skills

In the author's own words, from each skill's SKILL.md — showing the first 30 of 383 files.

• accelerated-computing-cudf

  Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

  skills/accelerated-computing-cudf/SKILL.md on GitHub (opens in a new tab)

• aiq-deploy

  Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.

  skills/aiq-deploy/SKILL.md on GitHub (opens in a new tab)

• aiq-research

  Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.

  skills/aiq-research/SKILL.md on GitHub (opens in a new tab)

• amc-run-rtsp-calibration

  Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

  skills/amc-run-rtsp-calibration/SKILL.md on GitHub (opens in a new tab)

• amc-run-sample-calibration

  Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

  skills/amc-run-sample-calibration/SKILL.md on GitHub (opens in a new tab)

• amc-run-video-calibration

  Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

  skills/amc-run-video-calibration/SKILL.md on GitHub (opens in a new tab)

• amc-setup-calibration-stack

  Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

  skills/amc-setup-calibration-stack/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.

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

• kermt-continue-pretrain

  Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized training and write model bundles, prepared data, logs, and checkpoints to user-selected host directories.

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

• kermt-embed

  Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories.

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

• kermt-finetune

  Finetune a pretrained KERMT encoder on a labeled CSV. Validate the checkpoint and data, prepare features, and run containerized training. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Write model bundles, prepared data, logs, and trained models to user-selected host directories.

  skills/bionemo-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).

  skills/bionemo-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).

  skills/bionemo-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.

  skills/bionemo-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.

  skills/bionemo-kermt-setup/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…

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

• nvmolkit-usage

  Use when writing or debugging nvMolKit Python code for GPU-accelerated RDKit fingerprints, similarity, conformers, clustering, and molecular searches.

  skills/bionemo-nvmolkit-usage/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.

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

• cudaq-guide

  Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.

  skills/cudaq-guide/SKILL.md on GitHub (opens in a new tab)

• cudaq-importing

  Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.

  skills/cudaq-importing/SKILL.md on GitHub (opens in a new tab)

• cuopt-developer

  Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.

  skills/cuopt-developer/SKILL.md on GitHub (opens in a new tab)

• cuopt-install

  Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.

  skills/cuopt-install/SKILL.md on GitHub (opens in a new tab)

• cuopt-multi-objective-exploration

  Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).

  skills/cuopt-multi-objective-exploration/SKILL.md on GitHub (opens in a new tab)

• cuopt-numerical-optimization-api

  LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

  skills/cuopt-numerical-optimization-api/SKILL.md on GitHub (opens in a new tab)

• cuopt-numerical-optimization-formulation

  LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

  skills/cuopt-numerical-optimization-formulation/SKILL.md on GitHub (opens in a new tab)

• cuopt-routing-api-python

  Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

  skills/cuopt-routing-api-python/SKILL.md on GitHub (opens in a new tab)

• cuopt-server-api-python

  cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.

  skills/cuopt-server-api-python/SKILL.md on GitHub (opens in a new tab)

• dali-dynamic-mode

  DALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.

  skills/dali-dynamic-mode/SKILL.md on GitHub (opens in a new tab)

• data-designer

  Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

  skills/data-designer/SKILL.md on GitHub (opens in a new tab)

• deepstream-dev

  NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.

  skills/deepstream-dev/SKILL.md on GitHub (opens in a new tab)

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