video-processing
Guide for video analysis and frame-level event detection tasks using OpenCV and similar libraries. This skill should be used when detecting events in videos (jumps, movements, gestures), extracting frames, analyzing motion patterns, or implementing computer vision algorithms on video data. It provides verification strategies and helps avoid common pitfalls in video processing workflows.
npx skills add majiayu000/claude-skill-registry --skill video-processing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Video Processing ## Overview This skill provides guidance for video processing tasks involving frame-level analysis, event detection, and motion tracking using computer vision libraries like OpenCV. It emphasizes verification-first approaches and guards against common pitfalls in video analysis workflows. ## Core Approach: Verify Before Implementing Before writing detection algorithms, establish ground truth understanding of the video content: 1. **Extract and inspect sample frames** - Save key frames as images to visually verify what is happening at specific frame numbers 2. **Understand video metadata** - Frame count, FPS, duration, resolution 3. **Map expected events to frame ranges** - If test data exists, understand what frames correspond to which events 4. **Build diagnostic tools first** - Frame extraction and visualization utilities provide critical insight ## Workflow for Event Detection Tasks ### Phase 1: Video Exploration ```python # Essential first steps for any video analysis task import cv2 cap = cv2.VideoCapture(video_path) frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) fps = cap.get(cv2.CAP_PROP_FPS) duration = frame_count / fps print(f"Frames: {frame_count}
- Overview
- Core Approach: Verify Before Implementing
- Workflow for Event Detection Tasks
- Phase 1: Video Exploration
- Phase 2: Algorithm Development
- Phase 3: Validation
- Common Detection Approaches
- Frame Differencing
- Contour-Based Detection
- Tracking Position Over Time
- Verification Strategies
- 1. Visual Inspection
- 2. Timing Reasonableness
- 3. Sequence Validation
What does the video-processing skill do?
Guide for video analysis and frame-level event detection tasks using OpenCV and similar libraries. This skill should be used when detecting events in videos (jumps, movements, gestures), extracting frames, analyzing motion patterns, or implementing computer vision algorithms on video data. It provides verification strategies and helps avoid common pitfalls in video processing workflows.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill video-processing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.