Agent skill

Video Processing Editing

by curiositech in curiositech/some_claude_skills

FFmpeg automation for cutting, trimming, concatenating videos.

MITAuto-check passedMedia & Creative

Install Video Processing Editing

skills CLI
$ npx skills add curiositech/some_claude_skills --skill video-processing-editing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install curiositech/some_claude_skills video-processing-editing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/curiositech/some_claude_skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/video-processing-editing .claude/skills/video-processing-editing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
video-processing-editing
GitHub stars
243
Token cost
~4.2k tokens
SKILL.md length
743 words
Files
13 (incl. scripts, references)
Skills in repo
109
Repo updated
First seen
Licence
MIT

At a glance

FFmpeg automation for cutting, trimming, concatenating videos.

  • Videogen projects
  • SKILL.md covers When to Use, Technology Selection, Common Anti-Patterns and Production Checklist, plus 3 more sections
  • Runs Python scripts from its folder; calls ffmpeg and ffprobe
  • Content creation

What it does

Video Processing Editing is an agent skill from curiositech/some_claude_skills. FFmpeg automation for cutting, trimming, concatenating videos. Audio mixing, timeline editing, transitions, effects. Export optimization for YouTube, social media. Subtitle handling, color grading, batch processing. Use for videogen projects, content creation, automated video production. Activate on "video editing", "FFmpeg", "trim video", "concatenate", "transitions", "export optimization". NOT for real-time video editing UI, 3D compositing, or motion graphics.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts and reference files (for example `.claude-plugin/plugin.json`, `references/export-optimization.md` and `references/ffmpeg-guide.md`).

It sits in Media & Creative, covering Video production. It works with FFmpeg and YouTube. The repository describes itself as: Claude skills that make my life easier. The licence is MIT.

When your agent uses it

  • Videogen projects
  • Content creation
  • Automated video production

Example prompts

  • “video editing”
  • “FFmpeg”
  • “trim video”
  • “/video-processing-editing”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(ffmpeg*,ffprobe*,python*)

What it can do on your machine

Read from SKILL.md and the folder at commit 6713fc7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(ffmpeg*
    • ffprobe*
    • python*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 7 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • ffmpeg
    • ffprobe

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Video Processing Editing loads about 4.2k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 123 tokens; SKILL.md has 743 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~123
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from curiositech/some_claude_skills at commit 6713fc7, republished under its MIT licence (© curiositech). 743 words, ~4,248 tokens.

Download SKILL.mdSave it as .claude/skills/video-processing-editing/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
video-processing-editing
description
FFmpeg automation for cutting, trimming, concatenating videos. Audio mixing, timeline editing, transitions, effects. Export optimization for YouTube, social media. Subtitle handling, color grading, batch processing. Use for videogen projects, content creation, automated video production. Activate on "video editing", "FFmpeg", "trim video", "concatenate", "transitions", "export optimization". NOT for real-time video editing UI, 3D compositing, or motion graphics.
allowed-tools
Read, Write, Edit, Bash(ffmpeg*,ffprobe*,python*)
metadata.tags
video, processing, editing, video-editing, ffmpeg

Video Processing & Editing

Expert in FFmpeg-based video editing, processing automation, and export optimization for modern content creation workflows.

When to Use

✅ Use for:

  • Automated video editing pipelines (script-to-video)
  • Cutting, trimming, concatenating clips
  • Adding transitions, effects, overlays
  • Audio mixing and normalization
  • Subtitle/caption handling
  • Export optimization for platforms
  • Batch video processing
  • Color grading and correction

❌ NOT for:

  • Real-time video editing UI (use DaVinci Resolve/Premiere)
  • 3D compositing (use After Effects/Blender)
  • Motion graphics animation (use After Effects)
  • Basic screen recording (use OBS)

Technology Selection

Video Editing Tools
ToolSpeedFeaturesUse Case
FFmpegVery FastCLI automationProduction pipelines
MoviePyMediumPython APIProgrammatic editing
PyAVFastLow-level controlCustom processing
DaVinci ResolveSlowFull NLEManual editing

Decision tree:

Need automation? → FFmpeg
Need Python API? → MoviePy
Need frame-level control? → PyAV
Need manual editing? → DaVinci Resolve

Common Anti-Patterns

Anti-Pattern 1: Not Using Keyframe-Aligned Cuts

Novice thinking: "Just cut the video at any timestamp"

Problem: Causes artifacts, black frames, and playback issues.

Wrong approach:

bash
# ❌ Cut at arbitrary timestamp (not keyframe-aligned)
ffmpeg -i input.mp4 -ss 00:01:23.456 -to 00:02:45.678 -c copy output.mp4

# Result: Black frames, artifacts, sync issues

Why wrong:

  • Video codecs use keyframes (I-frames) every 2-10 seconds
  • Non-keyframe cuts require re-encoding
  • Using -c copy (stream copy) without keyframe alignment breaks playback
  • GOP (Group of Pictures) structure depends on keyframes

Correct approach 1: Re-encode for precise cuts

bash
# ✅ Re-encode for frame-accurate cutting
ffmpeg -i input.mp4 -ss 00:01:23.456 -to 00:02:45.678 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a aac -b:a 192k \
  output.mp4

# Frame-accurate, but slower (re-encoding)

Correct approach 2: Keyframe-aligned stream copy

bash
# ✅ Fast cutting with keyframe alignment
# Step 1: Find keyframes near cut points
ffprobe -select_streams v -show_frames -show_entries frame=pkt_pts_time,key_frame \
  -of csv input.mp4 | grep ",1$" | awk -F',' '{print $2}'

# Step 2: Cut at nearest keyframes (fast, no re-encoding)
ffmpeg -i input.mp4 -ss 00:01:22.000 -to 00:02:46.000 -c copy output.mp4

# Blazing fast, no quality loss, but not frame-accurate

Correct approach 3: Two-pass for best of both worlds

bash
# ✅ Fast seek + precise cut
ffmpeg -ss 00:01:20.000 -i input.mp4 \
  -ss 00:00:03.456 -to 00:01:25.678 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a aac -b:a 192k \
  output.mp4

# -ss BEFORE -i: Fast seek to keyframe (no decode)
# -ss AFTER -i: Precise trim (only decode needed portion)

Performance comparison:

MethodTime (1-hour video)AccuracyQuality
Stream copy (arbitrary)2s❌ Broken❌ Artifacts
Stream copy (keyframe)2s±2s✅ Perfect
Re-encode (simple)15min✅ Frame⚠️ Quality loss
Two-pass (optimal)3min✅ Frame✅ Perfect

Timeline context:

  • 2010: FFmpeg required full re-encoding for cuts
  • 2015: -c copy added for stream copying
  • 2020: Two-pass cutting became best practice
  • 2024: Hardware acceleration (NVENC) makes re-encoding viable

Anti-Pattern 2: Re-encoding Unnecessarily

Novice thinking: "Apply all edits in one FFmpeg command"

Problem: Multiple re-encodings cause cumulative quality loss.

Wrong approach:

bash
# ❌ Re-encode for each operation (quality degradation)
# Operation 1: Trim
ffmpeg -i input.mp4 -ss 00:01:00 -to 00:05:00 \
  -c:v libx264 -crf 23 temp1.mp4

# Operation 2: Add audio
ffmpeg -i temp1.mp4 -i audio.mp3 -c:v libx264 -crf 23 \
  -map 0:v -map 1:a temp2.mp4

# Operation 3: Add subtitles
ffmpeg -i temp2.mp4 -vf subtitles=subs.srt \
  -c:v libx264 -crf 23 output.mp4

# Result: 3x re-encoding = significant quality loss

Why wrong:

  • Each re-encode is lossy (even with high CRF)
  • Cumulative quality loss (generation loss)
  • 3x encoding time
  • Wasted disk I/O

Correct approach 1: Chain operations in single command

bash
# ✅ Single-pass encoding with all operations
ffmpeg -ss 00:01:00 -i input.mp4 -i audio.mp3 \
  -to 00:04:00 \
  -vf "subtitles=subs.srt" \
  -map 0:v -map 1:a \
  -c:v libx264 -crf 18 -preset medium \
  -c:a aac -b:a 192k \
  output.mp4

# Single re-encode, all operations applied at once

Correct approach 2: Use stream copy when possible

bash
# ✅ Lossless operations with stream copy
# Trim (stream copy)
ffmpeg -i input.mp4 -ss 00:01:00 -to 00:05:00 -c copy temp.mp4

# Add audio (stream copy video, encode audio)
ffmpeg -i temp.mp4 -i audio.mp3 \
  -map 0:v -map 1:a \
  -c:v copy -c:a aac -b:a 192k \
  temp2.mp4

# Burn subtitles (must re-encode video)
ffmpeg -i temp2.mp4 -vf subtitles=subs.srt \
  -c:v libx264 -crf 18 -preset medium \
  -c:a copy \
  output.mp4

# Only 1 video re-encode (for subtitles)

Quality comparison:

MethodEncoding PassesQuality (VMAF)Time
3x re-encode (CRF 23)382/10045min
Single pass (CRF 23)191/10015min
Stream copy + 1 encode195/10018min
All stream copy0100/10030s

Anti-Pattern 3: Ignoring Color Space Conversions

Novice thinking: "Just concatenate videos together"

Problem: Color shifts, mismatched brightness, broken playback.

Wrong approach:

bash
# ❌ Concatenate videos with different color spaces
# clip1.mp4: BT.709 (HD), yuv420p
# clip2.mp4: BT.601 (SD), yuvj420p (full range)
# clip3.mp4: BT.2020 (HDR), yuv420p10le

# Create concat list
echo "file 'clip1.mp4'" > list.txt
echo "file 'clip2.mp4'" >> list.txt
echo "file 'clip3.mp4'" >> list.txt

# Concatenate without color normalization
ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4

# Result: Color shifts between clips, broken HDR metadata

Why wrong:

  • Different color spaces (BT.601 vs BT.709 vs BT.2020)
  • Different pixel formats (yuv420p vs yuvj420p)
  • Different color ranges (limited vs full)
  • Metadata conflicts

Correct approach:

bash
# ✅ Normalize color space before concatenation

# Step 1: Analyze color space of each clip
ffprobe -v error -select_streams v:0 \
  -show_entries stream=color_space,color_transfer,color_primaries,pix_fmt \
  -of default=noprint_wrappers=1 clip1.mp4

# Step 2: Normalize all clips to common color space
# Target: BT.709 (HD), yuv420p, limited range

# Normalize clip1 (already BT.709)
ffmpeg -i clip1.mp4 -c copy clip1_normalized.mp4

# Normalize clip2 (BT.601 SD → BT.709 HD)
ffmpeg -i clip2.mp4 \
  -vf "scale=in_range=full:out_range=limited,colorspace=bt709:iall=bt601:fast=1" \
  -color_primaries bt709 \
  -color_trc bt709 \
  -colorspace bt709 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a copy \
  clip2_normalized.mp4

# Normalize clip3 (BT.2020 HDR → BT.709 SDR)
ffmpeg -i clip3.mp4 \
  -vf "zscale=t=linear:npl=100,format=gbrpf32le,zscale=p=bt709,tonemap=hable:desat=0,zscale=t=bt709:m=bt709:r=limited,format=yuv420p" \
  -color_primaries bt709 \
  -color_trc bt709 \
  -colorspace bt709 \
  -c:v libx264 -crf 18 -preset medium \
  -c:a copy \
  clip3_normalized.mp4

# Step 3: Concatenate normalized clips
echo "file 'clip1_normalized.mp4'" > list.txt
echo "file 'clip2_normalized.mp4'" >> list.txt
echo "file 'clip3_normalized.mp4'" >> list.txt

ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4

Color space guide:

StandardColor SpaceTransferPrimariesUse Case
BT.601SDbt470bgbt470bgOld SD content
BT.709HDbt709bt709Modern HD/FHD
BT.2020UHD/HDRsmpte2084bt20204K HDR
sRGBWebiec61966-2-1bt709Web delivery

Show full SKILL.md (310 more words)Show less
Anti-Pattern 4: Poor Audio Sync

Novice thinking: "Video and audio are separate, just overlay them"

Problem: Lip sync issues, audio drift, broken playback.

Wrong approach:

bash
# ❌ Replace audio without sync consideration
ffmpeg -i video.mp4 -i audio.mp3 \
  -map 0:v -map 1:a \
  -c:v copy -c:a copy \
  output.mp4

# Problems:
# - Audio duration ≠ video duration
# - No audio stretching/compression
# - Drift over time

Why wrong:

  • Audio and video have different durations
  • No timebase synchronization
  • No drift correction
  • Ignores original audio sync

Correct approach 1: Stretch/compress audio to match video

bash
# ✅ Adjust audio speed to match video duration

# Get durations
VIDEO_DUR=$(ffprobe -v error -show_entries format=duration \
  -of default=noprint_wrappers=1:nokey=1 video.mp4)
AUDIO_DUR=$(ffprobe -v error -show_entries format=duration \
  -of default=noprint_wrappers=1:nokey=1 audio.mp3)

# Calculate speed ratio
RATIO=$(echo "$VIDEO_DUR / $AUDIO_DUR" | bc -l)

# Stretch audio to match video (with pitch correction)
ffmpeg -i video.mp4 -i audio.mp3 \
  -filter_complex "[1:a]atempo=${RATIO}[a]" \
  -map 0:v -map "[a]" \
  -c:v copy -c:a aac -b:a 192k \
  output.mp4

Correct approach 2: Precise offset and trim

bash
# ✅ Sync audio with offset and trim

# Audio starts 0.5s late, trim to match video
ffmpeg -i video.mp4 -itsoffset 0.5 -i audio.mp3 \
  -map 0:v -map 1:a \
  -shortest \
  -c:v copy -c:a aac -b:a 192k \
  output.mp4

# -itsoffset: Delay audio by 0.5s
# -shortest: Trim to shortest stream

Correct approach 3: Mix multiple audio tracks with sync

bash
# ✅ Mix dialogue, music, effects with precise timing

ffmpeg -i video.mp4 -i dialogue.wav -i music.mp3 -i sfx.wav \
  -filter_complex "
    [1:a]adelay=0|0[dlg];
    [2:a]volume=0.3,adelay=500|500[mus];
    [3:a]adelay=1200|1200[sfx];
    [dlg][mus][sfx]amix=inputs=3:duration=first[a]
  " \
  -map 0:v -map "[a]" \
  -c:v copy -c:a aac -b:a 256k \
  output.mp4

# adelay: Precise millisecond timing
# amix: Mix multiple audio streams
# volume: Normalize levels

Audio sync checklist:

□ Verify video and audio durations match
□ Use -shortest to prevent excess audio
□ Apply adelay for precise timing offsets
□ Use atempo for speed adjustment (maintains pitch)
□ Set audio bitrate appropriately (128k-256k)
□ Test lip sync at beginning, middle, end

Anti-Pattern 5: Wrong Codec/Bitrate for Platform

Novice thinking: "One export settings for everything"

Problem: Wasted bandwidth, poor quality, rejected uploads, compatibility issues.

Wrong approach:

bash
# ❌ Export everything at 4K 50 Mbps
ffmpeg -i input.mp4 \
  -c:v libx264 -b:v 50M -s 3840x2160 \
  -c:a aac -b:a 320k \
  output.mp4

# For Instagram story: 2 GB file, rejected (max 100 MB)
# For YouTube: Could use 10 Mbps and look identical
# For Twitter: Exceeds bitrate limits

Why wrong:

  • Platform-specific size/bitrate limits
  • Over-encoding wastes bandwidth
  • Wrong resolution for platform
  • Incompatible codecs

Correct approach: Platform-optimized exports

YouTube (recommended settings):

bash
# ✅ YouTube 1080p upload
ffmpeg -i input.mp4 \
  -c:v libx264 -preset slow -crf 18 \
  -s 1920x1080 -r 30 \
  -pix_fmt yuv420p \
  -color_primaries bt709 -color_trc bt709 -colorspace bt709 \
  -movflags +faststart \
  -c:a aac -b:a 192k -ar 48000 \
  youtube_1080p.mp4

# YouTube 4K upload
ffmpeg -i input.mp4 \
  -c:v libx264 -preset slow -crf 18 \
  -s 3840x2160 -r 60 \
  -pix_fmt yuv420p \
  -movflags +faststart \
  -c:a aac -b:a 256k -ar 48000 \
  youtube_4k.mp4

Instagram (Stories, Reels, Feed):

bash
# ✅ Instagram Story (9:16, max 100 MB, 15s)
ffmpeg -i input.mp4 \
  -c:v libx264 -preset medium -crf 23 \
  -s 1080x1920 -r 30 -t 15 \
  -pix_fmt yuv420p \
  -movflags +faststart \
  -c:a aac -b:a 128k \
  instagram_story.mp4

# ✅ Instagram Reel (9:16, max 90s)
ffmpeg -i input.mp4 \
  -c:v libx264 -preset medium -crf 23 \
  -s 1080x1920 -r 30 -t 90 \
  -pix_fmt yuv420p \
  -movflags +faststart \
  -c:a aac -b:a 128k \
  instagram_reel.mp4

# ✅ Instagram Feed (1:1 or 4:5)
ffmpeg -i input.mp4 \
  -c:v libx264 -preset medium -crf 23 \
  -s 1080x1080 -r 30 \
  -pix_fmt yuv420p \
  -movflags +faststart \
  -c:a aac -b:a 128k \
  instagram_feed.mp4

Twitter/X:

bash
# ✅ Twitter video (max 512 MB, 2:20)
ffmpeg -i input.mp4 \
  -c:v libx264 -preset medium -crf 23 \
  -s 1280x720 -r 30 -t 140 \
  -maxrate 5000k -bufsize 10000k \
  -pix_fmt yuv420p \
  -movflags +faststart \
  -c:a aac -b:a 128k \
  twitter.mp4

TikTok:

bash
# ✅ TikTok (9:16, max 287 MB, 10 min)
ffmpeg -i input.mp4 \
  -c:v libx264 -preset medium -crf 23 \
  -s 1080x1920 -r 30 -t 600 \
  -pix_fmt yuv420p \
  -movflags +faststart \
  -c:a aac -b:a 128k \
  tiktok.mp4

Web (HTML5 video):

bash
# ✅ Web optimized (fast load, broad compatibility)
ffmpeg -i input.mp4 \
  -c:v libx264 -preset medium -crf 23 \
  -s 1920x1080 -r 30 \
  -pix_fmt yuv420p \
  -profile:v baseline -level 3.0 \
  -movflags +faststart \
  -c:a aac -b:a 128k -ar 48000 \
  web.mp4

Platform specs table:

PlatformMax SizeMax DurationResolutionFPSBitrateCodec
YouTubeUnlimitedUnlimited8K60AutoH.264/VP9
Instagram Story100 MB15s1080x192030~5 MbpsH.264
Instagram Reel1 GB90s1080x192030~8 MbpsH.264
Twitter512 MB2:201920x1080605 MbpsH.264
TikTok287 MB10min1080x192030~4 MbpsH.264
LinkedIn5 GB10min1920x1080305 MbpsH.264
WebVariesVaries1920x1080302-5 MbpsH.264

Export optimization checklist:

□ Use -movflags +faststart for web (progressive download)
□ Use -pix_fmt yuv420p for broad compatibility
□ Set -r 30 for most platforms (avoid variable framerate)
□ Use -preset slow for final exports (better quality)
□ Use -preset ultrafast for drafts
□ Apply -maxrate and -bufsize for streaming
□ Test playback on target platform before bulk export

Production Checklist

□ Align cuts to keyframes (or two-pass seek)
□ Chain operations in single FFmpeg command
□ Normalize color spaces before concatenating
□ Verify audio/video sync (test at multiple points)
□ Use platform-specific export presets
□ Apply -movflags +faststart for web delivery
□ Set proper color metadata (bt709 for HD)
□ Test output file on target platform
□ Keep lossless intermediate files (ProRes, FFV1)
□ Use hardware acceleration for batch jobs (NVENC, VideoToolbox)

When to Use vs Avoid

ScenarioAppropriate?
Automated video pipeline (script → video)✅ Yes - FFmpeg automation
Batch process 100 videos✅ Yes - parallel FFmpeg jobs
Trim/cut clips programmatically✅ Yes - precise cutting
Add subtitles to videos✅ Yes - burn or soft subs
Color grade footage⚠️ Limited - basic only
Multi-cam editing❌ No - use DaVinci Resolve
Motion graphics❌ No - use After Effects
Real-time preview editing❌ No - use Premiere/Resolve

References

  • /references/ffmpeg-guide.md - Complete FFmpeg command reference
  • /references/timeline-editing.md - Timeline concepts, multi-track editing
  • /references/export-optimization.md - Platform-specific export settings

Scripts

  • scripts/video_editor.py - Cut, trim, concatenate, transitions, effects
  • scripts/batch_processor.py - Parallel batch video processing

This skill guides: Video editing | FFmpeg | Timeline editing | Transitions | Export optimization | Audio mixing | Color grading | Automated video production

© curiositech, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (scripts, references) in .claude/skills/video-processing-editing of curiositech/some_claude_skills.

  • SKILL.md
  • .claude-plugin/plugin.json
  • demo/index.html
  • references/export-optimization.md
  • references/ffmpeg-guide.md
  • references/timeline-editing.md
  • scripts/audio_mixer.py
  • scripts/batch_processor.py
  • scripts/motion_graphics.py
  • scripts/quality_assessment.py
  • scripts/thumbnail_generator.py
  • scripts/timelapse_creator.py
  • scripts/video_editor.py

Open the folder on GitHubat commit 6713fc7

Compare with similar skills

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Works with

Questions about Video Processing Editing

What does Video Processing Editing do?

FFmpeg automation for cutting, trimming, concatenating videos. Video Processing Editing is an agent skill from curiositech/some_claude_skills. FFmpeg automation for cutting, trimming, concatenating videos.

When should I use Video Processing Editing?

Video Processing Editing fits situations like: videogen projects; content creation; automated video production.

How do I install Video Processing Editing in Claude Code?

Run `npx skills add curiositech/some_claude_skills --skill video-processing-editing -a claude-code`. Or copy the skill folder (.claude/skills/video-processing-editing in curiositech/some_claude_skills) into .claude/skills/video-processing-editing in your project. Claude Code loads it when a task matches its description.

How do I install Video Processing Editing in Codex?

Run `npx skills add curiositech/some_claude_skills --skill video-processing-editing -a codex`. Or copy the skill folder (.claude/skills/video-processing-editing in curiositech/some_claude_skills) into .agents/skills/video-processing-editing in your project. Codex loads it when a task matches its description.

Can I use Video Processing Editing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add curiositech/some_claude_skills --skill video-processing-editing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/video-processing-editing, .gemini/skills/video-processing-editing, .github/skills/video-processing-editing and .opencode/skills/video-processing-editing in your project.

What does Video Processing Editing need to run?

Going by SKILL.md and its folder, Video Processing Editing needs Python for the scripts in its folder and the command-line tools its instructions call (ffmpeg and ffprobe). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(ffmpeg*,ffprobe*,python*).

Does Video Processing Editing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Video Processing Editing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Video Processing Editing use?

Video Processing Editing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Video Processing Editing use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.4k tokens, read only when the agent opens those files.

What are the alternatives to Video Processing Editing?

Skills that share tags, products or a category with Video Processing Editing: Shorts Video Maker (uxjoseph/content-marketing-team, 107 stars), Showcase Video (rehan-remade/universal-modder, 5.3k stars), Video Download (calesthio/OpenMontage, 65k stars) and Video Transcript Downloader (sundial-org/awesome-openclaw-skills, 663 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Video Processing Editing?

curiositech (a GitHub organization) maintains it in curiositech/some_claude_skills, which has 243 GitHub stars. The repository holds 109 skills in this directory. The repository was last updated on September 6, 2026.

Source: curiositech/some_claude_skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.