| name | video-editing |
| description | Automated video editing pipeline for CC's personal brand content. Use whenever CC drops raw footage and needs it edited, captioned, graded, and sent for review. The primary tool is video_editor.py which handles the full 8-step pipeline. |
| triggers | ["video","edit","raw footage","silence","filler","caption","grade","master","review","content day","batch"] |
| tier | standard |
| dependencies | ["content-engine","elite-video-production"] |
| canon_references | ["sutherland-signalling","dunford-positioning"] |
| canon_source | brain/MARKETING_CANON.md |
| universal | true |
SKILL: Video Editing & Production
Primary tool: scripts/video_editor.py
Supports: scripts/content_pipeline.py (captions, transcription)
When to Activate
- CC says "edit this", "make this a post", or drops raw video files
- Content Day batch processing (7 videos at once)
- Any request involving silence removal, filler cutting, captioning, or color grading
- Platform-specific video export requests
Primary Tool: video_editor.py
8-Step Pipeline (edit command)
Step 1: Silence detection (FFmpeg silencedetect, threshold -30dB, min 0.4s)
Step 2: Filler word detection (Whisper word-level timestamps)
Step 3: Apply cuts (FFmpeg concat demuxer, keep segments only)
Step 4: Audio mastering (Gate→Highpass→Lowpass→Compand→Loudnorm -14 LUFS)
Step 5: Color grading (Pillar-auto-mapped: teal_orange, editorial, warm, clean)
Step 6: Karaoke captions (Word-by-word highlight via content_pipeline.py ASS format)
Step 7: Background music (Auto-ducked, mood-matched to pillar from media/music/)
Step 8: Telegram review (sendVideo to CC, awaits 'approve' or feedback)
CLI Commands
python scripts/video_editor.py edit media/raw/piece_1.mp4 --pillar ai_oracle
python scripts/video_editor.py clean media/raw/piece_1.mp4
python scripts/video_editor.py analyze media/raw/piece_1.mp4
python scripts/video_editor.py export media/exports/piece_1_final.mp4
python scripts/video_editor.py review media/exports/piece_1_final.mp4
python scripts/video_editor.py batch media/raw/ --manifest data/content_day/2026-04-27_template.json
Skip Flags (for partial runs)
--skip-music
--skip-captions
--skip-review
--skip-grade
--skip-master
--grade warm
Color Grade Presets
| Preset | Pillar Default | Look |
|---|
teal_orange | AI Oracle | Cinematic teal shadows, warm skin tones |
editorial | CEO Log | Desaturated, high contrast, professional |
warm | The Becoming, The Journey | Warm orange tones, gentle vignette |
clean | (manual) | Neutral with micro-contrast, minimal processing |
Audio Mastering Chain
Applied automatically in step 4:
- Gate (threshold 0.01) — kills breath noise between words
- Highpass (100Hz) — removes rumble, HVAC, handling noise
- Lowpass (10kHz) — tames harshness
- Compand (3:1 ratio, -7dB ceiling) — consistent loudness
- Loudnorm (-14 LUFS, -1.0 dBTP) — platform standard
Platform Export Specs
| Platform | Max Duration | Resolution |
|---|
| Instagram Reels | 90s | 1080x1920 |
| TikTok | 180s | 1080x1920 |
| YouTube Shorts | 60s | 1080x1920 |
| LinkedIn | 600s | 1080x1920 |
| Facebook | 240s | 1080x1920 |
| X/Twitter | 140s | 1080x1920 |
File Structure
media/
raw/ # CC drops raw footage here
exports/ # Edited videos output here
music/
intense/ # AI Oracle pillar tracks
warm/ # The Becoming pillar tracks
emotional/ # The Journey pillar tracks
confident/ # CEO Log pillar tracks
Dependencies
| Package | Purpose | Status |
|---|
openai-whisper | Transcription + filler detection | Installed |
auto-editor | Silence removal (backup method) | Installed |
pydub | Audio analysis | Installed |
ffmpeg | All video/audio processing | Installed |
Integration Points
- content_pipeline.py — Called internally for karaoke caption generation
- batch_content_day.py — Schedules edited videos via Zernio API
- notify.py — Telegram review notifications
- elite-video-production SKILL — Reference spec for advanced techniques (zoom punches, SFX, B-roll)
Content Day Workflow (End-to-End)
1. CC films 7 pieces → drops into media/raw/
2. python scripts/video_editor.py batch media/raw/ --manifest data/content_day/YYYY-MM-DD_template.json
3. Each video: silence cut → filler cut → master → grade → caption → music → Telegram
4. CC reviews each on phone, replies 'approve' or feedback
5. python scripts/batch_content_day.py schedule --manifest data/content_day/YYYY-MM-DD_template.json
6. Videos go live across 8 platforms over the next 7 days
Key GitHub Repos (Reference)
Taste Profile Capture (Step 8 — Post-Review Loop)
After scripts/video_editor.py review sends the edit to Telegram and CC responds:
| CC's response | Where to log | What to propagate to brain/video-style/taste_profile.md |
|---|
| "approve" | brain/video-style/approved_edits/YYYY-MM-DD_<slug>.md — note what landed (hook style, pacing, B-roll choices, grade) | Nothing direct, but this counts toward a 3-confirmation promotion of a default. |
| "approve but [change]" | approved_edits/YYYY-MM-DD_<slug>.md AND propagate change | Add dated bullet under the relevant section, cite the slug. |
| "reject because [reason]" | brain/video-style/rejected_edits/YYYY-MM-DD_<slug>.md — paste CC's verbatim feedback | Add dated bullet under the relevant section, cite the slug. Update last_updated:. |
Read brain/video-style/taste_profile.md FIRST on the next edit. Full system spec in brain/video-style/README.md and skills/elite-video-production/SKILL.md Section 16.
Obsidian Links
- [[skills/elite-video-production/SKILL]] | [[skills/content-engine/SKILL]] | [[brain/CAPABILITIES]]
- [[brain/video-style/README]] | [[brain/video-style/taste_profile]]
