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-deploy-video-embeddinggit 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-deploy-video-embedding)<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-deploy-video-embedding"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-deploy-video-embedding/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-deploy-video-embedding"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-deploy-video-embedding.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 | $0.00115 | $0.02198 |
| Opus 5.5 | $0.00046 | $0.00879 |
| Sonnet 5.5 | $0.00023 | $0.00440 |
| Haiku 4.5 | $0.00012 | $0.00220 |
Grade B, and why
vss-deploy-video-embedding scanned grade B with 2 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 27d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
`sudo -n docker` and stop with the exact manual command if passwordless sudo is Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| [references/rest-api.md](references/rest-api.md) | Full REST endpoint catalog with worked `curl` examples for file uploads, video/text embeddings, live streams, and health/metrics. | How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VSS Video Embedding (RT-Embed)
Use this skill for the RT-Embed video embedding microservice, including the standard Cosmos-Embed1 deployment path and custom/BYOM embedding model work.
Trigger phrases: vss-deploy-video-embedding, RT-Embed, rtvi-embed,
video embedding service, Cosmos-Embed1, embed live stream, embed video file, generate video embeddings, text embedding for video search,
RT-Embed BYOM, VideoPrism embed, custom embed model,
MODEL_IMPLEMENTATION_PATH, MODEL_REPOSITORY_SCRIPT_PATH, bring your own embedding model.
Do not use this skill for RT-CV, RT-VLM, VSS Agent, or general VSS deployment work unless the request deploys, operates, integrates, or customizes RT-Embed.
When to Use
- Deploy, size, upgrade, roll back, or tear down standalone RT-Embed (Cosmos-Embed1 or a custom/BYOM model)
- Call RT-Embed's
/v1API for text/video embeddings, live streams, model listing, health, metrics - Wire RT-Embed into another service with Redis, Kafka, OpenTelemetry, auth, or storage
- Add or validate a custom/BYOM embedding backend
- Debug RT-Embed readiness, model/cache startup, or Redis/Kafka reachability
Service Snapshot
- Skill:
vss-deploy-video-embedding. - Legacy 3.1 name: RT-Embed.
- Compose service:
rtvi-embed. - Container name:
vss-rtvi-embed. - Image:
ghcr.io/nvidia-ai-blueprints/vss/vss-rt-embed(override withVSS_RT_EMBED_IMAGE). - Default tag:
develop-latest(override withVSS_RT_EMBED_TAG; usedevelop-latest-sbsafor an SBSA/DGX Spark host). - Profile:
rtvi-embed. - Container port:
8000(host-side${RTVI_EMBED_PORT}). - Default model:
cosmos-embed1-448pfromnvidia/Cosmos-Embed1-448p. - BYOM loader variables:
MODEL_PATH,MODEL_IMPLEMENTATION_PATH,MODEL_REPOSITORY_SCRIPT_PATH. - Health endpoint:
GET /v1/ready. - Healthcheck startup grace:
1200s(20 minutes) on first boot.
Route First
Choose one primary path before acting. Load the linked reference and follow it; do not duplicate full workflows from this top-level file.
What ships with it
13 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.
- BENCHMARK.md 3.8 KB
- evals/byom_custom_model_workflow.json 1.9 KB
- evals/byom_routing.json 1.7 KB
- evals/evals.json 2.0 KB
- evals/standalone_deploy.json 4.0 KB
- references/byom-custom-model.md 12 KB
- references/deploy-vss-deploy-video-embedding.md 13 KB
- references/environment.md 8.7 KB
- references/integrate-vss-deploy-video-embedding.md 19 KB
- references/README.md 1.8 KB
- references/rest-api.md 13 KB
- references/troubleshooting.md 7.8 KB
- skill-card.md 4.9 KB
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.
- 27d ago Changed 6d6df57dcb92
- 28d ago Changed · +8 lines 474f148a32e7
- 1mo ago Changed · -143 lines · +29 tokens per session 7fad86469446
- 1mo ago First seen · 274 lines · 86 tokens per session scan B a023102dbdef
vss-deploy-video-embedding is a skill published in the GitHub repository NVIDIA-AI-Blueprints/video-search-and-summarization (1,951 stars, last pushed today), licensed Apache-2.0. It adds 115 tokens to every session and 2,198 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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