raw-video-processing
This skill processes raw screen recordings by removing silent segments and applying speed adjustments using FFmpeg-based Python scripts.
Is raw-video-processing safe to install?
Review the source first: our audit of raw-video-processing's source files found 2 shell commands, 0 external URLs, file reads and writes (high risk). Every command and URL listed appears verbatim in the skill's source. The skill executes shell commands via uv and performs file system operations to read input videos and write processed output files.
How we audit skills: our security review methodology.
Who is this skill for?
Users who need to edit screencasts by removing dead air, background noise, and keyboard sounds, or by increasing playback speed.
What can you do with it?
- Remove silent segments from raw screen recordings.
- Apply speed multipliers to video files.
- Filter out keyboard typing and background noise from audio tracks.
- Preview silence removal results without creating new files.
How good is this skill?
Quality score: 5/10. The documentation provides clear instructions, specific command examples, and parameter guidance for common use cases.
What does the skill file contain?
# 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 pro...
Frequently asked questions
What dependencies are required?
The skill requires FFmpeg and uv to be installed on the system.
Why should I run silence removal before speed adjustment?
Silence detection relies on original audio characteristics. Speeding up the video first alters these characteristics and reduces detection accuracy.
How do I handle negative values for the silence threshold?
Pass the threshold using the equals syntax, such as -t="-20dB", to prevent the argument parser from interpreting the value as a flag.
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