cudaq-guide
CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.
tao-list-capabilities
Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests. Use when the user asks "what can TAO Skill Bank do", "list TAO models", "which TAO workflows are available", or "what supports AutoML".
Full skill instructions
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
Use this skill when the user asks what tao-skill-bank can do, asks for plugin
capabilities, asks which application or data workflows are available, asks which
models are supported, or asks what models are capable with AutoML.
Run scripts/list_tao_capabilities.py for general capability questions, or
scripts/list_tao_models.py for model/action and AutoML support questions.
For a general capabilities answer, run the packaged helper:
${TAO_SKILL_BANK_PATH:-~/tao-skill-bank}/scripts/list_tao_capabilities.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --format text
Use the helper output as the source of truth for the answer instead of manually enumerating capabilities from this skill or plugin metadata. Include:
applications/ and what it can do.data/ and what it can do.skills/platform/tao-run-on-*/SKILL.md frontmatter.models/: train,
evaluate, inference, export, and TensorRT engine generation when those actions
are present in the packaged schema manifest.When the user asks which TAO models are available or which actions a model can run, use the packaged model-list script instead of manually scanning model folders:
${TAO_SKILL_BANK_PATH:-~/tao-skill-bank}/scripts/list_tao_models.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --scope all --format text
The model list comes from skills/models/schemas.manifest.json.
When the user asks what models are capable with AutoML, use the same model-list script in AutoML mode, or the compatibility wrapper:
${TAO_SKILL_BANK_PATH:-~/tao-skill-bank}/scripts/list_tao_models.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --scope automl --format text
${TAO_SKILL_BANK_PATH:-~/tao-skill-bank}/scripts/list_automl_support.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skill-bank} --format text
Use --action distill, --action prune, or --action quantize when the user
asks about a specific compression action.
AutoML support for an action requires
skills/models/<model_skill>/schemas/<action>.schema.json to be packaged with the plugin
and parse successfully as JSON. If that dataclass schema is missing or invalid,
do not describe the model/action as AutoML-supported.
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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