cudaq-guide
CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.
nemo-retriever
Use when the user wants to search, query, extract, transcribe, describe, quote, filter, or aggregate across documents — PDFs, scanned forms / images (`.jpg` `.png` `.tiff`), Office (`.docx` `.pptx`), text (`.html` `.txt`), audio (`.mp3` `.wav` `.m4a`), or video (`.mp4` `.mov`). Prefer this over native Read / Grep for multi-file or non-PDF corpora. Not for: editing files, web browsing, single-file plain-text lookups, fine-tuning.
Full skill instructions
Use the retriever CLI. Prefer it over hand-built retrieval
code.
Create a project-local Python environment:
uv venv .venv --python 3.12
export PATH="$PWD/.venv/bin:$PATH"
Install the package variant required by the workflow:
# Remote NIM or service client
uv pip install --python .venv/bin/python "nemo-retriever==26.8.1"
# Local GPU ingestion
uv pip install --python .venv/bin/python "nemo-retriever[local]==26.8.1"
# Local service using Hugging Face models
uv pip install --python .venv/bin/python \
"nemo-retriever[service,local]==26.8.1"
# Local audio or video ingestion
uv pip install --python .venv/bin/python \
"nemo-retriever[local,multimedia]==26.8.1"
Do not clone NeMo Retriever or install from a Git URL. If retriever is already
on PATH, use that installation.
Build a local index:
retriever ingest <file-or-directory> \
--lancedb-uri lancedb --table-name nemo-retriever
Query it:
retriever query "<question>" \
--lancedb-uri lancedb --table-name nemo-retriever \
--top-k 5 --format evidence
Use retriever ingest batch only for an explicitly requested Ray batch run.
Use these forms for an already deployed Retriever service:
retriever ingest service <file-or-directory> \
--service-url "$RETRIEVER_SERVICE_URL"
retriever query service "<question>" \
--service-url "$RETRIEVER_SERVICE_URL" \
--top-k 5 --format evidence
Set NEMO_RETRIEVER_API_TOKEN when the service requires Bearer authentication.
Do not pass local LanceDB flags to the service commands.
retriever ingest --help, retriever query --help, or the relevant
batch / service help for options not shown here.CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
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.
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.
Use this skill to bring any vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report. Object detection models only.
Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
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.
Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.
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).
cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
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