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Audio Transcriber

sickn33/antigravity-awesome-skills

  • 1.2k installs
  • 44k repo stars
  • Updated July 27, 2026
  • sickn33/antigravity-awesome-skills

audio-transcriber provides documented workflows for Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

About

The audio-transcriber skill transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration 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 Discove.

  • 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

Audio Transcriber by the numbers

  • 1,187 all-time installs (skills.sh)
  • +23 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #355 of 2,209 Security skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

audio-transcriber capabilities & compatibility

Capabilities
user needs to transcribe audio/video files to te · user wants meeting minutes automatically generat · user requires speaker identification (diarizatio · user needs subtitles/captions (srt, vtt formats) · user wants executive summaries of long audio con
Use cases
documentation
From the docs

What audio-transcriber says it does

It uses Faster-Whisper or Whisper with zero configuration, working universally across projects without hardcoded paths or API keys.
SKILL.md
Inspired by tools like Plaud, this skill transforms raw audio recordings into actionable documentation, making it ideal for meetings, interviews, lectures, and content analysis.
SKILL.md
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill audio-transcriber

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Listed on Skillselion
Installs1.2k
repo stars★ 44k
Security audit2 / 3 scanners passed
Last updatedJuly 27, 2026
Repositorysickn33/antigravity-awesome-skills ↗

How do I use audio-transcriber for the task described in its SKILL.md triggers?

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

Who is it for?

Teams invoking audio-transcriber when the user request matches documented triggers and prerequisites.

Skip if: Skip when cached docs are missing, the request is a negative trigger, or another sibling skill owns the workflow.

When should I use this skill?

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

What you get

Step-by-step guidance grounded in audio-transcriber documentation and reference files.

  • Structured LLM-processed document
  • Improved or auto-generated extraction prompt

By the numbers

  • Version 1.1.0 released 2026-02-03 with Step 3b prompt workflow

Files

SKILL.mdMarkdownGitHub ↗

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
fi

This 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)

2. Verify file exists:

if [[ ! -f "$AUDIO_FILE" ]]; then
    echo "❌ File not found: $AUDIO_FILE"
    exit 1
fi

3. Extract metadata using ffprobe or file utilities:

# Get file size
FILE_SIZE=$(du -h "$AUDIO_FILE" | cut -f1)

# Get duration and format using ffprobe
DURATION=$(ffprobe -v error -show_entries format=duration \
    -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)
FORMAT=$(ffprobe -v error -select_streams a:0 -show_entries \
    stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)

# Convert duration to HH:MM:SS
DURATION_HMS=$(date -u -r "$DURATION" +%H:%M:%S 2>/dev/null || echo "Unknown")

4. Check file size (warn if large for cloud APIs):

SIZE_MB=$(du -m "$AUDIO_FILE" | cut -f1)
if [[ $SIZE_MB -gt 25 ]]; then
    echo "⚠️  Large file ($FILE_SIZE) - processing may take several minutes"
fi

5. Validate format (supported: MP3, WAV, M4A, OGG, FLAC, WEBM):

EXTENSION="${AUDIO_FILE##*.}"
SUPPORTED_FORMATS=("mp3" "wav" "m4a" "ogg" "flac" "webm" "mp4")

if [[ ! " ${SUPPORTED_FORMATS[@]} " =~ " ${EXTENSION,,} " ]]; then
    echo "⚠️  Unsupported format: $EXTENSION"
    if command -v ffmpeg &>/dev/null; then
        echo "🔄 Converting to WAV..."
        ffmpeg -i "$AUDIO_FILE" -ar 16000 "${AUDIO_FILE%.*}.wav" -y
        AUDIO_FILE="${AUDIO_FILE%.*}.wav"
    else
        echo "❌ Install ffmpeg to convert formats: brew install ffmpeg"
        exit 1
    fi
fi

Step 3: Generate Markdown Output

Objective: Create structured Markdown with metadata, transcription, meeting minutes, and summary.

Output Template:

# Audio Transcription Report

## 📊 Metadata

| Field | Value |
|-------|-------|
| **File Name** | {filename} |
| **File Size** | {file_size} |
| **Duration** | {duration_hms} |
| **Language** | {language} ({language_code}) |
| **Processed Date** | {process_date} |
| **Speakers Identified** | {num_speakers} |
| **Transcription Engine** | {engine} (model: {model}) |


## 📋 Meeting Minutes

### Participants
- {speaker_1}
- {speaker_2}
- ...

### Topics Discussed
1. **{topic_1}** ({timestamp})
   - {key_point_1}
   - {key_point_2}

2. **{topic_2}** ({timestamp})
   - {key_point_1}

### Decisions Made
- ✅ {decision_1}
- ✅ {decision_2}

### Action Items
- [ ] **{action_1}** - Assigned to: {speaker} - Due: {date_if_mentioned}
- [ ] **{action_2}** - Assigned to: {speaker}


*Generated by audio-transcriber skill v1.0.0*  
*Transcription engine: {engine} | Processing time: {elapsed_time}s*

Implementation:

Use Python or bash with AI model (Claude/GPT) for intelligent summarization:

def generate_meeting_minutes(segments):
    """Extract topics, decisions, action items from transcription."""
    
    # Group segments by topic (simple clustering by timestamps)
    topics = cluster_by_topic(segments)
    
    # Identify action items (keywords: "should", "will", "need to", "action")
    action_items = extract_action_items(segments)
    
    # Identify decisions (keywords: "decided", "agreed", "approved")
    decisions = extract_decisions(segments)
    
    return {
        "topics": topics,
        "decisions": decisions,
        "action_items": action_items
    }

def generate_summary(segments, max_paragraphs=5):
    """Create executive summary using AI (Claude/GPT via API or local model)."""
    
    full_text = " ".join([s["text"] for s in segments])
    
    # Use Chain of Density approach (from prompt-engineer frameworks)
    summary_prompt = f"""
    Summarize the following transcription in {max_paragraphs} concise paragraphs.
    Focus on key topics, decisions, and action items.
    
    Transcription:
    {full_text}
    """
    
    # Call AI model (placeholder - user can integrate Claude API or use local model)
    summary = call_ai_model(summary_prompt)
    
    return summary

Output file naming:

# v1.1.0: Use timestamp para evitar sobrescrever
TIMESTAMP=$(date +%Y%m%d-%H%M%S)
TRANSCRIPT_FILE="transcript-${TIMESTAMP}.md"
ATA_FILE="ata-${TIMESTAMP}.md"

echo "$TRANSCRIPT_CONTENT" > "$TRANSCRIPT_FILE"
echo "✅ Transcript salvo: $TRANSCRIPT_FILE"

if [[ -n "$ATA_CONTENT" ]]; then
    echo "$ATA_CONTENT" > "$ATA_FILE"
    echo "✅ Ata salva: $ATA_FILE"
fi
SCENARIO A: User Provided Custom Prompt

Workflow:

1. Display user's prompt:

   📝 Prompt fornecido pelo usuário:
   ┌──────────────────────────────────┐
   │ [User's prompt preview]          │
   └──────────────────────────────────┘

2. Automatically improve with prompt-engineer (if available):

   🔧 Melhorando prompt com prompt-engineer...
   [Invokes: gh copilot -p "melhore este prompt: {user_prompt}"]

3. Show both versions:

   ✨ Versão melhorada:
   ┌──────────────────────────────────┐
   │ Role: Você é um documentador...  │
   │ Instructions: Transforme...      │
   │ Steps: 1) ... 2) ...             │
   │ End Goal: ...                    │
   └──────────────────────────────────┘

   📝 Versão original:
   ┌──────────────────────────────────┐
   │ [User's original prompt]         │
   └──────────────────────────────────┘

4. Ask which to use:

   💡 Usar versão melhorada? [s/n] (default: s):

5. Process with selected prompt:

  • If "s": use improved
  • If "n": use original
LLM Processing (Both Scenarios)

Once prompt is finalized:

from rich.progress import Progress, SpinnerColumn, TextColumn

def process_with_llm(transcript, prompt, cli_tool='claude'):
    full_prompt = f"{prompt}\n\n---\n\nTranscrição:\n\n{transcript}"
    
    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        transient=True
    ) as progress:
        progress.add_task(
            description=f"🤖 Processando com {cli_tool}...",
            total=None
        )
        
        if cli_tool == 'claude':
            result = subprocess.run(
                ['claude', '-'],
                input=full_prompt,
                capture_output=True,
                text=True,
                timeout=300  # 5 minutes
            )
        elif cli_tool == 'gh-copilot':
            result = subprocess.run(
                ['gh', 'copilot', 'suggest', '-t', 'shell', full_prompt],
                capture_output=True,
                text=True,
                timeout=300
            )
    
    if result.returncode == 0:
        return result.stdout.strip()
    else:
        return None

Progress output:

🤖 Processando com claude... ⠋
[After completion:]
✅ Ata gerada com sucesso!
Final Output

Success (both files):

💾 Salvando arquivos...

✅ Arquivos criados:
  - transcript-20260203-023045.md  (transcript puro)
  - ata-20260203-023045.md         (processado com LLM)

🧹 Removidos arquivos temporários: metadata.json, transcription.json

✅ Concluído! Tempo total: 3m 45s

Transcript only (user declined LLM):

💾 Salvando arquivos...

✅ Arquivo criado:
  - transcript-20260203-023045.md

ℹ️  Ata não gerada (processamento LLM recusado pelo usuário)

🧹 Removidos arquivos temporários: metadata.json, transcription.json

✅ Concluído!

Step 5: Display Results Summary

Objective: Show completion status and next steps.

Output:

echo ""
echo "✅ Transcription Complete!"
echo ""
echo "📊 Results:"
echo "  File: $OUTPUT_FILE"
echo "  Language: $LANGUAGE"
echo "  Duration: $DURATION_HMS"
echo "  Speakers: $NUM_SPEAKERS"
echo "  Words: $WORD_COUNT"
echo "  Processing time: ${ELAPSED_TIME}s"
echo ""
echo "📝 Generated:"
echo "  - $OUTPUT_FILE (Markdown report)"
[if alternative formats:]
echo "  - ${OUTPUT_FILE%.*}.srt (Subtitles)"
echo "  - ${OUTPUT_FILE%.*}.json (Structured data)"
echo ""
echo "🎯 Next steps:"
echo "  1. Review meeting minutes and action items"
echo "  2. Share report with participants"
echo "  3. Track action items to completion"

Example Usage

Example 1: Basic Transcription

User Input:

copilot> transcribe audio to markdown: meeting-2026-02-02.mp3

Skill Output:

✅ Faster-Whisper detected (optimized)
✅ ffmpeg available (format conversion enabled)

📂 File: meeting-2026-02-02.mp3
📊 Size: 12.3 MB
⏱️  Duration: 00:45:32

🎙️  Processing...
[████████████████████] 100%

✅ Language detected: Portuguese (pt-BR)
👥 Speakers identified: 4
📝 Generating Markdown output...

✅ Transcription Complete!

📊 Results:
  File: meeting-2026-02-02.md
  Language: pt-BR
  Duration: 00:45:32
  Speakers: 4
  Words: 6,842
  Processing time: 127s

📝 Generated:
  - meeting-2026-02-02.md (Markdown report)

🎯 Next steps:
  1. Review meeting minutes and action items
  2. Share report with participants
  3. Track action items to completion

Example 3: Batch Processing

User Input:

copilot> transcreva estes áudios: recordings/*.mp3

Skill Output:

📦 Batch mode: 5 files found
  1. team-standup.mp3
  2. client-call.mp3
  3. brainstorm-session.mp3
  4. product-demo.mp3
  5. retrospective.mp3

🎙️  Processing batch...

[1/5] team-standup.mp3 ✅ (2m 34s)
[2/5] client-call.mp3 ✅ (15m 12s)
[3/5] brainstorm-session.mp3 ✅ (8m 47s)
[4/5] product-demo.mp3 ✅ (22m 03s)
[5/5] retrospective.mp3 ✅ (11m 28s)

✅ Batch Complete!
📝 Generated 5 Markdown reports
⏱️  Total processing time: 6m 15s

Example 5: Large File Warning

User Input:

copilot> transcribe audio to markdown: conference-keynote.mp3

Skill Output:

✅ Faster-Whisper detected (optimized)

📂 File: conference-keynote.mp3
📊 Size: 87.2 MB
⏱️  Duration: 02:15:47
⚠️  Large file (87.2 MB) - processing may take several minutes

Continue? [Y/n]:

User: Y

🎙️  Processing... (this may take 10-15 minutes)
[████░░░░░░░░░░░░░░░░] 20% - Estimated time remaining: 12m

This skill is platform-agnostic and works in any terminal context where GitHub Copilot CLI is available. It does not depend on specific project configurations or external APIs, following the zero-configuration philosophy.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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How it compares

Use for agent-driven audio-to-structured-doc pipelines; use markitdown when the source is already PDF or Office files rather than spoken audio.

FAQ

What does audio-transcriber do?

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

When should I use audio-transcriber?

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

What are common prerequisites?

--- name: audio-transcriber description: "Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration" category: content risk: safe source: community tags: "[audio

Is Audio Transcriber safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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