NVIDIA AI Blueprint for Video Search and Summarization (VSS) is a reference architecture for applications that use AI to search, analyze, question, and summarize live or recorded video. It is intended for building video analytics agents with vision-language models, language models, retrieval-augmented generation, and NVIDIA NIM microservices. The catalogue entries provide coding-agent rules, skills, commands, agents, instructions, and settings associated with the project.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarizationnpx agentmods add skills/nvidia-ai-blueprints/video-search-and-summarization/rtvi-vlm-perf-testingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/rtvi-vlm-perf-testing)<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/rtvi-vlm-perf-testing"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/rtvi-vlm-perf-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/rtvi-vlm-perf-testing"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/rtvi-vlm-perf-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.08065 |
| Opus 5.5 | $0.00022 | $0.03226 |
| Sonnet 5.5 | $0.00011 | $0.01613 |
| Haiku 4.5 | $0.00006 | $0.00806 |
Grade A, and why
rtvi-vlm-perf-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RTVI VLM Perf Testing
Operating Rules
This repository ships the benchmark implementation under services/rtvi/rt-vlm/perf/. Set the manifest repo to that service directory and confirm that it contains perf/benchmark/ before using repository-specific benchmark commands. Read the repository contributor instructions before changing scripts, configs, docs, or benchmark behavior. Check git status --short before editing and preserve unrelated dirty worktree changes.
The committed planning and canary helpers require Python 3.9+. Executed canaries additionally require SSH, tmux, Docker with NVIDIA Container Toolkit, FFmpeg/FFprobe, and an NVIDIA GPU on the named remote host. Model images and protected artifacts may require the user's existing NGC or artifact-registry access; never request that credentials be stored in a manifest.
Do not start or stop services when the user only asks to inspect existing reports. Treat setup, teardown, and benchmark runs as side-effecting operations and state that you are about to run them.
When editing shell scripts, validate with bash -n. When changing commands, environment variables, Docker behavior, benchmark scenarios, or report workflows, update the matching guidance in the repository AGENTS.md, service README, and perf/benchmark/README.RTVI_VLM.md or PERF_GUIDE.RTVI_VLM.md.
Before launching a new experiment, freeze its identity, workload, metrics, scenarios, and isolated
paths using references/performance-contract.md. Validate and render the plan without executing it:
python3 skills/benchmarking/rtvi-vlm-perf-testing/scripts/perf_plan.py validate plan.json
python3 skills/benchmarking/rtvi-vlm-perf-testing/scripts/perf_plan.py render plan.json
The helper is standard-library only and never starts workloads. A launch, restart, remote edit, or cleanup remains side effecting and requires the applicable authorization. Use a fresh pinned runtime for every new or rerun experiment; never reuse a stale benchmark container.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 393 lines · 56 tokens per session scan A 64bb0e466423
rtvi-vlm-perf-testing is a skill published in the GitHub repository NVIDIA-AI-Blueprints/video-search-and-summarization (1,932 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 8,065 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-06.
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