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zc277584121/marketing-skills2.2k installs

raw-video-processing

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

How do I install this agent skill?

npx skills add https://github.com/zc277584121/marketing-skills --skill raw-video-processing
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill provides automated video processing capabilities by wrapping FFmpeg commands in Python scripts. It allows users to remove silent segments from recordings and adjust playback speed. The implementation follows standard practices for command execution.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • Runlayerpass

    1/3 files flagged

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

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

FlagDefaultDescription
-o, --output<input>_nosilence.mp4Custom output path
-t, --threshold-30dBSilence threshold in dB (higher = more aggressive). Always use -20dB for screencasts — pass as -t="-20dB" to avoid argparse issues with negative values
-d, --duration0.8Minimum silence duration in seconds to remove. Use 0.5 for screencasts
-p, --padding0.2Padding kept around non-silent segments
--dry-runoffOnly print detected segments, don't export

speed_video.py

FlagDefaultDescription
-o, --output<input>_<speed>x.mp4Custom output path
-s, --speed1.2Playback 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 0.8 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.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/zc277584121/marketing-skills/raw-video-processing">View raw-video-processing on skillZs</a>