name: raw-video-processing
description: Post-process raw screen recordings by removing silent segments and applying speed adjustments. Uses FFmpeg-based Python scripts to optimize video pacing automatically.

Skill: Raw Video Processing

Post-process raw screen recordings to improve pacing — remove silent segments, then speed up the result.

Prerequisite: FFmpeg and Python 3 must be installed.

When to Use

The user has recorded a screencast and wants to clean it up before publishing. Typical issues in raw recordings:
- Long pauses / dead air while thinking or waiting for loading
- Overall pacing feels slow and could benefit from a slight speed boost

Default Workflow

When the user provides a raw video file, run both scripts in sequence by default:

Step 1: Remove Silent Segments

python /path/to/skills/raw-video-processing/scripts/remove_silence.py <input.mp4>

This detects and cuts out silent portions, producing <input>_nosilence.mp4.

Default parameters (good for most screencasts):
- --threshold -30dB — silence detection sensitivity
- --duration 0.8 — minimum silence length (seconds) to remove
- --padding 0.2 — seconds of breathing room kept around speech boundaries

The script prints a detailed summary: number of silent segments found, total silence removed, and all kept segments with timestamps. Review this output to confirm the result looks reasonable.

Step 2: Speed Up the Video

python /path/to/skills/raw-video-processing/scripts/speed_video.py <input>_nosilence.mp4

This applies a speed multiplier to the silence-removed video, producing <input>_nosilence_1.2x.mp4.

Default parameters:
- --speed 1.2 — 1.2x playback speed (a subtle boost that doesn't feel rushed)

Script Options

remove_silence.py

speed_video.py

Custom Scenarios

• Only remove silence — run just Step 1.
• Only speed up — run just Step 2 directly on the input file.
• Aggressive cleanup — use --threshold -25dB --duration 0.5 for stricter silence removal, and --speed 1.5 for faster playback.
• Preview before committing — use --dry-run on remove_silence.py to see what would be cut without creating a file.
• Custom output name — use -o on either script to control the output path.

Important Notes

• Always run remove_silence before speed_video. Silence detection works on the original audio; speeding up first would alter the audio characteristics and make silence detection less accurate.
• For long videos (>30 min), the silence removal step may take a few minutes as it processes each segment individually.
• Both scripts preserve video quality — remove_silence uses stream copy (no re-encoding), while speed_video re-encodes with FFmpeg defaults.