youtube-knowledge-extractor

What this skill does

# YouTube Video Analyzer — Multimodal

This skill performs deep analysis of YouTube videos through **both information channels**:
- **Audio channel**: Transcript with timestamps (what is SAID)
- **Visual channel**: Frame extraction + image analysis (what is SHOWN)

Most YouTube skills only extract transcripts. This skill closes the gap by **synchronizing visual frames with spoken content**, enabling accurate step-by-step guides where "click the blue button" is matched with the actual screenshot showing which button.

## Workflow Overview

```
YouTube URL
    |
    +---> 1. Get metadata (title, duration, video ID)
    |
    +---> 2. Extract transcript (yt-dlp --dump-json + curl)
    |         -> Timestamped segments
    |
    +---> 3. Extract frames (yt-dlp + ffmpeg)
    |         -> Keyframes at strategic intervals
    |
    +---> 4. Synchronize frames <-> transcript
    |         -> Match frames to spoken content by timestamp
    |
    +---> 5. Multimodal analysis
              -> Read each frame image, combine with transcript
              -> Generate structured output
```

## Step 1: Setup Working Directory

```bash
VIDEO_URL="<YOUTUBE_URL>"
WORK_DIR=$(mktemp -d /tmp/yt-analysis-XXXXXX)
mkdir -p "$WORK_DIR/frames"
```

## Step 2: Get Video Metadata

```bash
yt-dlp --print title --print duration --print id "$VIDEO_URL" 2>/dev/null
```

This returns three lines: title, duration in seconds, video ID. Store these for later use.

## Step 3: Extract Transcript

**IMPORTANT: Direct subtitle download via `--write-sub` frequently hits YouTube rate limits (HTTP 429).
Use the reliable two-step method below instead.**

### Step 3a: Get subtitle URL from video JSON

```bash
yt-dlp --dump-json "$VIDEO_URL" 2>/dev/null | python3 -c "
import json, sys
data = json.load(sys.stdin)
auto = data.get('automatic_captions', {})
subs = data.get('subtitles', {})

# Priority: manual subs > auto subs. Prefer user's language, fallback chain.
for source in [subs, auto]:
    for lang in ['en', 'de', 'en-orig', 'fr', 'es']:
        if lang in source:
            for fmt in source[lang]:
                if fmt.get('ext') == 'json3':
                    print(fmt['url'])
                    sys.exit(0)

# Fallback: take first available auto-caption, get json3 URL
for lang in sorted(auto.keys()):
    for fmt in auto[lang]:
        if fmt.get('ext') == 'json3':
            url = fmt['url']
            # Remove translation param to get original language
            import re
            url = re.sub(r'&tlang=[^&]+', '', url)
            print(url)
            sys.exit(0)

print('NO_SUBS', file=sys.stderr)
sys.exit(1)
" > "$WORK_DIR/sub_url.txt"
```

### Step 3b: Download and parse transcript

```bash
curl -s "$(cat "$WORK_DIR/sub_url.txt")" -o "$WORK_DIR/transcript.json3"
```

Verify it is valid JSON (not an HTML error page):

```bash
head -c 20 "$WORK_DIR/transcript.json3"
# Should start with { — if it starts with <html, retry after 10s sleep
```

### Step 3c: Parse json3 into readable timestamped segments

```bash
python3 -c "
import json

with open('$WORK_DIR/transcript.json3') as f:
    data = json.load(f)

for event in data.get('events', []):
    segs = event.get('segs', [])
    if not segs:
        continue
    start_ms = event.get('tStartMs', 0)
    duration_ms = event.get('dDurationMs', 0)
    text = ''.join(s.get('utf8', '') for s in segs).strip()
    if not text or text == '\n':
        continue
    s = start_ms / 1000
    e = (start_ms + duration_ms) / 1000
    print(f'[{int(s//60):02d}:{int(s%60):02d} - {int(e//60):02d}:{int(e%60):02d}] {text}')
" > "$WORK_DIR/transcript.txt"
```

Read `$WORK_DIR/transcript.txt` to get the full transcript with timestamps.

### Fallback: No transcript available

If no subtitles exist at all, inform the user and proceed with visual-only analysis.

## Step 4: Download Video and Extract Frames

### Step 4a: Download video (720p is sufficient for frame analysis)

```bash
yt-dlp -f "bestvideo[height<=720]+bestaudio/best[height<=720]" \
       -o "$WORK_DIR/video.mp4" "$VIDEO_URL"
```

### Step 4b: Get exact duration

```bash
DURATION=$(ffprobe -v quiet -show_entries format=duration -of csv=p=0 "$WORK_DIR/video.mp4")
```

### Step 4c: Extract frames using adaptive interval strategy

Choose interval based on video length:

| Duration | Interval | Approx. Frames | Rationale |
|----------|----------|-----------------|-----------|
| < 5 min | 10s | 20-30 | Dense enough for detailed analysis |
| 5-20 min | 20s | 15-60 | Good balance of coverage vs. volume |
| 20-60 min | 30-45s | 30-120 | Focus on key moments |
| > 60 min | 60s | 60-120+ | Ask user if they want to focus on specific sections |

```bash
# Example for a 5-20 minute video (interval=20):
ffmpeg -i "$WORK_DIR/video.mp4" -vf "fps=1/20" -q:v 3 "$WORK_DIR/frames/frame_%04d.jpg" 2>&1
```

**For scene-change-detection (software HowTos, UI demos):**

```bash
ffmpeg -i "$WORK_DIR/video.mp4" \
       -vf "select='gt(scene,0.3)',showinfo" \
       -vsync vfr -q:v 3 "$WORK_DIR/frames/scene_%04d.jpg" 2>&1
```

### Step 4d: Calculate timestamps for each frame

For fixed-interval extraction: frame N has timestamp `(N-1) * interval` seconds.

```
frame_0001.jpg -> 0:00
frame_0002.jpg -> 0:20
frame_0003.jpg -> 0:40
...
```

## Step 5: Synchronize Frames with Transcript

For each extracted frame:
1. Calculate the frame's timestamp in seconds
2. Find the transcript segment(s) covering that timestamp
3. Create a synchronized pair: `{timestamp, transcript_text, frame_path}`

This is done mentally or via a simple lookup — no external script needed.

## Step 6: Multimodal Analysis

### Step 6a: Read and analyze each frame

Use the `Read` tool (or `view` tool) to look at each frame image. For each frame, consider:

- **UI elements**: Buttons, menus, dialogs, settings panels visible
- **Text on screen**: Code, labels, error messages, URLs, terminal output
- **Diagrams/graphics**: Charts, flow diagrams, architecture drawings
- **Physical actions**: Hand positions, tool usage (for physical HowTos)
- **Changes**: What changed compared to the previous frame?

### Step 6b: Synthesize both channels

For each key moment, combine audio and visual:

```
Segment [TIMESTAMP]:
  SAID: "Click the blue button in the top right"
  SHOWN: Settings page screenshot, blue "Save" button highlighted
         in top-right corner, cursor pointing at it
  SYNTHESIS: -> On the Settings page, click the blue "Save" button
               in the top-right corner
```

### Step 6c: Identify visual-only information

Flag moments where the visual channel provides information NOT present in audio:
- Specific button names, menu paths, exact UI locations
- Code that is shown but not read aloud
- Error messages visible on screen
- Before/after comparisons

## Output Formats

Generate the appropriate format based on the user's request:

### Format A: Step-by-Step Guide (most common)

```markdown
# [Video Title] — Guide

## Step 1: [Action] (00:15)
[Description based on transcript + frame analysis]
> Visual: [What the screen/image shows at this point]

## Step 2: [Action] (00:42)
[...]
```

### Format B: Comprehensive Summary with Visual Anchors

```markdown
# [Video Title] — Summary

## Overview
[2-3 sentence summary of the entire video]

## Key Sections

### [Section Name] (00:00 - 02:30)
[Summary of this section]
- Key visual: [Description of what's shown]
- Key quote: "[Important spoken content]"

### [Section Name] (02:30 - 05:00)
[...]

## Key Takeaways
- [Takeaway 1]
- [Takeaway 2]
```

### Format C: Technical Detail Analysis

Separate analysis of both channels plus discrepancy detection:

```markdown
# [Video Title] — Technical Analysis

## Audio Channel Analysis
[What was said, key points, structure]

## Visual Channel Analysis
[What was shown, UI flows, code, diagrams]

## Channel Synchronization
[Where audio and visual complement each other]

## Visual-Only Information
[Important details only visible in frames, not mentioned in speech]
`