bionemo-agent-toolkit

In the author's own words, from each skill's SKILL.md — showing the first 30 of 62 files.

• cuequivariance

  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 cuequivariance group theory, irreps, or segmented polynomials.

  library-skills/cuequivariance/SKILL.md on GitHub (opens in a new tab)

• genomics-workflow-acceleration

  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 off). Do NOT use for individual pbrun command routing — use parabricks.

  library-skills/genomics-workflow-acceleration/SKILL.md on GitHub (opens in a new tab)

• nvmolkit-usage

  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 substructure search. Use when the user is importing `nvmolkit.*`, debugging an `nvmolkit` call, choosing between nvMolKit and RDKit for a batched…

  library-skills/nvmolkit-usage/SKILL.md on GitHub (opens in a new tab)

• parabricks

  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 genomics-workflow-acceleration.

  library-skills/parabricks/SKILL.md on GitHub (opens in a new tab)

• boltz2-nim

  Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.

  nim-skills/boltz2-nim/SKILL.md on GitHub (opens in a new tab)

• diffdock-nim

  Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Use for DiffDock, molecular docking, ligand docking, blind docking, SMILES or SDF ligands, ranked poses, confidence scores, hosted NVIDIA API, or local Docker deployment.

  nim-skills/diffdock-nim/SKILL.md on GitHub (opens in a new tab)

• evo2-nim

  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, and BioNeMo NIM workflows.

  nim-skills/evo2-nim/SKILL.md on GitHub (opens in a new tab)

• genmol-nim

  Generate novel drug-like molecules using the GenMol NIM microservice. Use for de novo generation, scaffold decoration, motif extension, lead optimization, SAFE notation, QED or LogP ranking, hosted NVIDIA API calls, or local Docker deployment. GenMol takes SAFE notation in the smiles field, not ordinary SMILES.

  nim-skills/genmol-nim/SKILL.md on GitHub (opens in a new tab)

• molmim-nim

  Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.

  nim-skills/molmim-nim/SKILL.md on GitHub (opens in a new tab)

• msa-search-nim

  Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (…

  nim-skills/msa-search-nim/SKILL.md on GitHub (opens in a new tab)

• openfold2-nim

  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.

  nim-skills/openfold2-nim/SKILL.md on GitHub (opens in a new tab)

• openfold3-nim

  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.

  nim-skills/openfold3-nim/SKILL.md on GitHub (opens in a new tab)

• proteinmpnn-nim

  Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Use for ProteinMPNN, inverse folding, sequence design, backbone redesign, fixed chains/residues, omit_AAs, sampling temperature, soluble model, hosted NVIDIA API, local Docker, PDB input, and multi-FASTA output.

  nim-skills/proteinmpnn-nim/SKILL.md on GitHub (opens in a new tab)

• rfdiffusion-nim

  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.

  nim-skills/rfdiffusion-nim/SKILL.md on GitHub (opens in a new tab)

• drug-discovery-pipeline

  NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. U…

  nim-skills/meta-skills/drug-discovery-pipeline/SKILL.md on GitHub (opens in a new tab)

• msa-structure-prediction-pipeline

  NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using th…

  nim-skills/meta-skills/msa-structure-prediction-pipeline/SKILL.md on GitHub (opens in a new tab)

• kermt-add-cmim-pretrain

  Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Effectively kermt-continue-pretrain with a one-time ckpt-conversion step prepended.

  open-models-skills/kermt/kermt-add-cmim-pretrain/SKILL.md on GitHub (opens in a new tab)

• kermt-continue-pretrain

  Continue pretraining from an existing KERMT checkpoint. The skill validates the user's checkpoint and pretrain CSV, prepares the data into shard/vocab/features form, then launches pretrain_ddp.py inside the kermt container (detached for long runs). Auto-dispatches `--pretrain_mode` based on the checkpoint type (grover_base vocab-only, cmim, or hybrid).

  open-models-skills/kermt/kermt-continue-pretrain/SKILL.md on GitHub (opens in a new tab)

• kermt-embed

  Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint (grover_base / cmim / hybrid / finetuned). Writes one .npy per readout type (atom_from_atom, bond_from_atom, atom_from_bond, bond_from_bond) plus canonical_smiles.npy and validity.npy. Calls task/extract_embeddings.py (which featurizes SMILES on the fly — no pre-computed features needed).

  open-models-skills/kermt/kermt-embed/SKILL.md on GitHub (opens in a new tab)

• kermt-finetune

  Finetune a pretrained KERMT encoder on a labeled CSV. The skill validates the input checkpoint (must be a pretrain ckpt — grover_base / cmim / hybrid), validates the labeled CSV, prepares the data (clean + features + optional split), then launches main.py finetune inside the kermt container (detached for hours-scale runs). Hyperparameters come from agent/config/defaults_finetune.json with per-fla…

  open-models-skills/kermt/kermt-finetune/SKILL.md on GitHub (opens in a new tab)

• kermt-infer

  Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).

  open-models-skills/kermt/kermt-infer/SKILL.md on GitHub (opens in a new tab)

• kermt-monitor

  Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).

  open-models-skills/kermt/kermt-monitor/SKILL.md on GitHub (opens in a new tab)

• kermt-pretrain-scratch

  Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.

  open-models-skills/kermt/kermt-pretrain-scratch/SKILL.md on GitHub (opens in a new tab)

• kermt-setup

  Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on this; invoke it first.

  open-models-skills/kermt/kermt-setup/SKILL.md on GitHub (opens in a new tab)

• complexa-design

  End-to-end Proteina-Complexa design pipeline driver. Use when 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 binder design", "ligand binder", "design a small-molecule binder", "ATP-binding protein", "AME motif scaffolding", "scaffold a motif near a ligand", "motif + ligand d…

  open-models-skills/proteina-complexa/complexa-design/SKILL.md on GitHub (opens in a new tab)

• complexa-evaluate-pdbs

  Standalone evaluation of an existing PDB directory with Proteina-Complexa. Use when 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 designs", "score binder candidates", "designability of this folder", "scRMSD for designs", "motif RMSD for these PDBs", "complexa analysis", "complexa evaluate fr…

  open-models-skills/proteina-complexa/complexa-evaluate-pdbs/SKILL.md on GitHub (opens in a new tab)

• complexa-setup

  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 environment file", "first-time setup", "what models do I have installed", "what's in my environment file", "download model weights", "download Complexa / AF2 / RF3 / ProteinMPNN / LigandMPNN / ESM2 / ESMF…

  open-models-skills/proteina-complexa/complexa-setup/SKILL.md on GitHub (opens in a new tab)

• complexa-sweep

  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 include "sweep beam width", "sweep nsteps", "hyperparameter sweep", "parameter scan", "scan beam_width and temperature", "compare configura…

  open-models-skills/proteina-complexa/complexa-sweep/SKILL.md on GitHub (opens in a new tab)

• complexa-target

  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 target for binder design", "register a hotspot", "set up a PDL1 binder target", "ligand binder pocket", "SMILES target", "AME task", "enzyme m…

  open-models-skills/proteina-complexa/complexa-target/SKILL.md on GitHub (opens in a new tab)

• complexa-binder-design

  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), select with the internal AF2 reward gate, then INDEPENDENTLY validate each binder by refolding the complex with Boltz2 (default) or OpenFol…

  workflows/generative-protein-binder-design/complexa-binder-design/SKILL.md on GitHub (opens in a new tab)

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