NVIDIA Corporation

Find the right NVIDIA NuRec skills for turning sensor recordings into renderable 3D scenes. This routing guide organizes dataset downloads, conversion, training, rendering, and frame cleanup into workflows handled by separate specialist skills.

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• Choose the right NuRec skill
• Plan 3D reconstruction from sensor recordings
• Render a pretrained NuRec scene

Turn records from a stopped NVFLARE Auto-FL optimization campaign into a reproducible Markdown report and JSON summary. Summarize improvements, retained candidates, failed ideas, and literature outcomes, with a refreshed progress plot when plotting dependencies are available.

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• Create a final report after an optimization campaign stops
• Summarize baseline scores and retained improvements
• Review successful and unsuccessful literature-based ideas

Create or refresh a governance card, a structured review document for an existing agent skill. It gathers local source evidence, renders a Markdown draft, and checks for unresolved review markers before submission for human review.

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• Create a governance card for an existing skill
• Refresh a card after skill changes
• Prepare material for legal and safety review

Convert existing Hugging Face Trainer or TRL SFTTrainer code into an NVFLARE job for federated learning, where multiple sites train a shared model. Preserve training settings, validate locally, and export the resulting job.

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• Converting Trainer-based training into a federated job
• Adapting PEFT or LoRA fine-tuning for federated learning
• Preserving evaluation metrics during conversion

Build apps for viewing USD, a 3D scene format, using guidance matched to browser streaming or local execution. The skill selects implementation references for rendering, camera controls, selection, interface layout, and validation.

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• Build a browser viewer for USD scenes
• Create a local workstation viewer
• Add camera controls and object selection

Fine-tune the MAISI VAE, a model that compresses and reconstructs CT or MRI volumes, using NIfTI datasets. Prepare training settings, check inputs before training, and save model checkpoints and loss histories for review.

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• Check a NIfTI dataset before GPU training
• Fine-tune a VAE with CT or MRI volumes
• Compare reconstruction losses across training runs

Diagnose slow loading, high memory use, and low frame rates in USD 3D scenes. It coordinates measurement, scene validation, and optimization, then produces reports comparing available before-and-after evidence and an optimized scene when changes are performed.

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• Investigate slow loading in USD scenes
• Diagnose low frame rates and high memory use
• Review repeated structures in large CAD scenes

Fine-tune NV-Generate-CTMR MR-Brain v1 using supplied brain MRI volumes. Check the input file list, prepare training configurations, run training, and optionally generate sample volumes, producing a saved model and a structured run summary.

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• Check a brain MRI dataset before GPU training
• Fine-tune a model with T1, T2, FLAIR, SWI, or MRA data
• Prepare a training command with explicit data and output paths

Coordinate defect image generation, augmentation, and labeling for circuit board, metal surface, and glass inspection on OSMO. Select workflows that create initial board datasets from CAD scenes, process real images, or fine-tune NVIDIA Cosmos AnomalyGen models.

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• Creating initial circuit board inspection datasets from CAD scenes
• Generating circuit board texture and component pose defects
• Running inference and labeling on real inspection images

Find the right next step for an unclear NVFLARE request. Uses source or data inspection evidence to recommend a specific workflow skill for conversion, validation, deployment, or diagnosis, while keeping the project unchanged.

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• Choosing where to start with NVFLARE
• Mapping a local project to a FLARE workflow
• Resolving unclear or conflicting training ownership

Run NV-Segment-CTMR to divide CT or MRI NIfTI volumes into labeled regions. Save the label map and records of region volumes, processing time, and geometry checks for technical verification, without clinical interpretation.

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• Automatically segmenting CT body volumes
• Segmenting body MRI or preprocessed brain MRI
• Requesting regions by specific label IDs

Find PhysicsNeMo models, data loading pipelines, and examples for machine learning tasks involving physical systems. The skill checks current repository files, presents relevant alternatives, and suggests a reading order without writing training code.

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• Finding model families for physical forecasting
• Comparing models for grids and unstructured meshes
• Finding data loading pipelines for a dataset

Switch the vision-language model, which handles images and text, in VSS Alerts Blueprint. Configure alert generation, verification, and agent queries separately, choose an external endpoint or bundled vLLM deployment, and verify an actual inference response.

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• Switch all three VSS services to a different model endpoint
• Run standalone rtvi-vlm with an external model server
• Serve a model with vLLM inside the RTVI container

Benchmark a deployed NVIDIA RAG Blueprint server using a YAML configuration. Measure time spent in each processing stage, run optional aiperf load tests, and compare response latency, throughput, and bottlenecks across concurrency and retrieval settings.

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• Find slow stages in a running RAG server
• Compare performance across concurrency levels
• Measure the impact of retrieval and reranking settings

Coordinate conversion of CAD and other source assets into simulation-ready USD, a 3D scene format. Organizes material and physics assignment, SimReady conformance checks, validation, and optional preview images and deliverable packages, with consolidated reports.

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• Convert CAD assets for simulation
• Assign materials and physics properties
• Validate existing USD assets against SimReady requirements

Provides shared guidance and templates used by NVFLARE conversion skills. Supports consistent dependency setup, site-specific data paths, validation, and custom model aggregation, combining results from participating sites. It is an internal companion rather than a standalone converter.

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• Consulting dependency setup rules during conversion
• Checking data paths for participating sites
• Following shared validation and reporting guidance

Deploy and manage NVIDIA RAG Blueprint, a system that retrieves information to support AI answers. Check the running environment, configure models and search features, troubleshoot unhealthy services, and verify affected workflows after changes.

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• Deploy NVIDIA RAG Blueprint
• Configure search and document ingestion
• Change models or enable image features

Convert existing PyTorch training code into an NVFLARE federated learning job, where sites train with their own data. Adapt training and evaluation, exchange model updates through the Client API, validate locally, and export the job.

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• Convert a manual PyTorch training loop to federated learning
• Adapt existing evaluation code for federated rounds
• Validate and export a FedAvg training job

Convert existing PyTorch Lightning training code into an NVFLARE job for federated learning, where sites train collaboratively while keeping data local. It integrates model exchange through the Lightning Client API and supports local validation or job export.

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• Converting Lightning training code to federated learning
• Preparing collaborative training with data kept at each site
• Checking a converted job in a local simulation

Generate synthetic CT volumes with paired masks that mark anatomical regions or tumors. Configure body regions and output dimensions, then inspect image slices, label mappings, and recorded generation details for synthetic research workflows.

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• Generate synthetic chest CT volumes with lung masks
• Explore tumor size settings in synthetic CT generation
• Preview configuration and resource estimates before generation

Investigate failures in NVIDIA TAO Visual ChangeNet classification experiments using prediction scores and actual images. Produces an illustrated root cause report covering data, reference images, training settings, and simulated changes to assess possible fixes.

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• Investigate missed defects and poor recall
• Analyze FAR regressions and false positives
• Audit reference images and inspection data

Optimize an existing NVFLARE federated learning job, which trains across participating sites, through isolated candidate changes. Compare results under consistent training budgets and metric definitions, while preserving the best source and recording changes, scores, and artifacts.

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• Compare changes to an existing federated learning job
• Evaluate two approaches under a fixed training budget
• Resume optimization using recorded candidates and results

Generate synthetic 3D brain MRI volumes with NV-Generate-CTMR MR-Brain v1, choosing scan types such as T1 or FLAIR. The wrapper records generation settings and output checks for experimental review, with restrictions on clinical and production use.

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• Generating synthetic T1 or T2 brain MRI volumes
• Trying FLAIR or SWI generation settings
• Specifying a random seed for generation

Generate synthetic body MRI volumes, or three-dimensional images, with NV-Generate-CTMR rflow-mr. The skill stages configuration settings, runs the existing inference workflow, and records output geometry and data checks alongside the generated images.

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• Generate synthetic body MRI volumes for experimentation
• Try supported MRI modalities such as T1 and T2
• Compare outputs with different settings and random seeds