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.
Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling…
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via liv…
Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops,…
Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, Mean-Variance/SOCP variance caps, efficient frontiers, scenario generation, or NVIDIA cuOpt.
Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected …
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint…
Run RFDiffusion protein backbone design via NVIDIA NIM. Use for de novo protein backbones, motif scaffolding, binder design, hotspot residues, contigs syntax, diffusion steps, hosted NVIDIA API calls, local Docker deployment, and PDB backb…
How to swap the DeepStream CV detection model in the VSS Alerts Blueprint verification (2d_cv) mode - covers ONNX export, custom bbox parsers, compose mount gotchas, nvinfer config, runtime TRT engine build, deployment, and a segmentation-…
Scaffold a standalone RTVI CV microservice that plugs into VSS Search and Alerts profiles via Kafka mdx-raw. The shipped scaffold script is a YOLO26 reference implementation (ONNX, labels, custom parser required). Use when building a new p…
How to swap the VLM in the VSS Alerts Blueprint — covers RTVI-VLM microservice deployment methods, all three VLM consumers (rtvi-vlm, vlm-as-verifier, vss-agent), and health checks.
Mod or remaster a game with RTX Remix - open and edit projects, swap textures and models. Connect to the Remix Toolkit App via MCP. Not for non-Remix game interaction.
Use only to generate or update a governance skill card for a specified existing agent skill directory. Do not use for explaining, listing, comparing, or discussing skill capabilities.
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics…
Run TAO Data Services KPI analysis for object detection, comparing inference annotations against ground truth to compute per-class TP/FP/FN/TN, precision, recall, accuracy, and AP at a fixed IoU. Use when an object detection workflow needs…
Run TAO Data Services object-detection gap analysis from ground-truth and inference annotations. Use when an object detection workflow needs to identify weak images by comparing model predictions against ground truth using per-class recall…
Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sam…
Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions. Use when the user asks to "analyze VLM BCQ gaps", "extract VLM false positives and false negatives", or identify failure case…
The contract home for TAO's SDK-free execution pipeline — authoritative JSON Schemas for the four typed artifacts (spec-bundle, job-record, results_dir layout, best_rec) plus the fixed job-status vocabulary and the nested-not-dotted spec r…
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.