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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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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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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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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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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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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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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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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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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Set up Auto Ontology with Helm or Docker Compose, or connect an agent to an existing deployment through MCP, an agent connection protocol. Guides database connections, readiness checks, and troubleshooting for startup and sign-in problems.
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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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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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Guides fine-tuning public Encodon models on labeled protein-coding sequences or variants. Supports numerical prediction and classification, with data checks, LoRA adapters that train selected parameters, head-only training, or full training, followed by checkpoint and metric checks.
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Prepare a public CodonFM v1 environment and download supported Encodon checkpoints, the files containing trained model weights. Covers container or host setup, GPU access checks, and verification of downloaded weights and configuration files.
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Validate coding-sequence CSV files and extract embeddings, numerical features representing each sequence, with public CodonFM/Encodon models. Prepare extraction commands, choose suitable checkpoints, and verify outputs for downstream modeling of translation efficiency, expression, or mRNA stability.
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Prepare or run variant scoring with public CodonFM Encodon models. Validate coding sequences and codon positions in CSV inputs, assemble the scoring command, and check output arrays that compare reference and alternate codon probabilities in context.
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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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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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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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Integrate PhysicsNeMo domain parallelism, splitting one input across GPUs, into training or inference scripts. Guides DDP or FSDP2 integration, adds support for new operations, and checks distributed outputs and gradients against single-GPU results.
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Find NVIDIA skills for a product or workflow using the current catalog. Get up to three relevant recommendations with reasons, installation commands, and starter prompts, or use an already installed skill when one fits.
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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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Generate videos from DEFT media samples using PAIDF Cosmos Predict. Prepare configuration, run generation in Docker, avoid repeated processing of duplicate media paths, and produce JSONL records linking each original sample ID to successful or missing video outputs.
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Run basic engineering checks for NV-Reason-CXR using chest X-ray images or test fixtures. Test commands with mock responses or run the model locally or through a public API, then inspect JSON results. Not for clinical use.
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