NVIDIA Corporation

Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.

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• Launching the AutoMagicCalib backend and web interface
• Configuring service ports and network access
• Adding the optional VGGT model during setup

Model and solve optimization problems with cuOpt through Python, C/C++, or the command line. Choose linear, mixed-integer, or quadratic methods based on the decisions involved, configure stopping limits, and investigate infeasible or unbounded results.

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• Modeling resource allocation problems
• Representing whole-number and yes/no decisions
• Minimizing squared error or variance

Predict the structure of a single protein chain from its amino acid sequence using OpenFold2 NIM. Run through NVIDIA's hosted API or local Docker, optionally add sequence alignments and structural templates, and save predictions with confidence information.

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• Predicting a single protein chain's structure from its sequence
• Adding A3M sequence alignments or mmCIF structural templates
• Comparing predictions from multiple model parameter sets

Build video analytics applications with NVIDIA DeepStream and Python's pyservicemaker API. The skill guides video input, object detection, optional tracking, annotated output, and Kafka messaging, with configuration guidance and fixes for common pipeline errors.

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• Detect objects in video files or camera streams
• Track objects across video frames
• Send detection results to Kafka

Start a cuOpt REST server and build Python or curl clients to submit routing and optimization problems. Follow result polling, translate Python API field names into REST fields, and diagnose rejected requests.

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• Start a cuOpt server locally or in Docker
• Submit a routing problem from Python
• Retrieve optimization results by polling

Build English clinical speech-recognition benchmarks by selecting medical terms, checking pronunciations, and generating synthetic audio. Outputs include audio files and a NeMo manifest, a structured file listing recordings and metadata, ready for a separate scoring stage.

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• Building a speech-recognition benchmark for a clinical specialty
• Checking synthesized pronunciations of drug and procedure names
• Generating evaluation audio across voices, noise, and contexts

Explore competing goals such as cost and service quality through repeated cuOpt solves. Organize efficient alternatives into a Pareto frontier, a set of tradeoff choices, and explain what each improvement costs in another objective.

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• Compare cost and service quality
• Explore return and risk tradeoffs
• Compare delivery distance and vehicle count

Port quantum circuits from Qiskit or other frameworks into CUDA-Q while preserving the original algorithm. Guides gate conversion, measurement ordering, precision checks, and small test comparisons to produce code that runs without the source framework.

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• Porting Qiskit circuits to CUDA-Q
• Comparing measurement results after migration
• Removing source framework dependencies

Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.

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• Build an appointment-scheduling voice agent
• Collect five patient-intake fields through conversation
• Design a custom healthcare conversation with tool calls

Build and solve routing problems in Python with cuOpt, including delivery fleets, round trips, and pickup-delivery tasks. Define vehicle capacities, arrival windows, and visit order constraints, then inspect routes, total cost, and orders that cannot be served.

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• Calculate delivery routes with vehicle capacity limits
• Plan visits within specified arrival windows
• Define pickup-delivery pairs and visit order

Turn molecules listed in a SMILES CSV into numerical feature vectors using a KERMT model. The workflow checks the model and input, cleans molecular notation, and saves four sets of vectors with validity information.

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• Extract molecular feature vectors from a SMILES CSV
• Reuse a local KERMT checkpoint
• Extract features with a released pretrained model

Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.

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• Verify a newly installed AMC service
• Check the sample's calibration errors against ground truth
• Walk through the sample workflow in Swagger UI

Generate small-molecule candidates from a starting structure, optimize supported molecular properties, and save results for validation. Local deployment also supports embeddings, numerical representations of molecules, and workflows that convert between molecular structures and learned internal representations.

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• Generate molecular candidates around a seed structure
• Optimize candidates for QED or plogP
• Obtain molecular embeddings with a local deployment

Check one DICOM medical imaging series before conversion or AI inference by inspecting headers only. Produce a JSON report with file inventory, image orientation, personal information flags, findings, and a pass, warn, or fail verdict.

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• Checking a DICOM series before conversion
• Reviewing image orientation before AI inference
• Recording preflight findings in JSON

Extend an existing grover_base model with a new decoder that reconstructs molecular structure strings, then continue pretraining on your corpus. The workflow validates inputs, rebuilds vocabulary components, and records the conversion and training outputs.

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• Extend an existing grover_base model with cMIM
• Continue pretraining on a molecular corpus
• Trace checkpoint conversion and training records

Design protein backbones, the structural framework of proteins, with RFDiffusion through NVIDIA NIM. Generate new structures, build around existing structural motifs, or design binders, then save PDB files for subsequent amino acid sequence design.

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• Generating a new protein backbone
• Building a backbone around an existing structural motif
• Designing a binder around specified target residues

Integrate a supported object detection model from HuggingFace or NVIDIA NGC into NVIDIA DeepStream. Automates model acquisition and conversion, TensorRT engine building, detection output parsing, multi-stream performance benchmarks, and reports with charts in Markdown, HTML, and PDF.

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• Run the default RT-DETR model in DeepStream
• Integrate a supported HuggingFace or NVIDIA NGC detection model
• Compare performance across multiple video streams

Fine-tune Parakeet TDT v2 on clinical vocabulary using NeMo, then compare key-term error rates before and after training. The results guide whether to keep the model, expand the dataset, or prepare it for deployment.

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• Reducing transcription errors in medication names
• Checking whether training improved clinical term recognition
• Deciding whether to expand the training dataset

Fine-tune a pretrained KERMT model on a CSV of molecular structures and labeled targets. The workflow checks inputs, prepares features, launches training in a container, and saves trained models, logs, and test results.

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• Train a molecular model on labeled CSV data
• Select specific prediction targets for training
• Reuse prepared data to compare training settings

Turn written planning problems into mathematical optimization models. Separate fixed inputs, constraints, decisions, and objectives, then choose linear, mixed-integer, or quadratic formulations for tasks such as production planning, inventory management, and material allocation.

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• Formulate production plans with capacity and cost constraints
• Model inventory limits across purchasing and sales periods
• Define multiple goals in priority order

Modify NVIDIA cuOpt itself, covering C++/CUDA solver code, Python bindings, server endpoints, and CI. Follow project conventions for environment setup, builds, tests, debugging, and contributions, including the required sign-off for submitted changes.

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• Fixing solver internals and investigating numerical issues
• Changing Python APIs or server endpoints
• Setting up a development environment and running tests

Predict protein structures by connecting MSA-Search with OpenFold3. It finds related sequences, aligns them to provide evolutionary context, and saves predicted structures with confidence scores. It also supports comparing predictions with and without that context.

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• Predict protein structures using related sequences
• Save alignments, structures, and confidence scores
• Compare predictions with and without alignment context

Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.

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

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