Audio Transcriber
Audio Transcriber is an learning AI skill with a core value of Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration. It
helps developers solve real-world problems in the learning domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
Quick Facts
mkdir -p ./skills/audio-transcriber && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/audio-transcriber/SKILL.md -o ./skills/audio-transcriber/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
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:
# 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 (optimized)"
# Fallback to original Whisper
elif python3 -c "import whisper" 2>/dev/null; then
TRANSCRIBER="whisper"
echo "✅ OpenAI Whisper detected"
else
TRANSCRIBER="none"
echo "⚠️ No transcription tool found"
fi
# Check for ffmpeg (audio format conversion)
if command -v ffmpeg &>/dev/null; then
echo "✅ ffmpeg available (format conversion enabled)"
else
echo "ℹ️ ffmpeg not found (limited format support)"
fi**If no transcriber found:**
Offer automatic installation using the provided script:
echo "⚠️ No transcription tool found"
echo ""
echo "🔧 Auto-install dependencies? (Recommended)"
read -p "Run installation script? [Y/n]: " AUTO_INSTALL
if [[ ! "$AUTO_INSTALL" =~ ^[Nn] ]]; then
# Get skill directory (works for both repo and symlinked installations)
SKILL_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
# Run installation script
if [[ -f "$SKILL_DIR/scripts/install-requirements.sh" ]]; then
bash "$SKILL_DIR/scripts/install-requirements.sh"
else
echo "❌ Installation script not found"
echo ""
echo "📦 Manual installation:"
echo " pip install faster-whisper # Recommended"
echo " pip install openai-whisper # Alternative"
echo " brew install ffmpeg # Optional (macOS)"
exit 1
fi
# Verify installation succeeded
if python3 -c "import faster_whisper" 2>/dev/null || python3 -c "import whisper" 2>/dev/null; then
echo "✅ Installation successful! Proceeding with transcription..."
else
echo "❌ Installation failed. Please install manually."
exit 1
fi
else
echo ""
echo "📦 Manual installation required:"
echo ""
echo "Recommended (fastest):"
echo " pip install faster-whisper"
echo ""
echo "Alternative (original):"
echo " pip install openai-whisper"
echo ""
echo "Optional (format conversion):"
echo " brew install ffmpeg # macOS"
echo " apt install ffmpeg # Linux"
echo ""
exit 1
fiThis ensures users can install dependencies with one confirmation, or opt for manual installation if preferred.
**If transcriber found:**
Proceed to Step 0b (CLI Detection).
Step 1: Validate Audio File
**Objective:** Verify file exists, check format, and extract metadata.
**Actions:**
1. **Accept file path or URL** from user:
- Local file: `meeting.mp3`
- URL: `https://example.com/audio.mp3` (download to temp directory)
🎯 Best For
- Technical writers
- API documentation teams
- Claude users
- Students
- Lifelong learners
💡 Use Cases
- Generating JSDoc/TSDoc comments
- Writing README files for new projects
- Using Audio Transcriber in daily workflow
- Automating repetitive learning tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Audio Transcriber to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Does it follow my documentation style?
Most documentation skills respect existing style. Provide a style guide or example in your prompt.
How do I install Audio Transcriber?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/audio-transcriber/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Auto-generating without reviewing
AI documentation can contain inaccuracies. Always verify technical accuracy.
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.