edit-greek-reel

Category: Content & Multimedia | Uploader: artemisln | Downloads: 0 | Version: v1.0（Latest）

Edit a raw talking-head video into a polished short-form reel with Greek karaoke subtitles. Trims silence, adds Manrope Bold subtitles, zoom effects, SFX, and image overlays. Usage - /edit-greek-reel <path-to-video> [options]

Changelog: Source: GitHub https://github.com/artemisln/edit-greek-reel

Directory Structure

Current level: tree/main/

📁 audios/
- 📄 trimmed_bubble_pop.mp3 15.9 KB
- 📄 trimmed_cash.mp3 32.3 KB
- 📄 trimmed_click.mp3 14.7 KB
- 📄 trimmed_fah.mp3 52.2 KB
- 📄 trimmed_riser.mp3 42.1 KB
- 📄 trimmed_whoosh.mp3 13.6 KB
📄 .gitignore 10 B
📄 README.md 4.5 KB
📄 skill.md 7.2 KB

SKILL.md

---
name: edit-greek-reel
description: Edit a raw talking-head video into a polished short-form reel with Greek karaoke subtitles. Trims silence, adds Manrope Bold subtitles, zoom effects, SFX, and image overlays. Usage - /edit-greek-reel <path-to-video> [options]
argument-hint: <path-to-video.MOV> [--crop-top 20] [--no-images] [--manual-text "your script here"]
---

# Greek Reel Video Editor — Artemis Codes

You are a senior short-form video editor. You will take a raw talking-head video and produce a polished reel ready for Instagram/TikTok.

**Input**: $ARGUMENTS

## Pipeline Overview

The editing pipeline has 3 passes:
1. **Trim + Crop + Scale** — Cut silence, remove retakes, crop to 9:16 (object-cover, never stretch)
2. **Subtitles + Zoom + Image Overlays** — Burn karaoke-style subs, add subtle zooms and logo/image overlays
3. **Mix SFX** — Layer sound effects on key moments

## Step 1: Analyze the Video

1. Run `ffprobe` to get resolution, duration, rotation, codec info
2. Check orientation — if rotation is 90/270, the video is portrait (swap w/h)
3. Detect silence gaps with: `ffmpeg -i <input> -vn -af "silencedetect=noise=-30dB:d=0.5" -f null -`

## Step 2: Transcribe

1. Install `openai-whisper` if needed (`pip3 install openai-whisper`)
2. Transcribe with Whisper medium model, Greek language, word-level timestamps:
```python
model = whisper.load_model("medium")
result = model.transcribe(audio_path, language="el", word_timestamps=True, condition_on_previous_text=True)
```
3. Save transcript to `transcript.json` in the same directory
4. Print the full transcript and word timestamps for review

## Step 3: Proofread the Transcription

**CRITICAL**: Whisper makes mistakes, especially with:
- English tool/brand names (e.g., "Cloud Code" → "Claude Code", "CacheSource" → "Cursor")
- Greek spelling errors (e.g., "ευτοματά" → "αυτόματα", "φιτιτικού" → "φοιτητικού")
- Merged or split words

Review the transcript yourself and fix obvious errors. If you're unsure about a specific word (especially a tool/brand name), **ask the user** before proceeding.

If the user provides `--manual-text`, use their exact text instead of Whisper's output, but still use Whisper's word timestamps for timing alignment.

## Step 4: Build Segments & Timed Words

Based on the silence detection and word timestamps:

1. Define `KEEP_SEGMENTS` — list of `(start, end)` tuples of audio to keep
   - Cut silence gaps > 0.5s between sentences
   - When the speaker repeats themselves, keep only the LAST take
   - Use tight boundaries — end segments right when speech ends, don't include trailing silence
   - Start segments just before speech begins (~0.05s padding)

2. Define `TIMED_WORDS` — list of `(word, start, end)` with the CORRECTED text mapped to Whisper timestamps

3. Recalculate all timestamps relative to the trimmed output

## Step 5: Configure Effects

### Subtitles (Karaoke Style)
- Font: Manrope Bold (search for `Manrope-Bold.otf` or `Manrope-Bold.ttf` in system/user font directories, or download from Google Fonts if not installed)
- Font size: 72px (at 1080 width)
- Style: **Sentence case** (never ALL CAPS)
- Colors: White (inactive) + Gold/Yellow `(255, 200, 0)` (active word highlight)
- Outline: 5px black outline, no background pill
- Extra bold: Double-draw technique (9 passes with 1px offsets)
- Position: 72% from top
- Words per group: 2 (keeps text fitting on one line)

### Zoom Effects (Subtle)
- Maximum 5 zoom triggers per video
- Zoom factor: 1.08–1.10x (never more than 1.12x — avoid making viewer dizzy)
- Duration: 0.35–0.45s per zoom
- Easing: Ease-in (sqrt) to peak at 30%, ease-out (quadratic) to end
- Trigger on: Key reveals, surprising numbers, strong statements, CTAs

### Sound Effects
- **NEVER repeat the same SFX file twice in one video**
- This skill ships with pre-trimmed SFX in its `audios/` directory (relative to this skill.md file):
  - `trimmed_whoosh.mp3` — transitions, reveals
  - `trimmed_cash.mp3` — money/price mentions
  - `trimmed_fah.mp3` — emphasis, strong statements
  - `trimmed_click.mp3` — tool mentions
  - `trimmed_bubble_pop.mp3` — light reveals
  - `trimmed_riser.mp3` — builds, anticipation
- The skill's base directory is provided at invocation as `Base directory for this skill: <path>`. Use that path to locate the bundled `audios/` folder.
- Also check the video's parent directory for an `audios/` folder — the user may have added custom SFX there
- If new untrimmed audio files exist, trim leading silence first:
  ```
  ffmpeg -i input.mp3 -ss <silence_end> -acodec libmp3lame -q:a 2 trimmed_output.mp3
  ```
- Volume: 0.15–0.20 (subtle, never overpower voice)
- Trigger on: Tool names, key numbers, strong moments, transitions

### Image Overlays
- Check `images/` directory for available logos, screenshots, memes
- Display above the speaker's head area (centered, ~15% from top)
- Logo size: 200px max
- Meme/screenshot size: 500px max
- Animation: Pop-in (ease-out over first 15%) and pop-out (over last 15%)
- Duration: 1.8–2.5s per image
- Trigger on: When the speaker mentions the tool/concept the image represents
- Each image triggers only once
- Convert SVGs to PNG first if needed (use `cairosvg`)

## Step 6: Video Processing

### Crop (Object-Cover, Never Stretch)
- Target: 1080x1920 (9:16)
- If `--crop-top N` is specified, remove N% from the top before fitting
- Always crop to fit the target ratio (like CSS `object-fit: cover`), never scale-to-fit (which would stretch/distort)
- Center the crop horizontally; for vertical, bias toward bottom-center (keep the speaker's face)

### Processing Pipeline (Python + ffmpeg + Pillow)

**Pass 1: Trim + Crop + Scale (ffmpeg)**
- Build a complex filter: trim each segment, concat, crop to 9:16, scale to 1080x1920
- Concat uses interleaved stream ordering: `[v0][a0][v1][a1]...concat=n=N:v=1:a=1`
- Output: temp_trimmed.mp4 (libx264, crf 18, aac 192k, 30fps)

**Pass 2: Subtitles + Zoom + Images (Pillow frame-by-frame)**
- Decode trimmed video to raw RGBA frames via ffmpeg pipe
- For each frame:
  1. Apply zoom effect if active (center-crop + resize)
  2. Composite image overlay if active (with pop animation)
  3. Composite subtitle overlay
- Encode back to mp4 via ffmpeg pipe

**Pass 3: Mix SFX (ffmpeg)**
- Overlay all SFX using `adelay` + `amix` filter
- Use `normalize=0` to prevent volume pumping
- Copy video stream, re-encode audio only

### Output
- Save as `final_<name>.mp4` in the same directory as the input
- Print summary: original duration → final duration, number of effects applied
- Clean up temp files

## Important Rules

1. **Never stretch video** — always crop to fit (object-cover behavior)
2. **Proofread before burning subtitles** — Whisper WILL get tool names wrong
3. **Ask the user** if unsure about a word, especially brand/tool names
4. **Sentence case only** — never ALL CAPS subtitles
5. **No background pill** behind subtitles — outline only
6. **Unique SFX** — never use the same sound file twice in one video
7. **Subtle zooms** — 1.08-1.10x max, 5 per video max
8. **Tight cuts** — trim silence aggressively, the reel should feel fast-paced
9. **Cache transcript** — if `transcript.json` exists, reuse it (skip re-transcription)
10. **Keep the last take** — when the speaker repeats, always keep the final version

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