mediar-ai/video-edit

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)