Analyze video content via frames or streams using vision-language models.
npx skills add https://github.com/zrong/skills --skill video-analyzer-zrong
Please help me install this Agent Skill. Skill: video-analyzer Source: https://github.com/zrong/skills/tree/main/video-analyzer Command: npx skills add https://github.com/zrong/skills --skill video-analyzer-zrong
This Skill solves the difficulty of manually reviewing, summarizing, or extracting specific information from long or complex video files by leveraging advanced vision-language models.
Use the video-analyzer skill to analyze the provided video file and summarize its key events in a structured format.
High-intent search queries and answers about installing and using this skill.
You can analyze video content from streaming platform URLs by providing the direct link. The tool uses yt-dlp to handle external video source downloads and extracts frames for vision-language model processing.
Yes, you can extract structured JSON data from a video file. The analysis supports returning JSON-formatted output, making it ideal for automated workflows like identifying animation loops or extracting metadata.
Frame-based video analysis works by using OpenCV for frame manipulation to extract specific frames from the video. These frames are then processed through multimodal vision-language models to provide summaries and Q&A.
You need OpenCV for frame manipulation and yt-dlp for handling external video source downloads. While local files do not require yt-dlp for downloading, OpenCV is still required for extracting frames from the local video stream.
The best way to summarize key events is to use frame-based sampling for cost-efficiency. This approach extracts frames at intervals and passes them through a vision-language model to generate a structured summary.
Yes, video analysis supports direct video input for high-fidelity understanding. You can process raw video streams through vision-language models instead of using frame-based sampling to achieve more detailed results.
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