NVIDIA Corporation

Develop reusable external Holoscan Modules with the Holoscan CLI, from scaffolding and testing to Python bindings and package creation. Verify that applications can use installed packages in a clean environment, with checks for each requested format.

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• Creating a reusable Holoscan operator module
• Adding and testing Python bindings
• Using an installed module from another application

Generate protein structures and sequences with specified conditions using NVIDIA's hosted API or a local NIM service. Supports binder and antibody design, reward-guided search, and inspection of generated candidates, outputs, and validation results.

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• Generate protein candidates for a specified binding target
• Design antibodies or structures around a retained motif
• Explore candidates with reward-guided search

Guides CDF Auto-Labeling workflows for annotating data such as videos. Routes tasks to specialized references, covering shipped examples, stage selection, prompt and configuration changes, pipeline migration, container retrieval, and output validation.

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• Starting automatic labeling for warehouse-safety videos
• Adapting a shipped workflow to a new domain
• Writing annotation prompts and question banks

Create synthetic defect images for detector training using clean images and masks that mark defect regions. Guide mask placement, Cosmos fine-tuning, image generation, evaluation, quality refinement, and automatic assignment of provisional labels.

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• Generating synthetic PCB defect images
• Placing defect masks on clean images
• Fine-tuning Cosmos for a defect dataset

Helps prepare video and image datasets with Cosmos Curator through splitting, filtering, deduplication, captioning, and embeddings, numerical representations for matching. Covers pipeline configuration, execution checks, Cosmos Dataset Search ingestion and search, and compatible handoffs to TAO data mining.

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• Splitting and filtering videos into training datasets
• Deduplicating data and packaging it into archives
• Adding captions and embeddings to images

Create and validate Cosmos Data Factory YAML configurations for changing video scenes, editing images, or generating videos. Run generation through remote model APIs, configure captions and quality checks, and retry candidates within a defined limit.

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• Changing weather or lighting in source videos
• Editing selected attributes in still images
• Generating videos from an image or text

Review or prepare an NVSHMEM CUDA kernel for TMA, a GPU data-transfer mechanism. Assess hardware suitability, add shared-memory registration and launch settings, and consider direct transfers from shared memory when an intermediate copy is proven redundant.

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• Assessing TMA suitability for an NVSHMEM kernel
• Preparing a selected kernel and its launch settings for TMA
• Reviewing whether an intermediate memory copy can be removed

Recommends network-port assignments and environment settings for NVSHMEM using the compute node’s actual GPU and network topology. Helps select adapters, evaluate multiple-port options, and identify missing evidence before producing an exact configuration.

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• Choosing network ports closest to each GPU
• Checking multi-NIC options for an NVSHMEM version
• Diagnosing unexpected port selection

Choose an NVSHMEM transport, the communication path between compute nodes, from installed plugins, network evidence, and optional workload analysis. Receive a recommended configuration with minimal environment variables, compatibility evidence, alternatives, and clearly stated unknowns.

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• Choosing a transport compatible with a compute system
• Assessing communication needs in selected CUDA code
• Getting minimal transport selection settings

Measure NVSHMEM communication bandwidth and latency, then package results with GPU placement, hardware connections, exact commands, and raw logs. Produce a portable Markdown report for performance checks and comparisons against a baseline collected under matching conditions.

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• Running the four-test device communication sanity suite
• Recording same-node and two-node performance results
• Packaging benchmark logs and environment evidence for handoff

Assess whether NVSHMEM suits communication between GPUs, then learn its core concepts and plan a first C/C++ or Python program. Guidance covers program structure, compilation, launch commands, and appropriate next steps.

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• Assessing whether NVSHMEM fits a multi-GPU application
• Learning GPU communication and shared-memory concepts
• Designing, compiling, and launching a first C/C++ program

Plan and validate NVSHMEM and NVSHMEM4Py installations on individual machines, clusters, or containers. Checks system facts against version-specific documentation, selects a suitable installation method, and prepares commands and runtime validation steps for review.

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• Plan an NVSHMEM installation on a workstation
• Choose a Python installation method for NVSHMEM4Py
• Prepare a source build for a multi-node system

Diagnose NVSHMEM launch failures, crashes, hangs, and communication problems using exact commands and complete logs. Separates observations from likely causes, checks version-specific documentation, and prepares a Markdown bug report for the user to submit.

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• Investigate launch failures, crashes, and hangs
• Distinguish NVSHMEM defects from application or environment issues
• Check whether a known issue applies to the installed version

Find official NVSHMEM and NVSHMEM4Py documentation for installation, APIs, runtime settings, and troubleshooting. Receive concise explanations with direct source links, relevant version details, and explicit uncertainty when the requested documentation or compatibility evidence cannot be verified.

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• Finding installation instructions for a specific release
• Understanding APIs and runtime settings
• Checking NVSHMEM4Py compatibility

Choose the appropriate NVSHMEM performance workflow for collecting benchmark evidence, selecting communication between nodes, assigning network adapters to processing elements, or assessing TMA transfers between peer GPUs. This skill routes requests to specialists that perform the technical work.

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• Choose a workflow for baseline performance measurement
• Find the specialist for inter-node transport selection
• Route network adapter assignment questions

Diagnose GPU bugs in CUDA applications with NVIDIA Compute Sanitizer. Select the appropriate tool for memory access errors, shared-memory races, uninitialized reads, or synchronization faults, then build for source locations and interpret the diagnostic report.

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• Locate illegal or out-of-bounds GPU memory access
• Investigate inconsistent results caused by shared-memory races
• Find reads of uninitialized GPU memory

Connect Omniverse scenes to the Newton physics engine through ovnewton. The skill guides scene import, simulation stepping, synchronization of scene edits, and publication of simulation results for rendering, with setup guidance for supported solvers.

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• Importing a populated ovstage into Newton
• Applying scene edits to a running simulation
• Publishing physics results for Omniverse rendering

Guide the creation and maintenance of an agent’s ethos.md, a record of its purpose, boundaries, and success criteria. Locate existing files and route requests to intent exploration or editing, carrying forward information already confirmed.

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• Defining a new agent’s purpose and boundaries
• Locating an existing ethos.md and identifying gaps
• Updating specific rules or revisiting the agent’s intent

Clarify an AI agent’s purpose, boundaries, and success criteria by reviewing its code and documents, then asking focused questions about intent. The resulting draft is handed to ethos-write to create an ethos.md record.

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• Define the purpose and scope of a new agent
• Document the intended behavior of an existing agent
• Clarify priorities when desired outcomes conflict

Turn an AI agent’s agreed purpose, behavior, boundaries, and success criteria into an ethos.md design document. The skill writes or edits the local file, validates its structure, and reviews the contents with the user.

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• Document an agent design agreed through discussion
• Update an existing ethos.md when intent changes
• Record constraints and priorities for evaluation

Add optional NVIDIA Parabricks GPU steps to an existing genomics workflow while retaining its CPU path. Use runtime switches to compare outputs, execution time, and cost tradeoffs before enabling GPU processing for production.

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• Identify genomics steps suitable for GPU acceleration
• Add switchable CPU and GPU paths to an existing workflow
• Compare CPU and GPU outputs and runtime

Prepare NVIDIA Parabricks commands for individual genomics tools, translate options from CPU tools, and check runtime readiness. Get guidance matched to the target release, including reference compatibility, command flags, and unresolved requirements before execution.

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• Choosing a Parabricks tool for genomic data
• Translating STAR options into command drafts
• Checking GPU and container readiness

Guides the definition and publication of business terms, relationships, and measures in Auto Ontology. Covers SQL validation, model import and export, and persisted-change checks to verify that saved definitions reflect the intended business meaning.

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• Define or revise business terms and relationships
• Check measure formulas, units, and aggregation levels
• Back up models and verify imports

Ask Auto Ontology questions in plain language, then check the generated SQL, returned data, and written answer. Supports MCP or authenticated REST access to verify that reported counts and other findings match the intended question.

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• Checking a count against its SQL and returned data
• Confirming business terms before asking a question
• Reviewing incomplete or truncated query results