Tapestry-skills-for-claude-code youtube-transcript
Download YouTube video transcripts when user provides a YouTube URL or asks to download/get/fetch a transcript from YouTube. Also use when user wants to transcribe or get captions/subtitles from a YouTube video.
git clone https://github.com/michalparkola/tapestry-skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/michalparkola/tapestry-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/youtube-transcript" ~/.claude/skills/michalparkola-tapestry-skills-for-claude-code-youtube-transcript && rm -rf "$T"
youtube-transcript/SKILL.mdYouTube Transcript Downloader
This skill helps download transcripts (subtitles/captions) from YouTube videos using yt-dlp.
When to Use This Skill
Activate this skill when the user:
- Provides a YouTube URL and wants the transcript
- Asks to "download transcript from YouTube"
- Wants to "get captions" or "get subtitles" from a video
- Asks to "transcribe a YouTube video"
- Needs text content from a YouTube video
How It Works
Priority Order:
- Check if yt-dlp is installed - install if needed
- List available subtitles - see what's actually available
- Try manual subtitles first (
) - highest quality--write-sub - Fallback to auto-generated (
) - usually available--write-auto-sub - Last resort: Whisper transcription - if no subtitles exist (requires user confirmation)
- Confirm the download and show the user where the file is saved
- Optionally clean up the VTT format if the user wants plain text
Installation Check
IMPORTANT: Always check if yt-dlp is installed first:
which yt-dlp || command -v yt-dlp
If Not Installed
Attempt automatic installation based on the system:
macOS (Homebrew):
brew install yt-dlp
Linux (apt/Debian/Ubuntu):
sudo apt update && sudo apt install -y yt-dlp
Alternative (pip - works on all systems):
pip3 install yt-dlp # or python3 -m pip install yt-dlp
If installation fails: Inform the user they need to install yt-dlp manually and provide them with installation instructions from https://github.com/yt-dlp/yt-dlp#installation
Check Available Subtitles
ALWAYS do this first before attempting to download:
yt-dlp --list-subs "YOUTUBE_URL"
This shows what subtitle types are available without downloading anything. Look for:
- Manual subtitles (better quality)
- Auto-generated subtitles (usually available)
- Available languages
Download Strategy
Option 1: Manual Subtitles (Preferred)
Try this first - highest quality, human-created:
yt-dlp --write-sub --skip-download --output "OUTPUT_NAME" "YOUTUBE_URL"
Option 2: Auto-Generated Subtitles (Fallback)
If manual subtitles aren't available:
yt-dlp --write-auto-sub --skip-download --output "OUTPUT_NAME" "YOUTUBE_URL"
Both commands create a
.vtt file (WebVTT subtitle format).
Option 3: Whisper Transcription (Last Resort)
ONLY use this if both manual and auto-generated subtitles are unavailable.
Step 1: Show File Size and Ask for Confirmation
# Get audio file size estimate yt-dlp --print "%(filesize,filesize_approx)s" -f "bestaudio" "YOUTUBE_URL" # Or get duration to estimate yt-dlp --print "%(duration)s %(title)s" "YOUTUBE_URL"
IMPORTANT: Display the file size to the user and ask: "No subtitles are available. I can download the audio (approximately X MB) and transcribe it using Whisper. Would you like to proceed?"
Wait for user confirmation before continuing.
Step 2: Check for Whisper Installation
command -v whisper
If not installed, ask user: "Whisper is not installed. Install it with
pip install openai-whisper (requires ~1-3GB for models)? This is a one-time installation."
Wait for user confirmation before installing.
Install if approved:
pip3 install openai-whisper
Step 3: Download Audio Only
yt-dlp -x --audio-format mp3 --output "audio_%(id)s.%(ext)s" "YOUTUBE_URL"
Step 4: Transcribe with Whisper
# Auto-detect language (recommended) whisper audio_VIDEO_ID.mp3 --model base --output_format vtt # Or specify language if known whisper audio_VIDEO_ID.mp3 --model base --language en --output_format vtt
Model Options (stick to
base for now):
- fastest, least accurate (~1GB)tiny
- good balance (~1GB) ← USE THISbase
- better accuracy (~2GB)small
- very good (~5GB)medium
- best accuracy (~10GB)large
Step 5: Cleanup
After transcription completes, ask user: "Transcription complete! Would you like me to delete the audio file to save space?"
If yes:
rm audio_VIDEO_ID.mp3
Getting Video Information
Extract Video Title (for filename)
yt-dlp --print "%(title)s" "YOUTUBE_URL"
Use this to create meaningful filenames based on the video title. Clean the title for filesystem compatibility:
- Replace
with/- - Replace special characters that might cause issues
- Consider using sanitized version:
$(yt-dlp --print "%(title)s" "URL" | tr '/' '-' | tr ':' '-')
Post-Processing
Convert to Plain Text (Recommended)
YouTube's auto-generated VTT files contain duplicate lines because captions are shown progressively with overlapping timestamps. Always deduplicate when converting to plain text while preserving the original speaking order.
python3 -c " import sys, re seen = set() with open('transcript.en.vtt', 'r') as f: for line in f: line = line.strip() if line and not line.startswith('WEBVTT') and not line.startswith('Kind:') and not line.startswith('Language:') and '-->' not in line: clean = re.sub('<[^>]*>', '', line) clean = clean.replace('&', '&').replace('>', '>').replace('<', '<') if clean and clean not in seen: print(clean) seen.add(clean) " > transcript.txt
Complete Post-Processing with Video Title
# Get video title VIDEO_TITLE=$(yt-dlp --print "%(title)s" "YOUTUBE_URL" | tr '/' '_' | tr ':' '-' | tr '?' '' | tr '"' '') # Find the VTT file VTT_FILE=$(ls *.vtt | head -n 1) # Convert with deduplication python3 -c " import sys, re seen = set() with open('$VTT_FILE', 'r') as f: for line in f: line = line.strip() if line and not line.startswith('WEBVTT') and not line.startswith('Kind:') and not line.startswith('Language:') and '-->' not in line: clean = re.sub('<[^>]*>', '', line) clean = clean.replace('&', '&').replace('>', '>').replace('<', '<') if clean and clean not in seen: print(clean) seen.add(clean) " > "${VIDEO_TITLE}.txt" echo "✓ Saved to: ${VIDEO_TITLE}.txt" # Clean up VTT file rm "$VTT_FILE" echo "✓ Cleaned up temporary VTT file"
Output Formats
- VTT format (
): Includes timestamps and formatting, good for video players.vtt - Plain text (
): Just the text content, good for reading or analysis.txt
Tips
- The filename will be
(e.g.,{output_name}.{language_code}.vtt
)transcript.en.vtt - Most YouTube videos have auto-generated English subtitles
- Some videos may have multiple language options
- If auto-subtitles aren't available, try
instead for manual subtitles--write-sub
Complete Workflow Example
VIDEO_URL="https://www.youtube.com/watch?v=dQw4w9WgXcQ" # Get video title for filename VIDEO_TITLE=$(yt-dlp --print "%(title)s" "$VIDEO_URL" | tr '/' '_' | tr ':' '-' | tr '?' '' | tr '"' '') OUTPUT_NAME="transcript_temp" # ============================================ # STEP 1: Check if yt-dlp is installed # ============================================ if ! command -v yt-dlp &> /dev/null; then echo "yt-dlp not found, attempting to install..." if command -v brew &> /dev/null; then brew install yt-dlp elif command -v apt &> /dev/null; then sudo apt update && sudo apt install -y yt-dlp else pip3 install yt-dlp fi fi # ============================================ # STEP 2: List available subtitles # ============================================ echo "Checking available subtitles..." yt-dlp --list-subs "$VIDEO_URL" # ============================================ # STEP 3: Try manual subtitles first # ============================================ echo "Attempting to download manual subtitles..." if yt-dlp --write-sub --skip-download --output "$OUTPUT_NAME" "$VIDEO_URL" 2>/dev/null; then echo "✓ Manual subtitles downloaded successfully!" ls -lh ${OUTPUT_NAME}.* else # ============================================ # STEP 4: Fallback to auto-generated # ============================================ echo "Manual subtitles not available. Trying auto-generated..." if yt-dlp --write-auto-sub --skip-download --output "$OUTPUT_NAME" "$VIDEO_URL" 2>/dev/null; then echo "✓ Auto-generated subtitles downloaded successfully!" ls -lh ${OUTPUT_NAME}.* else # ============================================ # STEP 5: Last resort - Whisper transcription # ============================================ echo "⚠ No subtitles available for this video." # Get file size FILE_SIZE=$(yt-dlp --print "%(filesize_approx)s" -f "bestaudio" "$VIDEO_URL") DURATION=$(yt-dlp --print "%(duration)s" "$VIDEO_URL") TITLE=$(yt-dlp --print "%(title)s" "$VIDEO_URL") echo "Video: $TITLE" echo "Duration: $((DURATION / 60)) minutes" echo "Audio size: ~$((FILE_SIZE / 1024 / 1024)) MB" echo "" echo "Would you like to download and transcribe with Whisper? (y/n)" read -r RESPONSE if [[ "$RESPONSE" =~ ^[Yy]$ ]]; then # Check for Whisper if ! command -v whisper &> /dev/null; then echo "Whisper not installed. Install now? (requires ~1-3GB) (y/n)" read -r INSTALL_RESPONSE if [[ "$INSTALL_RESPONSE" =~ ^[Yy]$ ]]; then pip3 install openai-whisper else echo "Cannot proceed without Whisper. Exiting." exit 1 fi fi # Download audio echo "Downloading audio..." yt-dlp -x --audio-format mp3 --output "audio_%(id)s.%(ext)s" "$VIDEO_URL" # Get the actual audio filename AUDIO_FILE=$(ls audio_*.mp3 | head -n 1) # Transcribe echo "Transcribing with Whisper (this may take a few minutes)..." whisper "$AUDIO_FILE" --model base --output_format vtt # Cleanup echo "Transcription complete! Delete audio file? (y/n)" read -r CLEANUP_RESPONSE if [[ "$CLEANUP_RESPONSE" =~ ^[Yy]$ ]]; then rm "$AUDIO_FILE" echo "Audio file deleted." fi ls -lh *.vtt else echo "Transcription cancelled." exit 0 fi fi fi # ============================================ # STEP 6: Convert to readable plain text with deduplication # ============================================ VTT_FILE=$(ls ${OUTPUT_NAME}*.vtt 2>/dev/null || ls *.vtt | head -n 1) if [ -f "$VTT_FILE" ]; then echo "Converting to readable format and removing duplicates..." python3 -c " import sys, re seen = set() with open('$VTT_FILE', 'r') as f: for line in f: line = line.strip() if line and not line.startswith('WEBVTT') and not line.startswith('Kind:') and not line.startswith('Language:') and '-->' not in line: clean = re.sub('<[^>]*>', '', line) clean = clean.replace('&', '&').replace('>', '>').replace('<', '<') if clean and clean not in seen: print(clean) seen.add(clean) " > "${VIDEO_TITLE}.txt" echo "✓ Saved to: ${VIDEO_TITLE}.txt" # Clean up temporary VTT file rm "$VTT_FILE" echo "✓ Cleaned up temporary VTT file" else echo "⚠ No VTT file found to convert" fi echo "✓ Complete!"
Note: This complete workflow handles all scenarios with proper error checking and user prompts at each decision point.
Error Handling
Common Issues and Solutions:
1. yt-dlp not installed
- Attempt automatic installation based on system (Homebrew/apt/pip)
- If installation fails, provide manual installation link
- Verify installation before proceeding
2. No subtitles available
- List available subtitles first to confirm
- Try both
and--write-sub--write-auto-sub - If both fail, offer Whisper transcription option
- Show file size and ask for user confirmation before downloading audio
3. Invalid or private video
- Check if URL is correct format:
https://www.youtube.com/watch?v=VIDEO_ID - Some videos may be private, age-restricted, or geo-blocked
- Inform user of the specific error from yt-dlp
4. Whisper installation fails
- May require system dependencies (ffmpeg, rust)
- Provide fallback: "Install manually with:
"pip3 install openai-whisper - Check available disk space (models require 1-10GB depending on size)
5. Download interrupted or failed
- Check internet connection
- Verify sufficient disk space
- Try again with
if SSL issues occur--no-check-certificate
6. Multiple subtitle languages
- By default, yt-dlp downloads all available languages
- Can specify with
for English only--sub-langs en - List available with
first--list-subs
Best Practices:
- ✅ Always check what's available before attempting download (
)--list-subs - ✅ Verify success at each step before proceeding to next
- ✅ Ask user before large downloads (audio files, Whisper models)
- ✅ Clean up temporary files after processing
- ✅ Provide clear feedback about what's happening at each stage
- ✅ Handle errors gracefully with helpful messages