Download skills/bionemo-openfold3-nim/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does openfold3-nim live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use openfold3-nim?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>
name: openfold3-nim
description: >
Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. Invoke whenever the user mentions OpenFold3 or needs protein, protein-ligand, protein-DNA/RNA, or multi-chain complex prediction with the hosted NVIDIA API or local Docker NIM. Covers endpoint choice, auth, request payloads, output artifacts, confidence scores, and local container setup.
license: Apache-2.0 AND CC-BY-4.0
compatibility: "requests>=2.28"
allowed-tools: Bash, Read, Write, AskUserQuestion
OpenFold3 NIM
Predict biomolecular structures with OpenFold3. It supports proteins, DNA, RNA,
small-molecule ligands, and multi-entity assemblies. Use this guide for
basic hosted and local NIM use; load supplemental files only when the task needs
deeper context:
Local URL: http://localhost:8000/biology/openfold/openfold3/predict
Local readiness: http://localhost:8000/v1/health/ready
Mode difference: the local prediction path has no /v1/ prefix. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker
startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for
registry login, entitlement checks, and first-run model downloads; pass it
into the container with -e NGC_API_KEY. Local inference requests use no
auth header after readiness, so bind the host port to loopback with
-p 127.0.0.1:8000:8000. Warm-cache key-free startup varies by image version
and should not be assumed.
Auth And Environment
Use credentials already supplied in the environment or injected by a secret
manager. Do not load credential files, print keys, or enable shell tracing.
Confirm keys exist with shell tests.
Hosted needs NGC_API_KEY in the request header. Local startup needs
NGC_API_KEY, or NVIDIA_API_KEY as a fallback, plus LOCAL_NIM_CACHE.
Local Docker
Use the official OpenFold3 NIM image and mount LOCAL_NIM_CACHE at
/opt/nim/.cache. Before executing setup, explain that registry authentication
sends the key to the NVIDIA registry at https://nvcr.io and first startup
downloads about 10β15 GB of model weights into the cache. Run deployment only
when requested; for a setup guide, provide the commands without running them.
When writing local setup commands, copy the preflight below exactly. Do not
replace it with a simple : "${NGC_API_KEY:?Set NGC_API_KEY}" check, do not
drop NVIDIA_API_KEY, and do not invent a default LOCAL_NIM_CACHE; those
lines are the repo's local NIM env contract. The default single-GPU launch
should show the literal --gpus "device=0"; choose a different device only
when the user asks.
Top level is {"inputs": [...]} and OpenFold3 accepts exactly one input.
molecules can contain 1-32 objects with type: protein, dna, rna,
or ligand.
Protein/RNA MSAs are optional but, when supplied, alignment must start with
a FASTA header such as >query\nSEQUENCE.
Ligands use either smiles or ccd_codes, for example
{"type": "ligand", "id": "L", "ccd_codes": "ATP"}.
DNA/RNA entities use sequence, for example
{"type": "dna", "id": "B", "sequence": "ATCGATCG"}.
diffusion_samples is 1-5. output_format is pdb or cif.
Save And Interpret Output
Save every returned structure as a scientific artifact. Main response path:
result["outputs"][0]["structures_with_scores"].
output = result["outputs"][0]
for i, sample in enumerate(output["structures_with_scores"], start=1):
fmt = sample["format"]
with open(f"openfold3_structure_{i}.{fmt}", "w", encoding="utf-8") as fh:
fh.write(sample["structure"])
print("confidence_score", sample.get("confidence_score"))
print("complex_plddt_score", sample.get("complex_plddt_score"))
print("ptm_score", sample.get("ptm_score"))
print("iptm_score", sample.get("iptm_score"))
print("complex_pde_score", sample.get("complex_pde_score"))
Higher confidence_score, complex_plddt_score, ptm_score, and iptm_score
are generally better; lower complex_pde_score is generally better. Treat toy
or very short sequences as API smoke tests, not meaningful structural biology.
For why and when OpenFold3 is scientifically appropriate, read
references/science.md.
Common Limits
Inputs per request: 1.
Molecules per input: 1-32.
Diffusion samples: 1-5.
TensorRT path supports shorter sequences; PyTorch path can support longer
sequences, but long inputs need much more GPU memory.
Sequences over roughly 1800 residues require at least 80 GB GPU memory.
Local NIM is single-GPU only; choose the target device in the Docker flag.
Troubleshooting
401: missing, expired, or unauthorized NGC API key.
422: invalid molecule type, invalid sequence characters, bad MSA shape, or
diffusion_samples outside 1-5.
MSA errors: ensure the alignment starts with >query\n.
Local 404: remove /v1/ from the prediction URL.
Local startup stalls: first run may be downloading 10-15 GB of model weights
into LOCAL_NIM_CACHE.
Memory errors: shorten the sequence, reduce samples, or use a larger GPU.
How do I install openfold3-nim?
Download skills/bionemo-openfold3-nim/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does openfold3-nim live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use openfold3-nim?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.