-100" \ --num-designs 100 ``` **GPU**: A10G (24GB) | **Timeout**: 30min default ### Option 2: Local installation ```bash # Clone and install git clone https://github.com/RosettaCommons/RFdiffusion.git cd RFdiffusion && pip install -e . # Download weights wget http://files.ipd.uw.edu/pub/RFdiffusion/models/Complex_base_ckpt.pt # Run inference python run_inference.py \ inference.input_pdb=target.pdb \ contigmap.contigs=[A1-150/0 70-100] \ ppi.hotspot_res=[A45,A67,A89] \ inference.num_designs=100 ``` ## Config Schema (Hydra) ### Contigmap Syntax ```bash # De novo single chain (50-100 residues) contigmap.contigs=[50-100] # Binder + target (A = target chain, fixed with /0) contigmap.contigs=[A1-150/0 70-100] # Motif scaffolding (preserve residues, /0 = fixed) contigmap.contigs=[20-40/0 A10-30/0 20-40] # Multi-chain binder contigmap.contigs=[A1-100/0 B1-100/0 60-80] # Variable length ranges contigmap.contigs=[A1-150/0 50-100] # Binder 50-100 AA ``` ### Hotspot Specification ```bash # Residues for interface (chain + resnum, no spaces) ppi.hotspot_res=[A45,A67,A89] ``` ## Common mistakes ### Contig Syntax ✅ **Correct**: ```bash contigmap.contigs=[A1-150/0 70-100] # Target fixed (/0), binder variable ``` ❌ **Wrong**: ```bash contigmap.contigs=[A1-150 70-100] # Missing /0 - target will move! contigmap.contigs="A1-150/0 70-100" # Quotes break parsing contigmap.contigs=[A1-150/0, 70-100] # Comma breaks parsing ``` ### Hotspot Residues ✅ **Correct**: ```bash ppi.hotspot_res=[A45,A67,A89] # Chain letter + residue number ``` ❌ **Wrong**: ```bash ppi.hotspot_res=[45,67,89] # Missing chain letter ppi.hotspot_res=[A45, A67, A89] # Spaces break parsing ppi.hotspot_res="A45,A67,A89" # Quotes break parsing ``` ### Complete Parameter Reference #### Core Parameters | Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | `inference.num_designs` | 10 | 1-10000 | Number of designs to generate | | `inference.input_pdb` | - | path | Target structure file | | `inference.output_prefix` | output | string | Output filename prefix | | `diffuser.T` | 50 | 20-200 | Diffusion timesteps | | `denoiser.noise_scale_ca` | 1.0 | 0.0-2.0 | CA atom noise (0.5-0.8 = conservative) | | `denoiser.noise_scale_frame` | 1.0 | 0.0-2.0 | Frame noise | | `inference.ckpt_override_path` | - | path | Model checkpoint |
From BioTender-max/awesome-bio-agent-skills, MIT.
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Signals
- Stars
- 206
- Forks
- 36
- Open issues
- 1
- Last commit (this folder)
- May 25, 2026 (5 months ago)
- Last release
- Jul 1, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
rfdiffusion skill. Agent Mart. https://agentmart.dev/skills/biotender-max-rfdiffusion (last updated 2026-10-24)