Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/adaptyvbio/protein-design-skillsnpx agentmods add skills/adaptyvbio/protein-design-skills/rfdiffusionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adaptyvbio/protein-design-skills/rfdiffusion)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/rfdiffusion"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/rfdiffusion/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/rfdiffusion"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/rfdiffusion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.02681 |
| Opus 5 | $0.00060 | $0.01340 |
| Sonnet 5 | $0.00024 | $0.00536 |
| Haiku 4.5 | $0.00012 | $0.00268 |
Grade A, and why
rfdiffusion scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget -P models http://files.ipd.uw.edu/pub/RFdiffusion/e29311f6f1bf1af907f9ef9f44b8328b/Complex_base_ckpt.pt How it starts
The opening of the file, as written. The whole thing — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
RFdiffusion is not in biomodals, so run it from the official RosettaCommons repo or its Docker image, not through Modal.
Local installation (official repo)
git clone https://github.com/RosettaCommons/RFdiffusion.git
cd RFdiffusion
# Conda env including the required NVIDIA SE(3)-Transformer
conda env create -f env/SE3nv.yml
conda activate SE3nv
cd env/SE3Transformer && pip install . && cd ../..
pip install -e .
# Download weights (per-file hashed paths; see the repo README for the full list)
mkdir -p models
wget -P models http://files.ipd.uw.edu/pub/RFdiffusion/e29311f6f1bf1af907f9ef9f44b8328b/Complex_base_ckpt.pt
# Binder design run; single-quote the hydra args so the shell does not split [] or ,
./scripts/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
A RosettaCommons-maintained Docker image is also available from the repo README.
After backbone generation, design sequences with proteinmpnn.
Config Schema (Hydra)
Contigmap Syntax
# 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
# Residues for interface (chain + resnum, no spaces)
ppi.hotspot_res=[A45,A67,A89]
Common mistakes
Contig Syntax
✅ Correct:
'contigmap.contigs=[A1-150/0 70-100]' # Target fixed (/0), binder variable
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 301 lines · 119 tokens per session scan A debadd4943eb
rfdiffusion is a skill published in the GitHub repository adaptyvbio/protein-design-skills (159 stars, last pushed 3mo ago), licensed MIT. It adds 119 tokens to every session and 2,681 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
rfdiffusion
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…
rfdiffusion
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…
protein-structure-design-boltzgen
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For structure…
boltzgen
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only…
boltzgen
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only…
boltzgen
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For sequence-only…