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
VIDEO_URL="<YOUTUBE_URL>"
WORK_DIR=$(mktemp -d /tmp/yt-analysis-XXXXXX)
mkdir -p "$WORK_DIR/frames"
Step 2: Get Video Metadata
yt-dlp --print title --print duration --print id "$VIDEO_URL" 2>/dev/null
This returns three lines: title, duration in seconds, video ID. Store these for later use.
Step 3: Extract Transcript
IMPORTANT: Direct subtitle download via --write-sub frequently hits YouTube rate limits (HTTP 429).
Use the reliable two-step method below instead.
Step 3a: Get subtitle URL from video JSON
yt-dlp --dump-json "$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)
" > "$WORK_DIR/sub_url.txt"
Step 3b: Download and parse transcript
curl -s "$(cat "$WORK_DIR/sub_url.txt")" -o "$WORK_DIR/transcript.json3"
Verify it is valid JSON (not an HTML error page):
head -c 20 "$WORK_DIR/transcript.json3"
# Should start with { — if it starts with <html, retry after 10s sleep
Step 3c: Parse json3 into readable timestamped segments
python3 -c "
import json
with open('$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 $WORK_DIR/transcript.txt to get the full transcript with timestamps.
Fallback: No transcript available
If no subtitles exist at all, inform the user and proceed with visual-only analysis.
Step 4: Download Video and Extract Frames
Step 4a: Download video (720p is sufficient for frame analysis)
yt-dlp -f "bestvideo[height<=720]+bestaudio/best[height<=720]" \
-o "$WORK_DIR/video.mp4" "$VIDEO_URL"
Step 4b: Get exact duration
DURATION=$(ffprobe -v quiet -show_entries format=duration -of csv=p=0 "$WORK_DIR/video.mp4")
Step 4c: Extract frames using adaptive interval strategy
Choose interval based on video length:
| Duration | Interval | Approx. Frames | Rationale |
|---|
| < 5 min | 10s | 20-30 | Dense enough for detailed analysis |
| 5-20 min | 20s | 15-60 | Good balance of coverage vs. volume |
| 20-60 min | 30-45s | 30-120 | Focus on key moments |
| > 60 min | 60s | 60-120+ | Ask user if they want to focus on specific sections |
# Example for a 5-20 minute video (interval=20):
ffmpeg -i "$WORK_DIR/video.mp4" -vf "fps=1/20" -q:v 3 "$WORK_DIR/frames/frame_%04d.jpg" 2>&1
For scene-change-detection (software HowTos, UI demos):
ffmpeg -i "$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:
- Calculate the frame's timestamp in seconds
- Find the transcript segment(s) covering that timestamp
- 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)
# [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
# [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:
# [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]
Error Handling & Edge Cases
| Problem | Solution |
|---|
| HTTP 429 on subtitle download | Use --dump-json method (Step 3a). If curl also gets blocked, wait 10-15 seconds and retry with different User-Agent |
| No subtitles available at all | Proceed with visual-only analysis, inform user |
| Original audio language not in auto-captions list | The original language is the source — auto-captions are translations. Remove &tlang=XX from any auto-caption URL to get the original |
transcript.json3 contains HTML instead of JSON | YouTube returned an error page. Wait 10s, retry with: curl -s --user-agent "Mozilla/5.0 (Windows NT 10.0; Win64; x64)" "$URL" |
| Video > 60 min | Ask user if they want to focus on specific time ranges or chapters |
| Poor video quality / blurry frames | Extract more frames at tighter intervals to compensate |
| Video is age-restricted or private | Inform user that the video cannot be accessed. Suggest using --cookies-from-browser if they have access |
| yt-dlp download fails | Try alternative format: -f "best[height<=720]" without separate audio+video streams |
Cleanup
After analysis is complete, remove temporary files:
rm -rf "$WORK_DIR"
Tips for Best Results
- Software HowTos: Use scene-change detection — UI transitions create clear visual breaks
- Physical HowTos: Use tighter frame intervals (10-15s) — movements are subtler
- Read the transcript first: Identify "interesting timestamps" before extracting frames. Look for phrases like "as you can see here", "let me show you", "on the screen" — these signal important visual moments
- Context-aware frame analysis: When analyzing a frame, always provide the transcript context. The speaker often explains what's about to be shown
- Batch frame reading: Read frames in batches of 8-10 to maintain context across sequential frames and detect visual changes
- Always extract both channels in parallel: Start the video download while processing the transcript to save time
