ffmpeg-detect

Parse ffmpeg detect filter outputs into JSON-friendly results for media pipelines.

15|4|Updated Apr 18, 2026

Search Tags:#ffmpeg #video-analysis #detect #timecode #qc #scene-change #interlace

Ready to run

npx skills add https://github.com/damionrashford/media-os --skill ffmpeg-detect

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ffmpeg-detect
Source: https://github.com/damionrashford/media-os/tree/main/skills/ffmpeg-detect
Command: npx skills add https://github.com/damionrashford/media-os --skill ffmpeg-detect

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Detects and interprets ffmpeg analysis filters to enable scripted, read-only inspection of media without modifying source, enabling automated decision-making in video pipelines.

Core Features & Use Cases

Supports cropdetect, silencedetect, blackdetect, freezedetect, blurdetect, blockdetect, scdet, idet, signalstats, readeia608, readvitc, and volumedetect to surface analysis results.
Use cases include autocropping, silence trimming, scene splitting, interlace checks, and caption/timecode extraction as a pre-encode QC/automation step.

Quick Start

Run the detect.py script with a detector and an input media file to produce structured results.

Frequently Asked Questions about ffmpeg-detect

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

FAQPage Schema

How do I automate scene detection and silence trimming with ffmpeg?▼

Scene detection and silence trimming with ffmpeg are automated by running Python scripts that execute ffmpeg analysis filters like scdet and silencedetect, parsing stderr to output structured, JSON-friendly results for downstream decision-making in media pipelines.

Can I extract timecode and captions from video using ffmpeg detect filters?▼

Timecode and caption extraction with ffmpeg detect filters is supported through readvitc and readeia608, parsing the filter outputs into structured JSON data to enable automated timecode and caption extraction during pre-encode quality control.

What is the best way to run ffmpeg cropdetect for automated autocropping?▼

Automated autocropping with ffmpeg cropdetect is best handled by running a Python script that executes the cropdetect filter, parses the stderr output, and returns JSON-friendly results to drive deterministic second pass encoding decisions.

Does this ffmpeg analysis approach modify the source video files?▼

This ffmpeg analysis approach does not modify source video files, as it performs scripted, read-only inspection of media using various -vf and -af detectors to enable automated decision-making without altering the original source.

How do I check for interlaced video using ffmpeg idet?▼

Interlace checks with ffmpeg idet are performed by running Python scripts that execute the idet filter, parse the stderr output, and present structured results to automate interlace detection in video processing pipelines.

What ffmpeg detectors are supported for automated video QC?▼

Supported ffmpeg detectors for automated video QC include cropdetect, silencedetect, blackdetect, freezedetect, blurdetect, blockdetect, scdet, idet, signalstats, readeia608, readvitc, and volumedetect, all parsed into JSON-friendly outputs.