Imported from zc277584121/marketing-skills (
skills/raw-video-processing/SKILL.md) via skills.sh. Install upstream withnpx skills add zc277584121/marketing-skills --skill raw-video-processing. Copyright stays with the author.
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 0.5This 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.mp4This 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
| Flag | Default | Description |
|---|---|---|
-o, --output |
<input>_nosilence.mp4 |
Custom output path |
-t, --threshold |
-30dB |
Silence threshold in dB (higher = more aggressive). Always use -20dB for screencasts — pass as -t="-20dB" to avoid argparse issues with negative values |
-d, --duration |
0.8 |
Minimum silence duration in seconds to remove. Use 0.5 for screencasts |
-p, --padding |
0.2 |
Padding kept around non-silent segments |
--dry-run |
off | Only print detected segments, don't export |
speed_video.py
| Flag | Default | Description |
|---|---|---|
-o, --output |
<input>_<speed>x.mp4 |
Custom output path |
-s, --speed |
1.2 |
Playback speed multiplier |
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 0.8if the default is cutting too much speech. - Extra aggressive cleanup — use
-t="-15dB" -d 0.3and--speed 1.5for maximum compression. - Preview before committing — use
--dry-runon remove_silence.py to see what would be cut without creating a file. - Custom output name — use
-oon 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.
