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SKILL.md
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)
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:
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
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.