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-search-archivegit 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-search-archive)<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-search-archive"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-search-archive/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-search-archive"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-search-archive.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00067 | $0.03133 |
| Opus 5.5 | $0.00027 | $0.01253 |
| Sonnet 5.5 | $0.00013 | $0.00627 |
| Haiku 4.5 | $0.00007 | $0.00313 |
Grade A, and why
vss-search-archive 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 18d 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search archived VSS video
When to Use
- Search archived VSS video that is already registered in a configured deployment, by natural-language, similarity, attribute, object-ID, or lexical tag query.
Not for:
- A fresh visual question about a supplied local clip —
vss-ask-video. - Long-form summarization of a recording —
vss-summarize-video. - Deploying or changing a profile —
/vss-build-vision-ai. - Ingesting or deleting a source — use the deployment's source-management workflow.
Answer from the configured deployment through the installed vss CLI. Do not
fall back to raw REST when a CLI command fails.
Hard rule — use the
vssCLI for retrieval.vss configurehas already pointed the CLI at the deployment, so every search action goes throughvss search runand nothing reaches the deployment any other way.Four things follow:
- use the
vsson PATH (see Prerequisites); neverdocker exec,kubectl exec, a pod shell, or a hand-built/api/v1/searchcall;- a named source is resolved with
vss vios listbefore search — never inferred from a display name, and never substituted when missing;- capture stdout and the exit status separately — never put the command behind
if !, which hides the real exit code, and never discard usable exit-6 results;- a failing command is a finding: report the exit code rather than routing around it, repairing the deployment, or retrying with broadened scope.
Prerequisites
- A running VSS
searchprofile withvss configurealready run against its origin. Refresh configuration after first ingestion, once source provisioning has established readiness; lazy raw indexes enable frame enrichment for attribute/fusion. - The
vssCLI on PATH. The OpenClaw and Hermes harness images ship it; anywhere else, install it from the same checkout as this skill so the CLI and the skill match:uv tool install <checkout>/libs/vss/cli.
Bootstrap, exit codes, and common CLI rules live in AGENTS.md.
What ships with it
6 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.
- 18d ago Changed · -39 lines · +21 tokens per session 372d4d4bf1b8
- 19d ago Changed · +4 lines 8f519908c549
- 22d ago Changed · +2 lines b6641e7092f2
- 28d ago Changed b7fd1ebc8187
- 29d ago Changed · +12 lines 2c62c8d964bb
- 1mo ago Changed · -3 lines 63338cd37aab
- 1mo ago Changed · +2 lines 11d8e4ba5c75
- 1mo ago Changed · +5 lines c14da53d2c5c
- 1mo ago Changed c01a239a0ffb
- 1mo ago First seen · 255 lines · 46 tokens per session scan A 86fcc42b517d
vss-search-archive is a skill published in the GitHub repository NVIDIA-AI-Blueprints/video-search-and-summarization (1,953 stars, last pushed today), licensed Apache-2.0. It adds 67 tokens to every session and 3,133 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 2026-09-03.
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