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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/zc277584121/marketing-skills --skill raw-video-processing- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads raw-video-processing automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
| 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.
FAQ
What does the raw-video-processing skill do?
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 raw-video-processing?
Run npx skills add https://github.com/zc277584121/marketing-skills --skill raw-video-processing in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/raw-video-processing/ (personal) or .claude/skills/raw-video-processing/ (project).
Which AI agents can use raw-video-processing?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is raw-video-processing safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains raw-video-processing?
It is published by zc277584121 in zc277584121/marketing-skills. It has 3,915 recorded installs.
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