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Skills/zc277584121/marketing-skills/raw-video-processing
raw-video-processing logo

raw-video-processing

zc277584121/marketing-skills
1K installs0 stars
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Installation

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

Summary

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

SKILL.md

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.

Score

0–100
63/ 100

Grade

C

Popularity15/30

1,272 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Raw Video Processing skill score badge previewScore badge

Markdown

[![Raw Video Processing skill](https://www.remoteopenclaw.com/skills/zc277584121/marketing-skills/raw-video-processing/badges/score.svg)](https://www.remoteopenclaw.com/skills/zc277584121/marketing-skills/raw-video-processing)

HTML

<a href="https://www.remoteopenclaw.com/skills/zc277584121/marketing-skills/raw-video-processing"><img src="https://www.remoteopenclaw.com/skills/zc277584121/marketing-skills/raw-video-processing/badges/score.svg" alt="Raw Video Processing skill"/></a>

Raw Video Processing FAQ

How do I install the Raw Video Processing skill?

Run “npx skills add https://github.com/zc277584121/marketing-skills --skill raw-video-processing” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Raw Video Processing skill free?

Yes. Raw Video Processing is a free, open-source skill published from zc277584121/marketing-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Raw Video Processing work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Raw Video Processing works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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