Highlight Scanner

This skill combines all detection methods to find viral-worthy highlights from videos. It's the core analysis component for the autocut-shorts workflow.

When to Use

•User wants to find the best moments from a video
•Identifying viral-worthy segments for short-form content
•Creating highlight reels from long videos
•Analyzing podcast, vlog, gaming, or tutorial content
•Preparing content for autocut workflow

Detection Signals

1. Transcript Analysis

•Identifies hooks and attention-grabbing phrases
•Detects story beats and important points
•Finds question/answer patterns
•Keyword matching for viral phrases

2. Laughter Detection

•Finds humorous moments
•Detects audience reactions
•Identifies funny segments

3. Sentiment/Emotion Analysis

•Positive emotions (excitement, joy)
•Surprise moments
•Negative emotions (controversy, drama)
•Emotional peaks and intensity

4. Scene Detection

•Scene changes as natural cut points
•Topic transitions
•Visual changes

Scoring System

Each highlight is scored based on:

python

virality_score = (
    transcript_score * 0.35 +
    laughter_score * 0.25 +
    sentiment_score * 0.25 +
    scene_score * 0.15
)

Score Range: 0.0 - 1.0

•0.8 - 1.0: Premium viral potential (must use)
•0.6 - 0.8: High potential (excellent clips)
•0.4 - 0.6: Good potential (consider using)
•0.2 - 0.4: Moderate potential (optional)
•0.0 - 0.2: Low potential (skip)

Available Scripts

`scripts/find_highlights.py`

Find viral-worthy highlight segments.

Usage:

bash

python skills/highlight-scanner/scripts/find_highlights.py <video_path> [options]

Options:

•--transcript-path: Path to transcript SRT/VTT file
•--scenes-path: Path to scenes JSON file (from scene-detector)
•--laughter-path: Path to laughter JSON file (from laughter-detector)
•--sentiment-path: Path to sentiment JSON file (from sentiment-analyzer)
•--num-clips: Number of clips to generate - default: 5
•--min-duration: Minimum clip duration (seconds) - default: 15
•--max-duration: Maximum clip duration (seconds) - default: 60
•--output, -o: Output JSON path (default: <video_path>_highlights.json)

Examples:

Find highlights with transcript only:

bash

python skills/highlight-scanner/scripts/find_highlights.py video.mp4 --transcript-path video.srt

Full analysis with all signals:

bash

python skills/highlight-scanner/scripts/find_highlights.py video.mp4 \
  --transcript-path video.srt \
  --scenes-path video_scenes.json \
  --laughter-path video_laughter.json \
  --sentiment-path video_sentiment.json

Find 10 clips with custom duration:

bash

python skills/highlight-scanner/scripts/find_highlights.py video.mp4 \
  --transcript-path video.srt \
  --num-clips 10 \
  --min-duration 20 \
  --max-duration 45

`scripts/analyze_viral_potential.py`

Analyze the viral potential of video segments.

Usage:

bash

python skills/highlight-scanner/scripts/analyze_viral_potential.py <video_path> [options]

Options:

•--transcript-path: Path to transcript file
•--output, -o: Output JSON path

Example:

bash

python skills/highlight-scanner/scripts/analyze_viral_potential.py video.mp4 --transcript-path video.srt

Output Format

json

{
  "video_path": "video.mp4",
  "total_segments_analyzed": 15,
  "highlights": [
    {
      "rank": 1,
      "start_time": 45.2,
      "end_time": 72.5,
      "duration": 27.3,
      "virality_score": 0.92,
      "scores": {
        "transcript": 0.95,
        "laughter": 0.80,
        "sentiment": 0.85,
        "scenes": 0.70
      },
      "text": "This is the key moment text...",
      "reasoning": "Contains hook + laughter + positive emotion",
      "suggested_clip_start": 42.0,
      "suggested_clip_end": 75.0,
      "confidence": "high"
    }
  ],
  "analysis_summary": {
    "total_duration": 120.5,
    "avg_virality_score": 0.68,
    "best_segment_start": 45.2,
    "recommended_num_clips": 5
  }
}

Scoring Weights

Default weights (customizable):

python

DEFAULT_WEIGHTS = {
    'transcript': 0.35,   # Content analysis
    'laughter': 0.25,     # Humor detection
    'sentiment': 0.25,    # Emotion analysis
    'scenes': 0.15        # Visual transitions
}

Adjust weights based on content type:

•Comedy content: Increase laughter weight
•Emotional content: Increase sentiment weight
•Educational content: Increase transcript weight
•Action content: Increase scenes weight

Viral Phrases/Keywords

High-Viral Potential Phrases

Hooks/Attention Grabbers:

•"You won't believe..."
•"This changes everything..."
•"The secret to..."
•"What nobody tells you about..."
•"I made a huge mistake..."
•"This is illegal..."

Story Beats:

•"The plot twist..."
•"And then it happened..."
•"But here's the catch..."
•"The most important part..."

Engagement:

•"Comment if you agree..."
•"Like if you've experienced this..."
•"Wait for it..."
•"Watch till the end..."

Moderate-Viral Potential

•Interesting facts
•Tips and tricks
•How-to content
•Before/after reveals

Integration with Other Skills

This skill combines inputs from:

•video-transcriber: Transcript for content analysis
•scene-detector: Scene changes for cut points
•laughter-detector: Humorous moments
•sentiment-analyzer: Emotional peaks

Output is used by:

•video-trimmer: Create clips from highlights
•autocut-shorts: Full workflow execution

Common Workflow

•User provides video file
•Transcribe with video-transcriber
•Detect scenes with scene-detector (optional)
•Detect laughter with laughter-detector (optional)
•Analyze sentiment with sentiment-analyzer (optional)
•Find highlights using this skill (combines all signals)
•Create clips from highlights with video-trimmer or autocut-shorts

Tips

•More input signals = better highlight detection
•Always provide transcript (minimum requirement)
•Scene detection helps with clean cuts
•Laughter detection improves viral potential
•Sentiment analysis identifies emotional peaks
•Adjust weights based on your content type
•Filter by score threshold for quality control
•Consider clip duration when selecting highlights

Performance

•Transcript only: ~2 seconds for 1-minute video
•Full analysis: ~10-30 seconds for 10-minute video
•Scales linearly with video duration
•Can process in real-time for live content

References

•Viral content analysis research
•Engagement metrics studies
•TikTok/YouTube algorithm insights