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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-benchmark-vlm-qagit clone --depth 1 https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarizationWrote 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/vss-benchmark-vlm-qa)<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-benchmark-vlm-qa"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-benchmark-vlm-qa/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/vss-benchmark-vlm-qa"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-benchmark-vlm-qa.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 | $0.00080 | $0.02761 |
| Opus 5.5 | $0.00032 | $0.01104 |
| Sonnet 5.5 | $0.00016 | $0.00552 |
| Haiku 4.5 | $0.00008 | $0.00276 |
Grade A, and why
vss-benchmark-vlm-qa 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 16d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark video Q&A via vss vlm
Measure accuracy (LLM-as-judge vs ground truth) and latency of end-to-end
video question answering by calling vss vlm run against a deployed Cosmos
Reason 3 RT-VLM. Questions and clips come from DSS dataset vss-devx-base
(nvdataset).
This replaces docker exec vss-agent nat eval for the QA slice. It does not
score tool-calling or trajectories.
When to use
- The user asks to benchmark / evaluate VLM video Q&A after vss-agent / NAT eval was removed.
- The user wants latency and answer accuracy on
vss-devx-base.
When not to use
- Tool-calling or trajectory evaluation — out of scope.
- LVS summarization throughput — use
vss-benchmark-video-summarization. - Ad-hoc single questions — use
/vss-ask-video.
Prerequisites
-
A VSS stack with RT-VLM serving Cosmos Reason 3, and
vss configurealready run sovss configure checklistsrt_vlmasokandvstasok.Configure with a routable address, not
localhost. Clips are addressed as VIOS sensors so RT-VLM fetches them by URL; the URL VIOS mints is built from the configured origin. A loopback origin mints a loopback URL, which means nothing inside the RT-VLM container, so the CLI falls back to inlining the clip as base64 and the VLM rejects anything large withHTTP 422 ... content ... valid string.vss configure --base-url http://<host-ip>:7777avoids that —--base-urlis avss configureflag, not a benchmark one.--inline-mediais a benchmark flag; it forces the old inline behaviour and is only safe for clips under ~10 MB. -
uvand this checkout.benchmark_vlm_qa.pyruns the CLI asuv run --project services/agent --no-dev --extra cli vss. -
A deployment inside this skill's
requires-vssrange (>=3.3.0,<4.0.0— the floor is the Cosmos Reason 3 stack the published baselines were measured on, not a CLI capability: the CLI comes from this checkout either way). This is not enforced automatically. Unlikevss-benchmark-video-summarization, this skill has nopreflight.sh, and adding one just to carry a single check would be out of proportion;vss configure checkis already the gate it runs. Verify by hand when in doubt:
What ships with it
4 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.
- 16d ago Changed 59ce533fde89
- 17d ago Changed e76f81640b5e
- 23d ago Changed · +22 lines 5157780ec73b
- 27d ago First seen · 168 lines · 80 tokens per session scan A d9049af6d1e7
vss-benchmark-vlm-qa 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 80 tokens to every session and 2,761 once invoked, about $0.0003 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 2026-09-20.
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