video-edit

name	video-edit
description	Edit long videos into short, story-driven clips using transcript analysis and ffmpeg. Use when user asks to "edit video", "cut video", "shorten video", "make highlight reel", "trim video", "create clip", or wants to extract the best moments from a recording. Works with screen recordings, demos, meetings, and any video with speech.

Video Edit Skill

Edit long videos into concise, story-driven clips by analyzing the transcript, selecting key moments, and assembling them with ffmpeg.

Workflow

Phase 1: Discover & Transcribe

1. Find the video — locate file, get duration/format with ffprobe
2. Extract audio — convert to 16kHz mono WAV for transcription:

   ffmpeg -y -i INPUT -vn -acodec pcm_s16le -ar 16000 -ac 1 /tmp/audio.wav
   

3. Transcribe with Whisper — use whisper Python library (install via pip3 install openai-whisper if needed):

   import whisper, json
   model = whisper.load_model("base")
   result = model.transcribe("/tmp/audio.wav", language="en", word_timestamps=True)
   # Save full result for reference
   with open("/tmp/transcript.json", "w") as f:
       json.dump(result, f)
   # Print timestamped segments
   for seg in result["segments"]:
       print(f'[{seg["start"]:.1f}-{seg["end"]:.1f}] {seg["text"].strip()}')
   

Phase 2: Analyze & Select Moments

Read the full transcript and identify the story arc. Look for:

• Setup: Where the task/prompt is clearly stated
• Progress beats: AI working, intermediate results appearing
• Positive reactions: "wow", "that's great", "it worked", "cool", "upgraded", excitement
• Key insights: Moments where approach changes or something clever happens
• Resolution: Final confirmation that it worked, "thank you", wrap-up

Selection rules:

• Skip duplicate/repeated prompts (e.g., if there was a reset and the same prompt was given twice, use only the second clean attempt)
• Skip troubleshooting tangents, debugging, off-topic conversations, bathroom breaks, silence
• Skip filler ("uh", "hmm", long pauses) — but these are fine within a selected segment
• Keep segments long enough for context (15-75s each) — don't micro-cut
• Aim for 10-20% of original duration as a starting point
• Preserve the chronological order — never rearrange

Present the plan to the user before cutting — list each segment with timestamp range and what it captures.

Phase 3: Extract & Assemble

1. Extract each segment as a separate file with re-encoding for clean cuts:

   ffmpeg -y -ss START -t DURATION -i INPUT \
     -c:v libx264 -preset fast -crf 23 \
     -c:a aac -b:a 128k \
     /tmp/segments/segNN.mp4
   

2. Create concat file and merge:

   for f in /tmp/segments/seg*.mp4; do
     echo "file '$f'" >> /tmp/segments/filelist.txt
   done
   ffmpeg -y -f concat -safe 0 -i /tmp/segments/filelist.txt \
     -c:v libx264 -preset fast -crf 23 \
     -c:a aac -b:a 128k \
     OUTPUT.mp4
   

3. Verify — check duration, file size, playback.

Phase 4: Save

Save to the same directory as the source with _edited suffix, or as specified by user.

Common Mistakes & Fixes

Mistake	Fix
Including the first attempt when there was a reset/retry	Only include the clean second attempt — watch for repeated prompts
Cutting segments too short (<10s)	Keep 15-75s per segment for natural flow
Including long silences or "uh/hmm" segments	Skip segments that are mostly filler, but filler within a good segment is fine
Using -c copy for segment extraction	Always re-encode (-c:v libx264) — copy mode causes keyframe alignment issues and glitchy cuts
Forgetting -safe 0 in concat	Required when using absolute paths in the file list
Rearranging chronological order	Never do this — the story must flow naturally in time
Over-compressing (too few segments)	The edit should still tell the complete story — don't skip important transitions
Including debugging/troubleshooting tangents	Skip unless the debugging itself is the story

What Makes a Good Edit

• Tells a story: setup → progress → "wow it worked" → done
• Highlights positive outcomes: reactions of surprise, satisfaction, confirmation
• Shows the AI being smart: clever approaches, autonomous decisions, learning
• No dead time: every second earns its place
• Natural transitions: segments should feel like they flow, even with jumps
• User can follow along: enough context in each segment to understand what's happening

Dependencies

• ffmpeg / ffprobe (install via brew install ffmpeg)
• whisper Python library (install via pip3 install openai-whisper)
• Sufficient disk space for extracted audio + segments (rough guide: 2x source file size)