video-to-keyframes

Name: video-to-keyframes
Availability: InStock
Author: trae-community

Extract representative frames and segment boundaries from videos into HTML galleries.

20|19|Updated Jan 29, 2026

Search Tags:#automation #video #storyboard #gallery #keyframes #frame-extraction #scene-segmentation

Ready to run

npx skills add https://github.com/trae-community/trae-skills --skill video-to-keyframes

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: video-to-keyframes
Source: https://github.com/trae-community/trae-skills/tree/main/skills/video-to-keyframes
Command: npx skills add https://github.com/trae-community/trae-skills --skill video-to-keyframes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, opencv-python.

What problem does it solve?

This Skill automates turning long videos into a compact set of representative frames, detects cuts/segments, and generates browsable galleries to support storyboard creation and rapid review.

Core Features & Use Cases

Frame extraction at configurable intervals with quality metrics and metadata
Shot segmentation and candidate keyframe generation for each segment, with gallery outputs
On-disk organization of outputs (frames, metadata, and HTML galleries) for reproducible review and downstream production tasks
Suitable for video review, post-production planning, and storyboard authoring workflows

Quick Start

Run the one-click workflow to process a video by using the included run_video_workflow.py script and review the generated frames, segments, and galleries.

Frequently Asked Questions about video-to-keyframes

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract keyframes from a video for storyboard creation?▼

You can extract keyframes from a video by running a script-driven workflow that samples representative frames, detects scene cuts, and generates on-disk metadata catalogs to support storyboard creation.

What is scene segmentation and how does it help with video review?▼

Scene segmentation detects cut boundaries within a video to divide it into distinct segments, enabling deterministic sampling and rapid review of candidate keyframes for post-production planning.

Do I need numpy and opencv-python to automate frame extraction?▼

Yes, you need numpy and opencv-python installed in a Python 3.10+ environment to run the automated frame extraction and shot segmentation workflows.

Can I generate a browsable gallery of extracted video frames?▼

Yes, the workflow generates browsable HTML galleries alongside on-disk organized frames and metadata catalogs, supporting reproducible review and downstream production tasks.

What's the best way to organize extracted keyframes and metadata for reproducible review?▼

The best way is to use a script-driven workflow that outputs frames, metadata catalogs, and HTML galleries into organized on-disk directories, ensuring reproducible review and downstream production task readiness.

Does frame extraction at configurable intervals support deterministic sampling?▼

Yes, frame extraction supports deterministic sampling at configurable intervals, providing quality metrics and metadata for each extracted representative frame to ensure consistent video review outputs.