Agent skill · nvidia
rfd4-proteina-nim
Run RFD4-Proteina NIM for joint protein structure and sequence design, conditioned generation, and reward-guided search using the NVIDIA hosted API or a self-hosted NIM. Not for the older RFdiffusion contigs API.
What it needs
About 5k tokens when loaded.
What this skill does
RFD4-Proteina NIM Generate protein structures and sequences through the released RFD4-Proteina NIM. This skill covers conditioned protein and small-molecule binders, motif and enzyme scaffolding, antibody/CDR design, partial diffusion, inverse folding, and reward-guided beam search. Use science for interpretation. Prerequisites Use Python 3.10 or newer; the client uses only the standard library. Hosted inference requires a key obtained from the product page, set as NVIDIAAPIKEY (NGCAPIKEY is accepted as a fallback). Supply the key through the user's secret mechanism, not a prompt or command-line argument. Offline preparation and saved-response extraction need no credentials. For an existing local NIM, use its base URL. Starting a new container also requires Docker, NVIDIA Container Toolkit, a supported GPU/driver, and NGC access; read deployment only when deployment is needed. Instructions 1. Choose the requested destination. Use hosted for NVIDIA's API or local for a user-operated NIM. Do not switch destinations after an error. Preserve available diagnostics, report the failed or pending status, and follow Troubleshooting below; stop when the request's state is unresolved. If the user requests preparation only, use --dry-run without probing a service or starting a container. For instructions only, show commands without executing them. 2. Prepare the complete request. Read the user's structures and conditions. Use inline annotated mmCIF or a parsed conditionspec with all referenced mmCIF attachments. Preserve targets, residue selections, conditions, seeds, and replicate counts. If an input is missing, request it rather than inventing a structure or selection. 3. Run the helper. Use scripts/design.py with --request and a new --output-dir. Read examples for the public PD-1 binder payload and parameters before modifying schedules, batching, partial diffusion, or search. 4. Inspect the actual result. Read transport.json and summary.json. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-rfd4-proteina-nim