Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end.
Indexed The index holds package records for this repository. The package count is historical registry records; it does not mean those packages are available now.
Repository
Canonical name
NVIDIA/skills
GitHub repository ID
1167034425
Package records
394 (historical registry records, not current availability)
Discovery and queue
First recorded source
marketplace:anthropics/claude-plugins-official
Discovered
Queue state
ok
Recorded errors
0
Last settled processing
Next-check eligibility
Eligible from
The first recorded source is what the queue stored first, not the full discovery history. The error count increases when processing ends in the error state and resets after a successful settlement; it is not a count of all attempts. “Last settled processing” is when it finished, not when it started.
Eligibility is calculated as of . It is not a schedule: each collector run handles only a limited number of sources, and being eligible does not promise when a check happens.
Last saved scan
Repository metadata read
Scan status
complete
Scan saved
Extractor version
2
Continuation record
None
The scan record is separate from the queue state: the queue state shows processing progress, the scan record shows what was last saved. A continuation record only means unfinished state is stored; it has no remaining count.
Source-file evidence coverage
Raw counts of the pinned source files that stored package versions reference in this repository. They show what has been recorded, not how complete the repository is.
Scope and limits
Counts distinct pinned file links (repository, commit, path) across all stored versions, from any package, that name exactly this repository's canonical name. Files of packages owned by this repository are not counted unless some version links to them here. Alias names are not merged and no history is guessed from a current name.
“Known” means a regular-file identity is recorded. It does not mean a whole directory or package is covered. Links that are not valid pinned file URLs are not counted.
395 pinned source files referenced
395 known
0 absent at commit
0 file listing truncated
0 unsupported
0 not recorded
0 read failed
Deduplicated by repository, commit and path; the same path at two commits counts as two file identities. These are not component-group counts.
Failed source-commit reads 0
No source commit has a stored failed read for files that are still unrecorded.
Linked packages 394
Linked by verified repository identity. The count is historical registry records, not current availability.
Deformable DETR for 2D object detection. Uses deformable attention for efficient multi-scale feature processing, lighter than DINO with competitive accuracy. Use when training, evaluating, exporting, quantizing, or running inference for a …
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images. Use when training, evaluating, exporting, or running inference for a TAO monocular depth m…
DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support. Use when training, evaluating, exporting, distilling, qu…
DINOv3 continual self-supervised pre-training. Domain-adapts public DINOv3 ViT backbones on unlabeled images via teacher-student self-distillation (DINO + iBOT + KoLeo, optional Gram anchoring) and converts the EMA teacher into a timm-form…
Real-time stereo depth estimation using FastFoundationStereo (FFS), the distilled bp2 commercial variant of FoundationStereo. Predicts disparity maps from stereo image pairs with ~10× lower latency than full FoundationStereo. Use when trai…
Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D reconstruction. Use when training, evaluating, exporting, or running inference for a TAO FoundationStereo model. Trigger phrases include…
Grounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for language-guided detection — detects objects described by text prompts without a fixed class vocabulary. Use when training, evaluating,…
PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.) with distillation and quantization for deployment. Use when training, evaluating, distilling, quantizing, exporting, or running in…
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs them to learn visual representations; supports pretrain and finetune stages. Use when training, evaluating, exporting, or runn…
MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations (point or box annotations) using a ViT-MAE backbone. Use when training, evaluating, or running inference for a TAO MAL model. Tr…
Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for open-set segmentation guided by text prompts. Use when training, evaluating, exporting, quantizing, or running inference for a T…
Mask2Former for universal image segmentation (panoptic, instance, and semantic). Transformer-based with masked attention for high-quality segmentation results. Use when training, evaluating, exporting, quantizing, or running inference for …
Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or …
NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels and produces general-purpose visual features. Use when training, exporting, or running inference…
NVPanoptix3D for panoptic 3D scene reconstruction from posed RGB images. Produces 3D panoptic segmentation (semantic, instance, and panoptic masks) with occupancy completion. Built on a VGGT backbone with a Mask2Former-style head and 3D fr…
OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a differentiable binarization approach. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a …
OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC and attention-based decoders. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a…
OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries. Use when training, evaluating, exporting, quantizing, or running inference for a T…
Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues. Use when training, evaluating, exporting, or running inference for a TAO Optical Inspection…
PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a pillar-based representation, then applies 2D detection — used in autonomous driving and robotics. Use when training, evaluating, e…
When last recorded, these names pointed to this repository. This is a recorded resolution, not a live GitHub check; packages and metadata are never merged because of it.
No aliases pointing to this repository are recorded.
Unsupported marketplace entries 0
Entries in this repository's marketplace that this registry does not support yet. They are preserved source data and are never fetched or executed.