NVIDIA-BioNeMo

Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF optimization, TFD, conformer RMSD, Butina clustering, and…

Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities,…

Run a complete protein binder design campaign with NVIDIA Proteina-Complexa: resolve a target structure and hotspots from a name/sequence/PDB, co-design binder sequence+structure with reward-guided test-time search (best-of-n, beam search, FK steering, MCTS),…

Define custom groups (Irrep subclasses), build segmented tensor products with CG coefficients, create equivariant polynomials and IrDictPolynomials, and use built-in descriptors (linear, tensor products, spherical harmonics). Use when working with…

Use when accelerating existing genomics workflows with NVIDIA Parabricks, improving runtime or price/performance, converting pipeline steps to GPUs, or comparing CPU and GPU workflow outputs. Adds optional GPU steps in-place with runtime toggles (default…

Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF optimization, TFD, conformer RMSD, Butina clustering, and…

Route NVIDIA Parabricks pbrun tools, assess GPU/runtime readiness, and provide version-aware command guidance for FASTQ/BAM processing, RNA-seq, variant calling, BAM QC, and GVCF workflows. Do NOT use for inspecting or accelerating whole pipelines — use…

Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Use for binder design, minibinder design, de novo binders, RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines,…

Use when a phage-design workflow must discover or adapt to local GPU, SSH, Slurm, Lepton, manual, or unfamiliar execution infrastructure and produce durable launch, monitoring, resume, or handoff commands.

Use after selecting an Evo 2 phage SFT checkpoint and defining RL objectives to calibrate prompt serialization, temperature, prefix-length distribution, and fixed validation sampling.

Use when an Evo 2 phage SFT project needs a reproducible Microviridae or new phage genome collection from NCBI, paper-linked repositories, supplements, or other public biological databases.

Use when producing, deduplicating, hard-QC screening, clustering, ranking, or selecting final phage designs from a chosen RL checkpoint.

Use when adding or changing Evo2 phage RL metrics, reward functions, filter logic, or validation criteria after an objective plan has been approved.

Use when launching, monitoring, stopping, resuming, or relaunching Evo 2 phage SFT with Megatron Bridge, or when selecting its best validation-loss checkpoint across local, SSH, scheduler, or cloud execution.

Use when launching, monitoring, resuming, relaunching, or selecting checkpoints from a NeMo-RL Evo2 phage optimization run.

Use when converting a phage-design goal into target-specific RL rewards, validation criteria, and final QC filters, especially for a new reference phage or altered objective.

Benchmark a CP inference or training workflow to find the maximum token count that fits at a given CP size and to record end-to-end walltime. Sweeps input size (tokens N, atoms, MSA depth S) over real or synthesized data, ramps N until OOM to report the max,…

Probe and establish the distributed test infrastructure for CP development. Inventories local GPUs (count, model, memory, NVLink topology), checks the software stack (Python, PyTorch+CUDA, NCCL, torch.distributed), and runs shipped smoke tests for…

Orchestrate the END-TO-END integration of context parallelism into a custom co-folding / structure-prediction model: turn the whole effort into a prioritized, dependency-sorted worklist, then drive it phase by phase — map the model (learn_context), verify…

Orchestrate a team of agents to integrate many CP modules and tests in parallel. Builds the work-list from the module map, derives a data-flow dependency graph, topologically sorts it into GPU-slot-bounded waves, and spawns a coder+reviewer pair per work…

Stand up the distributed-model lifecycle for a CP training/inference run: device placement before DTensor wrapping, the all-trainable-params-are-DTensors invariant (with placeholder/freeze for unimplemented modules), checkpoint save (DTensor → plain) and load…

Implement DTensor-based context-parallel model modules that mirror a serial reference. Maps each serial layer/module to its Boltz-CP counterpart with exact input/output shapes, placements, collectives, and backward memory budget; writes the CP implementation…

Explore a custom co-folding / structure-prediction model to understand its inference and training workflows, entry points, training framework (PyTorch Lightning vs DeepSpeed), data-feature format, and featurization pipeline — then map those components onto…

Memory-profile a context-parallel (CP) inference (or training) workflow with the PyTorch CUDA caching-allocator history, then attribute the top-N memory peaks to specific modules and lines of code. Wraps the end-to-end forward in…

End-to-end Proteina-Complexa design pipeline driver. Reach for this skill whenever the user wants to "design a binder", "design binders for X", "run complexa design", "de novo binder", "PDL1 binder", "TrkA binder", "design proteins for target", "protein…

Standalone evaluation of an existing PDB directory with Proteina-Complexa. Use this skill whenever the user wants to "evaluate PDB files", "re-fold these designs", "compute interface pAE", "compute i_pLDDT for a folder", "run AF2 / RF3 / ESMFold on my…

First-time setup, environment configuration, and model-weight installation for Proteina-Complexa. Reach for this skill whenever the user says "set up complexa", "install complexa", "configure my .env", "first-time setup", "what models do I have installed",…

Use this skill whenever the user wants to run a parameter sweep over a Proteina-Complexa design pipeline — cartesian-product hyperparameter scans, Pareto search over generation/reward/evaluation knobs, or any "compare configurations" workflow. Trigger phrases…

Use this skill whenever the user wants to add, register, edit, list, show, or validate a Proteina-Complexa design target for any pipeline — protein binder (default), ligand binder, or AME / enzyme scaffolding. Triggers include "add a target", "define a new…

Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF optimization, TFD, conformer RMSD, Butina clustering,…

Build and run GPU-accelerated Quantitative Systems Pharmacology (QSP) and PBPK population ODE simulations with the nvQSP library, and differentiate them. nvQSP provides a sparse polynomial RODAS4 stiff solver (prebuilt) and a model-specialized dense adaptive…

Replace attention in a PyTorch model with HyenaND from the nvSubquadratic library. Covers 1D / 2D / 3D hosts (ViT, U-Net, diffusion, causal LM, hierarchical encoders). Trigger when the user wants a subquadratic alternative to attention, ports a model to…