audio-transcriberlisted

## Purpose This skill automates audio-to-text transcription with professional Markdown output, extracting rich technical metadata (speakers, timestamps, language, file size, duration) and generating structured meeting minutes and executive summaries. It uses Faster-Whisper or Whisper with zero configuration, working universally across projects without hardcoded paths or API keys. Inspired by tools like Plaud, this skill transforms raw audio recordings into actionable documentation, making it ideal for meetings, interviews, lectures, and content analysis. ## When to Use Invoke this skill when: - User needs to transcribe audio/video files to text - User wants meeting minutes automatically generated from recordings - User requires speaker identification (diarization) in conversations - User needs subtitles/captions (SRT, VTT formats) - User wants executive summaries of long audio content - User asks variations of "transcribe this audio", "convert audio to text", "generate meeting notes from recording" - User has audio files in common formats (MP3, WAV, M4A, OGG, FLAC, WEBM) ## Workflow ### Step 0: Discovery (Auto-detect Transcription Tools) **Objective:** Identify available transcription engines without user configuration. **Actions:** Run detection commands to find installed tools: ```bash # Check for Faster-Whisper (preferred - 4-5x faster) if python3 -c "import faster_whisper" 2>/dev/null; then TRANSCRIBER="faster-whisper" echo "✅ Faster-Whisper detected (o