# Rfdiffusion Nim

Run RFDiffusion protein backbone design via NVIDIA NIM.

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Design
- License: Apache-2.0 AND CC-BY-4.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NVIDIA/skills --skill rfdiffusion-nim -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill rfdiffusion-nim -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill rfdiffusion-nim -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill rfdiffusion-nim -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

Run RFDiffusion protein backbone design via NVIDIA NIM. Use for de novo protein backbones, motif scaffolding, binder design, hotspot residues, contigs syntax, diffusion steps, hosted NVIDIA API calls, local Docker deployment, and PDB backbone outputs for ProteinMPNN sequence design.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# RFDiffusion NIM

Design protein backbone PDBs for de novo proteins, motif scaffolds, and binders.
Use this guide for first-pass hosted/local usage; load supplemental files
only when needed:

- `references/api.md`: exact endpoints, schemas, Docker flags, response fields.
- `references/science.md`: design modes, strengths, limits, and handoffs.
- `references/parameters.md`: contigs, hotspots, steps, and seeds.
- `references/validation.md`: PDB, contig, and artifact sanity checks.
- `references/examples.md`: compact hosted/local request patterns.

## Choose Mode

Ask only when context is unclear:

> Hosted NVIDIA API or local Docker NIM?

- Hosted: `https://health.api.nvidia.com/v1/biology/ipd/rfdiffusion/generate`
- Local: `http://localhost:8000/biology/ipd/rfdiffusion/generate`

Local inference paths do not include `/v1/`. Hosted requests use `Authorization: Bearer $NGC_API_KEY`. Supported local Docker
startup uses `NGC_API_KEY` (or `NVIDIA_API_KEY` via the preflight) for
registry login, entitlement checks, and first-run model downloads; pass it
into the container with `-e NGC_API_KEY`. Local inference requests use no
auth header after readiness. Warm-cache key-free startup varies by
image/version and should not be assumed.

## Local Docker

For local setup answers, copy the preflight below exactly before `docker login`,
`docker run`, readiness, and the no-auth local request. Do not replace it with a
simple `: "${NGC_API_KEY:?Set NGC_API_KEY}"` check, do not invent a cache
default, and do not drop the `NVIDIA_API_KEY` fallback. Default setup is single
GPU `device=0`.

```bash
set -a
[ -f .env ] && . ./.env
set +a

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

mkdir -p "${LOCAL_NIM_CACHE}"
chmod 755 "${LOCAL_NIM_CACHE}"

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-rfdiffusion-nim/SKILL.md