No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by NVIDIA-AI-Blueprints · MCP Server · ★ 1.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is video-search-and-summarization safe to install? View the security audit →
NVIDIA AI Blueprint: Video Search and Summarization (VSS) Table of Contents Overview Use Case / Problem Description Agent Workflows Software Components Target Audience Repository Structure Overview Documentation Prerequisites Hardware Requirements Quickstart Guide Contributing License Overview The NVIDIA Blueprint for Video Search and Summarization (VSS) provides a suite of reference architectures for building vision agents and AI-powered video analytics applications. Those architectures bring together accelerated vision microservices, vision language models (VLMs), and large language models (LLMs) so you can use them in existing applications, as standalone microservices, or as part of a larger vision agent. VSS is organized into three areas of processing and analysis: real-time video intelligence (feature extraction, embeddings, and stream understanding with results published to a message broker), downstream analytics (enrichment of metadata into trajectories, incidents, and verified alerts), and agentic and offline processing (orchestrated tools for search, Q&A, summarization, and clip retrieval, including via the Mode
| Stars | 1,899 |
| Forks | 400 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 56.1682996156658/100 |
| Open Issues | 206 |
| Last Updated | 2026-10-03 |
| Created | 2024-10-22 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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video-search-and-summarization is NVIDIA AI Blueprint for video search and summarization (VSS) is a GPU-accelerated reference architecture for building video analytics agents with real-time verified alerts, visual Q&A, and automated r. It is categorized as a MCP Server with 1.9k GitHub stars.
video-search-and-summarization is primarily written in Python. It covers topics such as computer-vision, generative-ai, long-video-understanding.
You can find installation instructions and usage details in the video-search-and-summarization GitHub repository at github.com/NVIDIA-AI-Blueprints/video-search-and-summarization. The project has 1.9k stars and 400 forks, indicating an active community.
The top alternatives to video-search-and-summarization on Agent Skills Hub include Generative-Media-Skills, generative-ai, telemem. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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