Rfdiffusion Nim

What it does

When your agent uses it

• De novo protein backbones
• Motif scaffolding
• Hotspot residues
• Diffusion steps

Example prompts

• “/rfdiffusion-nim”

Requirements

• Python 3
• Docker
• A credential in NGC_API_KEY
• A credential in NVIDIA_API_KEY
• Compatibility (from SKILL.md): requests>=2.28
• Pre-approved tools (allowed-tools): Bash, Read, Write, AskUserQuestion

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Bash
  • Read
  • Write
  • AskUserQuestion

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • docker

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • health.api.nvidia.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • NGC_API_KEY
  • NVIDIA_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  requests>=2.28

  From compatibility in the SKILL.md frontmatter.

Context cost

Rfdiffusion Nim loads about 1.3k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 381 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~75

When it runs · the whole SKILL.md, loaded when a task matches

~1.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:48

  [ -f .env ] && . ./.env

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Bash, Read, Write, AskUserQuestion

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 381 words, ~1,344 tokens.

name

rfdiffusion-nim

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.

allowed-tools

Bash, Read, Write, AskUserQuestion

compatibility

requests>=2.28

license

Apache-2.0 AND CC-BY-4.0

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}"

docker run -it \
  --runtime=nvidia \
  --gpus "device=0" \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/ipd/rfdiffusion:2

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

DUMMY_PDB = (
    "CRYST1    1.000    1.000    1.000  90.00  90.00  90.00 P 1           1\n"
    "ATOM      1  CA  ALA A   1       0.000   0.000   0.000  1.00  0.00           C\n"
    "END\n"
)

import os
from pathlib import Path
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/ipd/rfdiffusion/generate"
    if HOSTED else "http://localhost:8000/biology/ipd/rfdiffusion/generate"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

payload = {
    "input_pdb": DUMMY_PDB,
    "contigs": "80-120",
    "diffusion_steps": 50,
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()
Path("designed_backbone.pdb").write_text(result["output_pdb"])

payload = {
    "input_pdb": Path("target.pdb").read_text(),
    "contigs": "A25-35/0 50-80",
    "diffusion_steps": 50,
}

payload = {
    "input_pdb": Path("target.pdb").read_text(),
    "contigs": "A1-100/0 50-100",
    "hotspot_res": ["A50", "A51", "A52", "A53", "A54"],
    "diffusion_steps": 50,
}

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (references) in skills/bionemo-rfdiffusion-nim of NVIDIA/skills.

• SKILL.md
• BENCHMARK.md
• config/skillspector-baseline.yml
• evals/config.yml
• evals/evals.json
• evals/trigger_evals.json
• references/api.md
• references/examples.md
• references/parameters.md
• references/science.md
• references/validation.md
• skill-card.md
• skill.oms.sig

Open the folder on GitHubat commit dfdd080

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.

• NVIDIA-BioNeMo/bionemo-agent-toolkitnim-skills/rfdiffusion-nimskills/bionemo-agent-toolkit/skills/rfdiffusion-nim