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-setup-video-analytics-apigit 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-setup-video-analytics-api)<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-setup-video-analytics-api"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-setup-video-analytics-api/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-setup-video-analytics-api"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-setup-video-analytics-api.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 | $0.00052 | $0.02354 |
| Opus 5.5 | $0.00021 | $0.00942 |
| Sonnet 5.5 | $0.00010 | $0.00471 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade B, and why
vss-setup-video-analytics-api 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 26d 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.
> Write any derived `.env` files with `umask 077` + `chmod 600`, Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf http://localhost:8081/livez How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Deploy the video-analytics-api REST service standalone with the user's chosen config and data-log bind. The service exposes port 8081 and /livez only after Elasticsearch, the insertion-timestamp-pipeline, and, when STREAM_TYPE=kafka, the configured Kafka topic requirements are ready.
Instructions
Follow the routing tables and step-by-step workflows below. Each section that ends in workflow, quick start, or flow is intended to be executed top-to-bottom. Detailed reference material lives in references/.
Examples
Worked end-to-end examples are kept under evals/ (each *.json manifest
contains a runnable scenario). Run a Tier-3 evaluation to replay them:
nv-base validate skills/deployment/vss-setup-video-analytics-api --agent-eval
A minimal standalone bring-up looks like:
cd $REPO/deploy/docker
export VSS_APPS_DIR=$(pwd)
export VSS_DATA_DIR=${VSS_DATA_DIR:-/tmp/vss-data}
mkdir -p "$VSS_DATA_DIR/data_log/vss_video_analytics_api"
docker compose -f services/analytics/video-analytics-api/compose.yml up -d vss-video-analytics-api
curl -sf http://localhost:8081/livez
Follow references/deploy-video-analytics-api-service.md for the full
workflow (config source, data-log bind, infrastructure dependencies, REST endpoints).
For the field-by-field JSON config reference, see references/configuration.md.
Limitations
- Requires the matching VSS profile / microservice to be deployed and reachable from the caller.
- NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.
- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.
Troubleshooting
- Error: REST call returns connection refused. Cause: target microservice not running. Solution: probe
/docsor/health; redeploy viavss-build-vision-aior the matchingvss-deploy-*skill. - Error: HTTP 401/403 from NGC pulls. Cause: missing/expired
NGC_CLI_API_KEY. Solution:docker login nvcr.ioand re-export the key before retrying. - Error: container OOM or model fails to load. Cause: insufficient GPU memory for the selected profile. Solution: switch to a smaller variant or free GPUs via
docker compose down.
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.
- 26d ago Changed 820f06b1eca0
- 1mo ago Changed e472aa1a46e9
- 1mo ago First seen · 140 lines · 52 tokens per session scan B 0bb9959ebe20
vss-setup-video-analytics-api is a skill published in the GitHub repository NVIDIA-AI-Blueprints/video-search-and-summarization (1,947 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 2,354 once invoked, about $0.0002 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 2026-09-03.
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