Nemo Relay Plugin Adaptive Tuning
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Configure and evaluate adaptive plugin behavior for NeMo Relay applications to optimize performance and request handling.
Skills from NVIDIA’s repositories that passed our trust, licence and security gates. Each keeps its author’s attribution.
| Repository | Tier | Verdict | Authorship | Checked | Skills |
|---|---|---|---|---|---|
| NVIDIA/skills | Vendor | Approved · created 2026-02-25, pushed 2026-11-06, 3888 stars | Written in-house | 2026-11-07 | 431 |
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Configure and evaluate adaptive plugin behavior for NeMo Relay applications to optimize performance and request handling.
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Builds a native, single-GPU cuDF proof of concept for query engines, focusing on adapter integration and buffer ownership.
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Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.
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Prepare and validate object-detection false-negative data for AnomalyGenNext generation workflows.
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Manage Cosmos-Embed1 video-text embedding workflows for retrieval, search, and fine-tuning.
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Plan, install, and validate NVSHMEM and NVSHMEM4Py across various environments.
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Manage the lifecycle and inference requests for TAO model microservices across multiple compute platforms.
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Visual ChangeNet for binary image classification and segmentation in AOI defect detection. Use when training, evaluating, exporting, or running inference for PCB defect detection or visual inspection, comparing image pairs for PASS/NO_PASS classification, or producing change-segmentation masks. Trigger phrases include "train Visual ChangeNet", "ChangeNet classify", "ChangeNet segment", "AOI defect detection", "PCB inspection model".
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Hardware-accelerated DEFLATE and LZ4 compression offload for NVIDIA BlueField DPU and ConnectX NICs using the DOCA Compress library.
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Run streamed Unitree G1 cloud-navigation demos in Isaac Sim with guarded gantry release. Use for demo, destination, Mission Control, or cleanup requests.
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A workflow for building and verifying the Holoscan SDK 5.x from source using the repository run wrapper.
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Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).
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Standardized documentation and docstring conventions for the NeMo-RL project.
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A discovery skill for querying available TAO models, application workflows, and AutoML capabilities within the TAO Skill Bank.
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Define and validate NVIDIA PVA operator specifications, test matrices, and evidence for OpGen handoff.
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Configure and validate sequence packing for efficient LLM and VLM training in Megatron-Bridge.
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Automate the selection, validation, and deployment of NVIDIA Dynamo Kubernetes recipes.
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Run iterative improvement for NVIDIA TAO CLIP / SigLIP image-text retrieval on attribute-labelled data. Use when a request combines retrieval evaluation, weak-attribute or caption-pair mining, repeated retraining, and a stopping condition based on a retrieval KPI, validation plateau, or iteration budget; the customer need not know the DEFT or People Attribute Search (PAS) names. The self-contained workflow performs dataset preparation, zero-shot evaluation, attribute gap analysis, caption-space k-NN mining, history-aware selection, retraining, and re-evaluation. Treat `tao-deft-pas` as shorthand for this canonical `tao-run-deft-pas` workflow. Do not use for standalone CLIP training, one-off evaluation or embedding, generic k-NN mining, or AOI/ChangeNet DEFT workflows.
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NVIDIA TAO Mask Auto-Label (MAL) tool for weakly-supervised segmentation using ViT-MAE backbones.
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Install and verify NVIDIA cuOpt for Python, C, or REST server environments.
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Orchestrates the creation and maintenance of an agent's ETHOS.md intent snapshot.
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Execute the end-to-end CDF AnomalyGen pipeline for synthetic defect image generation and model fine-tuning.
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Manage HOLOLINK_def.svh configuration files for HSB IP through generation, validation, and comparison workflows.
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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.