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-generate-video-reportgit 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-generate-video-report)<a href="https://agentmods.dev/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-generate-video-report"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-generate-video-report/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-generate-video-report"><img src="https://agentmods.dev/badge/skills/nvidia-ai-blueprints/video-search-and-summarization/vss-generate-video-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 520 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 543 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 652 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 679 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 656 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 683 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00061 | $0.09353 |
| Opus 5.5 | $0.00024 | $0.03741 |
| Sonnet 5.5 | $0.00012 | $0.01871 |
| Haiku 4.5 | $0.00006 | $0.00935 |
Grade A, and why
vss-generate-video-report scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf --max-time 5 "${VST_API_BASE:?paste the Endpoint resolution output at the top of this block}/sensor/version" >/dev/null \ How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report
When to Use
- Generate a per-clip VLM video analysis report (Mode A)
- Generate a narrative report over an incident/alert time range (Mode B)
- Generate an SOP compliance report via the SOP tools (Mode C)
Not for standalone video summarization, real-time alerts, or ad-hoc Q&A.
Generate a video analysis report by routing to one of three backends — never via POST /generate on the VSS agent.
| Mode | Backend | Steps |
|---|---|---|
| A. Video clip | A1 /vss-manage-video-io-storage → clip URL → VLM chat/completions OR A2 local video file on disk or base64 video + a VLM endpoint (caller-supplied or Step 2-discovered) |
references/report-types/video-analysis.md |
| B. Incident range | /vss-query-analytics → incident list → narrative report |
references/report-types/incident-range.md |
| C. SOP compliance | VA-MCP get_sop_report (direct MCP call on ${VA_MCP_URL}) → SOP compliance report |
references/report-types/sop-compliance.md |
If the request is ambiguous (e.g. "report on <sensor>" with no time range and no incident wording), default to Mode A. Ask only when the request names a sensor and a time range and carries neither incident / alert wording (→ Mode B) nor SOP / compliance wording (→ Mode C). Never run any mode's probe or gate until the mode is settled. See Examples below for the request phrasings that route to each mode.
Instructions
Select the report mode by request intent before any probes. Modes B/C report
over stored incident/SOP records and bypass this video-source check, even when
their sensor is a camera. For Mode A, before timeline resolution or inference
(including the workspace's short-report route), reject any stream (RTSP/RTSPS
URL or registered camera), including recorded time windows, with:
Live-stream summarization / report generation isn't supported. Then stop;
do not ingest, extract clips, or invoke inference. Reject an explicitly identified
stream/camera even if its URL is absent. For a registered sensor name/id only,
check its current type with vss vios list --sensor <name>; never reuse an earlier
file classification. Reject a stream; if the type is unclear or the lookup
fails, report that limitation and stop. Local/uploaded files, direct recorded-video
URLs, and Mode A2 local/base64 inputs are not sensor handles; use their file workflow.
What ships with it
10 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.
- evals/base_profile_report.json 11 KB
- evals/evals.json 12 KB
- references/default-vlm-prompt.md 354 B
- references/report-templates/incident-range-report.md 686 B
- references/report-templates/sop-compliance-report.md 1.2 KB
- references/report-templates/video-analysis-report.md 502 B
- references/report-types/incident-range.md 5.4 KB
- references/report-types/sop-compliance.md 11 KB
- references/report-types/video-analysis.md 27 KB
- skill-card.md 4.0 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.
- 1mo ago Changed · +13 lines e3f782d479a7
- 1mo ago Changed 96fd808c2496
- 1mo ago Changed 584f3e8ed2d2
- 1mo ago Changed · +8 lines b41521ab024e
- 1mo ago Changed · +2 lines 6fd84d3909e0
- 1mo ago Changed · +5 lines ddc3b3720811
- 1mo ago Changed · +5 lines d0bfe664b0d5
- 1mo ago Changed · -329 lines 524b563acc16
- 1mo ago Changed 6b04114b2ef2
- 1mo ago First seen · 718 lines · 61 tokens per session scan A 7f28bed68716
vss-generate-video-report is a skill published in the GitHub repository NVIDIA-AI-Blueprints/video-search-and-summarization (1,957 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 9,353 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (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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