Download skills/bionemo-openfold2-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 openfold2-nim live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use openfold2-nim?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>
name: openfold2-nim
description: >
Use this skill for OpenFold2, NVIDIA's BioNeMo NIM microservice for monomer protein structure prediction. Invoke whenever the user mentions OpenFold2, AlphaFold2-like monomer folding, protein sequence-to-structure prediction, A3M MSAs, mmCIF templates, hosted NVIDIA API calls, or local Docker deployment.
license: Apache-2.0 AND CC-BY-4.0
compatibility: "requests>=2.28"
allowed-tools: Bash, Read, Write, AskUserQuestion
OpenFold2 NIM
Predict a single protein-chain structure from an amino-acid sequence, with
optional A3M multiple sequence alignments and mmCIF templates. Use this
guide for basic hosted/local NIM use; load supplemental files only when
the task needs deeper context:
Local URL: http://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-template
Local readiness: http://localhost:8000/v1/health/ready
Mode difference: hosted and local use the same prediction path except local
does not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY; local inference requests use no auth header after readiness.
Auth And Environment
Do not print API keys. Confirm they exist with shell tests, not echoes.
Hosted needs NGC_API_KEY in the request header. Supported local Docker
startup uses NGC_API_KEY, or NVIDIA_API_KEY as a fallback, plus
LOCAL_NIM_CACHE. A repo-root .env file may be sourced as a local override.
Local Docker
Use the official OpenFold2 NIM image and mount LOCAL_NIM_CACHE at
/opt/nim/.cache. Current docs recommend at least 80 GB disk, 64 GB system
RAM, 8 CPU cores, and one supported GPU; the container is roughly 55 GB and
first startup downloads about 10 GB of model parameters.
For the exact startup preflight (.env sourcing, NGC_API_KEY/NVIDIA_API_KEY
handling, docker login, and the docker run for
nvcr.io/nim/openfold/openfold2:latest), copy the command block in
references/api.md under Local Docker verbatim — do
not drop .env, NGC_API_KEY, LOCAL_NIM_CACHE, or the no-auth local request.
Readiness check:
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done
Request Pattern
Use Python requests; curl escaping is fragile for A3M/mmCIF text. The
sequence field is required. input_id, alignments, selected_models,
relax_prediction, use_templates, and explicit_templates are optional.
OpenFold2 is monomer-only. For protein-ligand, protein-DNA/RNA, or
multi-chain complexes, use OpenFold3 or Boltz2 instead.
sequence must use valid amino-acid IUPAC symbols.
Hosted API docs list sequence length 1-1000; local docs say current NIM
supports sequences up to 2048 residues on supported hardware.
A3M alignments go under alignments by database name, then a3m with
alignment and format. When the user needs to create or deepen an MSA,
hand off to msa-search-nim / MSA Search and map its A3M output into this
alignments shape.
Starting with OpenFold2 2.0.0, use explicit_templates with mmCIF content;
do not write new HHR-template examples.
selected_models chooses AlphaFold2/OpenFold parameter sets 1-5. Select one
or two models for smoke tests; use all five for stronger production runs.
Save And Interpret Output
The response includes one prediction per selected model, ordered by confidence.
Save every returned structure-like text field and the full JSON response so
field-shape differences are auditable. Production answers should explicitly
write .pdb or .cif artifacts, preserve the response JSON, and print any
confidence/ranking fields the service returns.
from pathlib import Path
import json
Path("openfold2_response.json").write_text(json.dumps(result, indent=2))
def save_strings(obj, prefix="openfold2"):
i = 0
if isinstance(obj, dict):
for key, value in obj.items():
if isinstance(value, str) and ("ATOM" in value or value.lstrip().startswith("data_")):
i += 1
ext = "cif" if value.lstrip().startswith("data_") else "pdb"
Path(f"{prefix}_{key}_{i}.{ext}").write_text(value)
elif isinstance(value, (dict, list)):
i += save_strings(value, f"{prefix}_{key}")
elif isinstance(obj, list):
for idx, value in enumerate(obj, start=1):
if isinstance(value, (dict, list)):
i += save_strings(value, f"{prefix}_{idx}")
return i
saved = save_strings(result)
print(f"saved {saved} structure artifact(s)")
For production monomer runs:
Use selected_models: [1, 2, 3, 4, 5] unless the user requests a smoke test.
Use relax_prediction: True in Python payloads when relaxation is desired;
JSON examples may show true.
State the sequence length caveat: hosted API docs list 1-1000 residues, while
local support-matrix docs list up to 2048 residues on supported hardware.
If the task is a complex rather than a monomer, redirect to OpenFold3 or
Boltz2.
Treat tiny toy sequences and single-sequence MSAs as API smoke tests, not
quality evidence. For scientific interpretation and validation, read
references/science.md and references/validation.md.
Troubleshooting
401: missing, expired, or unauthorized NGC API key.
422: invalid amino-acid characters, sequence too long, malformed A3M, bad
selected_models, or malformed mmCIF template object.
Local 404: remove /v1/ from the prediction URL.
Weak structures: use MSA Search to generate deeper A3M alignments and add
biologically relevant mmCIF templates when appropriate.
Local startup stalls: first run downloads parameters into LOCAL_NIM_CACHE.
How do I install openfold2-nim?
Download skills/bionemo-openfold2-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 openfold2-nim live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use openfold2-nim?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.