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Found 10 Skills
Use to deploy the vss-video-analytics-api REST service standalone (config-source, data-log bind, Elasticsearch, optional Kafka). Not for full warehouse deploy.
Query video analytics data and metrics from Elastic search via the VA-MCP server (port 9901). This includes incidents, alerts, sensor data, and metrics. Use for any question about violations, alerts, incidents, object counts, speeds, occupancy, or anything that requires looking up recorded events. This is the primary way to answer a question that requires incidents, alerts and other metrics such as people counts and violations.
Use this skill when reading video-analytics metrics, incidents, alerts, and sensor data via the VA-MCP server (port 9901). Not for live VLM or incident-range narrative reports.
Use this skill when producing a VSS analysis report — Mode A per-clip VLM, Mode B incident-range via video-analytics. Not for real-time alerts or ad-hoc Q&A.
Research YouTube topics, analyze competitor videos, deconstruct viral content, and query the YouTube Data API. Use when researching a video topic before planning, analyzing video transcripts for viral patterns, searching competitor channels, or fetching video and channel stats via the YouTube Data API v3.
YouTube Data API v3 via curl. Use this skill to search videos, get video/channel info, list playlists, and fetch comments.
Integrate with YouTube for video management. Use when you need to: (1) upload videos to YouTube, (2) manage channel content, or (3) retrieve video analytics and insights.
Comprehensive guide to building video applications with Mux, the developer-first video infrastructure platform. This skill covers video streaming, live streaming, player integrations, analytics with Mux Data, and AI-powered workflows. Whether you are building a video-on-demand platform, live streaming application, or integrating video into an existing product, this documentation provides the patterns and code examples needed to ship quickly.
NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
Use when planning channel distribution and measuring video performance.