youtube-summarizer

Purpose

This skill extracts transcripts from YouTube videos and generates comprehensive, verbose summaries using the STAR + R-I-S-E framework. It validates video availability, extracts transcripts using the youtube-transcript-api Python library, and produces detailed documentation capturing all insights, arguments, and key points.

The skill is designed for users who need thorough content analysis and reference documentation from educational videos, lectures, tutorials, or informational content.

When to Use This Skill

This skill should be used when:

• User provides a YouTube video URL and wants a detailed summary
• User needs to document video content for reference without rewatching
• User wants to extract insights, key points, and arguments from educational content
• User needs transcripts from YouTube videos for analysis
• User asks to "summarize", "resume", or "extract content" from YouTube videos
• User wants comprehensive documentation prioritizing completeness over brevity

Step 0: Discovery & Setup

Before processing videos, validate the environment and dependencies:

# Check if youtube-transcript-api is installed
~/.venvs/youtube-summarizer/bin/python3 -c "import youtube_transcript_api" 2>/dev/null
if [ $? -ne 0 ]; then
 echo "⚠️ youtube-transcript-api not found"
 # Offer to install
fi

# Check Python availability
if ! command -v python3 &>/dev/null; then
 echo "❌ Python 3 is required but not installed"
 exit 1
fi

# Check Groq key (resumo sem tokens Claude)
if [ -z "$GROQ_API_KEY" ]; then
  echo "⚠️ GROQ_API_KEY não definido — resumo usará Claude (gasta tokens)"
fi

# Check faster-whisper (fallback para vídeos sem legenda)
python3 -c "import faster_whisper" 2>/dev/null || echo "⚠️ faster-whisper não instalado (necessário só para vídeos sem legenda)"

Fallback: Vídeo sem legenda → faster-whisper tiny

Se o Step 2 retornar TranscriptsDisabled ou NoTranscriptFound, usar áudio local:

# Baixar áudio do vídeo
python3 -m yt_dlp -x --audio-format mp3 -o /tmp/yt_audio_$VIDEO_ID.mp3 "$URL" 2>/dev/null

# Transcrever localmente com Whisper tiny (~300MB RAM, libera logo)
python3 -c "
from faster_whisper import WhisperModel
model = WhisperModel('tiny', device='cpu', compute_type='int8')
segments, info = model.transcribe('/tmp/yt_audio_$VIDEO_ID.mp3', language='pt')
text = ' '.join(s.text for s in segments)
with open('/tmp/transcript_$VIDEO_ID.txt', 'w') as f:
    f.write(text)
print(f'✅ Transcrição local: {len(text)} chars')
"
# Continuar para Step 4 normalmente