How does it handle Windows file paths?

The skill strictly enforces Windows path requirements, ensuring backslashes are used for all file operations on Windows systems to prevent tool errors.

Is it suitable for professional broadcast compliance?

Yes, it includes signal analysis tools to check for broadcast-safe levels (signalstats) and technical metadata compliance according to ITU-T standards.

Can this skill detect specific video artifacts?

Yes, it includes specialized commands for detecting black frames, frozen video, blurry content, and interlacing issues using filters like blackdetect and freezedetect.

Can I use this for automated cropping?

Yes, it features cropdetect automation patterns to identify optimal crop parameters and remove black borders from video files automatically.

Does it support objective quality measurement?

Absolutely, it provides implementation patterns for calculating PSNR, SSIM, and VMAF scores to compare original and encoded videos for quality loss.

FFmpeg Video Analysis & QC

Name: FFmpeg Video Analysis & QC
Author: JosiahSiegel

byJosiahSiegel

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Analíticas y Monitorización

Automates video quality control by detecting artifacts, measuring visual metrics, and analyzing frame-level metadata using FFmpeg.

Acerca de

This skill empowers Claude to perform sophisticated video analysis and broadcast-grade quality control using advanced FFmpeg filters. It provides automated detection of black, frozen, or blurry frames, identifies scene changes, and calculates objective quality metrics like PSNR, SSIM, and VMAF. Ideal for engineers building automated ingest pipelines or media workflows, this tool simplifies complex signal analysis and metadata logging, ensuring video content meets technical specifications and quality standards before delivery or further processing.

Características Principales

7 GitHub stars
Precision quality metrics including PSNR, SSIM, and VMAF scores
Detailed frame-level metadata extraction and logging patterns
Automated artifact detection for black, frozen, and blurry frames
Intelligent scene change and auto-crop parameter detection
Broadcast-grade signal statistics and interlace detection (idet)

Casos de Uso

Automated quality control for broadcast and streaming video pipelines
Detecting encoding errors or transmission artifacts in large video libraries
Pre-processing assets for optimal cropping and content-aware editing

Acerca de

Características Principales

7 GitHub stars
Precision quality metrics including PSNR, SSIM, and VMAF scores
Detailed frame-level metadata extraction and logging patterns
Automated artifact detection for black, frozen, and blurry frames
Intelligent scene change and auto-crop parameter detection
Broadcast-grade signal statistics and interlace detection (idet)

Casos de Uso

Automated quality control for broadcast and streaming video pipelines
Detecting encoding errors or transmission artifacts in large video libraries
Pre-processing assets for optimal cropping and content-aware editing