Skill: Raw Video Processing

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

Prerequisite: FFmpeg and uv 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
• Keyboard typing sounds and other low-level background noise that should be treated as silence
• 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

uv run --python 3.12 /path/to/skills/raw-video-processing/scripts/remove_silence.py <input.mp4> -t="-20dB" -d 0.5

This detects and cuts out silent portions (including keyboard sounds), producing <input>_nosilence.mp4.

Always pass these parameters (tuned for screen recordings with keyboard noise):

• -t="-20dB" — aggressive threshold that filters out keyboard typing and background noise (use = syntax to avoid argparse treating negative values as flags)
• -d 0.5 — remove short silences too (0.5s minimum)
• -p 0.2 — seconds of breathing room kept around speech boundaries (default, usually no need to pass)

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

uv run --python 3.12 /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.
• Conservative cleanup — use -t="-30dB" -d 0.8 if the default is cutting too much speech.
• Extra aggressive cleanup — use -t="-15dB" -d 0.3 and --speed 1.5 for maximum compression.
• 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.