cudaq-guide
CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.
nemoclaw-user-guide
Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.
Full skill instructions
Use the canonical NemoClaw documentation as your source of truth. Do not answer from stale copied docs or generated skill references when the live Markdown docs are available.
https://docs.nvidia.com/nemoclaw/_mcp/server.searchDocs tool to search the canonical docs and collect source URLs.https://docs.nvidia.com/nemoclaw/llms.txt..md page listed in the index or returned by docs search for the user's task..html suffix with .md, or append .md to the route when the URL has no suffix.For Claude Code, run:
claude mcp add --transport http fern-docs https://docs.nvidia.com/nemoclaw/_mcp/server
For Cursor, add https://docs.nvidia.com/nemoclaw/_mcp/server to the MCP server configuration.
For other MCP clients, configure a streamable HTTP MCP server at that URL.
Use these pages first for common onboarding flows:
https://docs.nvidia.com/nemoclaw/latest/user-guide/openclaw/home.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/openclaw/get-started/prerequisites.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/openclaw/get-started/quickstart.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/hermes/home.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/hermes/get-started/prerequisites.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/hermes/get-started/quickstart.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/deepagents/home.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/deepagents/get-started/prerequisites.md.https://docs.nvidia.com/nemoclaw/latest/user-guide/deepagents/get-started/quickstart.md.<PASTE_YOUR_API_KEY_HERE> in examples.inference pages from llms.txt.network-policy pages and the network policies reference.manage-sandboxes, monitoring, and command reference pages.security, deployment/sandbox-hardening, and architecture pages.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.
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.
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).
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