NVIDIA-AI-Blueprints

NVIDIA-AI-Blueprints/video-search-and-summarization

Skill Codex needs its repo

Plan, run, and diagnose reproducible RT-VLM GPU performance canaries and benchmarks. Use this skill when running fresh-container stream-capacity, semantic-isolation, latency, throughput, or regression experiments against an RTVI microservices checkout.

NVIDIA-AI-Blueprints/video-search-and-summarization

Skill Claude CodeCodex

Measure retrieval quality and latency of a deployed VSS search profile by ingesting a labelled dataset and running the vss CLI across retrieval paths. Use when the user asks to "benchmark video search", "compare search recall between builds", or "profile search latency" on a deployed VSS profile.

NVIDIA-AI-Blueprints/rag

Skill Claude Code

NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion…

NVIDIA-AI-Blueprints/rag

Skill Claude Code needs its repo

Filesystem RAG benchmarks: corpus/, train.json, evaluaterag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

NVIDIA-AI-Blueprints/rag

Skill Claude Code

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).