feat: add 4 universal skills from cli-ai-skills
- Add audio-transcriber skill (v1.2.0): Transform audio to Markdown with Whisper - Add youtube-summarizer skill (v1.2.0): Generate summaries from YouTube videos - Update prompt-engineer skill: Enhanced with 11 optimization frameworks - Update skill-creator skill: Improved automation workflow All skills are zero-config, cross-platform (Claude Code, Copilot CLI, Codex) and follow Quality Bar V4 standards. Source: https://github.com/ericgandrade/cli-ai-skills
This commit is contained in:
Eric Andrade
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skills/audio-transcriber/SKILL.md
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skills/audio-transcriber/SKILL.md
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---
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name: audio-transcriber
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description: "Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration"
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version: 1.2.0
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author: Eric Andrade
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created: 2025-02-01
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updated: 2026-02-04
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platforms: [github-copilot-cli, claude-code, codex]
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category: content
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tags: [audio, transcription, whisper, meeting-minutes, speech-to-text]
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risk: safe
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---
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## Purpose
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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.
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Inspired by tools like Plaud, this skill transforms raw audio recordings into actionable documentation, making it ideal for meetings, interviews, lectures, and content analysis.
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## When to Use
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Invoke this skill when:
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- User needs to transcribe audio/video files to text
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- User wants meeting minutes automatically generated from recordings
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- User requires speaker identification (diarization) in conversations
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- User needs subtitles/captions (SRT, VTT formats)
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- User wants executive summaries of long audio content
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- User asks variations of "transcribe this audio", "convert audio to text", "generate meeting notes from recording"
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- User has audio files in common formats (MP3, WAV, M4A, OGG, FLAC, WEBM)
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## Workflow
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### Step 0: Discovery (Auto-detect Transcription Tools)
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**Objective:** Identify available transcription engines without user configuration.
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**Actions:**
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Run detection commands to find installed tools:
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```bash
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# Check for Faster-Whisper (preferred - 4-5x faster)
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if python3 -c "import faster_whisper" 2>/dev/null; then
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TRANSCRIBER="faster-whisper"
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echo "✅ Faster-Whisper detected (optimized)"
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# Fallback to original Whisper
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elif python3 -c "import whisper" 2>/dev/null; then
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TRANSCRIBER="whisper"
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echo "✅ OpenAI Whisper detected"
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else
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TRANSCRIBER="none"
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echo "⚠️ No transcription tool found"
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fi
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# Check for ffmpeg (audio format conversion)
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if command -v ffmpeg &>/dev/null; then
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echo "✅ ffmpeg available (format conversion enabled)"
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else
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echo "ℹ️ ffmpeg not found (limited format support)"
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fi
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```
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**If no transcriber found:**
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Offer automatic installation using the provided script:
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```bash
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echo "⚠️ No transcription tool found"
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echo ""
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echo "🔧 Auto-install dependencies? (Recommended)"
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read -p "Run installation script? [Y/n]: " AUTO_INSTALL
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if [[ ! "$AUTO_INSTALL" =~ ^[Nn] ]]; then
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# Get skill directory (works for both repo and symlinked installations)
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SKILL_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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# Run installation script
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if [[ -f "$SKILL_DIR/scripts/install-requirements.sh" ]]; then
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bash "$SKILL_DIR/scripts/install-requirements.sh"
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else
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echo "❌ Installation script not found"
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echo ""
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echo "📦 Manual installation:"
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echo " pip install faster-whisper # Recommended"
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echo " pip install openai-whisper # Alternative"
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echo " brew install ffmpeg # Optional (macOS)"
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exit 1
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fi
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# Verify installation succeeded
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if python3 -c "import faster_whisper" 2>/dev/null || python3 -c "import whisper" 2>/dev/null; then
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echo "✅ Installation successful! Proceeding with transcription..."
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else
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echo "❌ Installation failed. Please install manually."
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exit 1
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fi
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else
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echo ""
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echo "📦 Manual installation required:"
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echo ""
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echo "Recommended (fastest):"
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echo " pip install faster-whisper"
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echo ""
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echo "Alternative (original):"
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echo " pip install openai-whisper"
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echo ""
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echo "Optional (format conversion):"
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echo " brew install ffmpeg # macOS"
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echo " apt install ffmpeg # Linux"
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echo ""
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exit 1
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fi
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```
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This ensures users can install dependencies with one confirmation, or opt for manual installation if preferred.
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**If transcriber found:**
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Proceed to Step 0b (CLI Detection).
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### Step 1: Validate Audio File
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**Objective:** Verify file exists, check format, and extract metadata.
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**Actions:**
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1. **Accept file path or URL** from user:
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- Local file: `meeting.mp3`
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- URL: `https://example.com/audio.mp3` (download to temp directory)
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2. **Verify file exists:**
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```bash
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if [[ ! -f "$AUDIO_FILE" ]]; then
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echo "❌ File not found: $AUDIO_FILE"
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exit 1
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fi
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```
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3. **Extract metadata** using ffprobe or file utilities:
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```bash
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# Get file size
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FILE_SIZE=$(du -h "$AUDIO_FILE" | cut -f1)
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# Get duration and format using ffprobe
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DURATION=$(ffprobe -v error -show_entries format=duration \
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-of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)
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FORMAT=$(ffprobe -v error -select_streams a:0 -show_entries \
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stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)
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# Convert duration to HH:MM:SS
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DURATION_HMS=$(date -u -r "$DURATION" +%H:%M:%S 2>/dev/null || echo "Unknown")
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```
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4. **Check file size** (warn if large for cloud APIs):
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```bash
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SIZE_MB=$(du -m "$AUDIO_FILE" | cut -f1)
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if [[ $SIZE_MB -gt 25 ]]; then
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echo "⚠️ Large file ($FILE_SIZE) - processing may take several minutes"
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fi
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```
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5. **Validate format** (supported: MP3, WAV, M4A, OGG, FLAC, WEBM):
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```bash
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EXTENSION="${AUDIO_FILE##*.}"
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SUPPORTED_FORMATS=("mp3" "wav" "m4a" "ogg" "flac" "webm" "mp4")
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if [[ ! " ${SUPPORTED_FORMATS[@]} " =~ " ${EXTENSION,,} " ]]; then
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echo "⚠️ Unsupported format: $EXTENSION"
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if command -v ffmpeg &>/dev/null; then
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echo "🔄 Converting to WAV..."
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ffmpeg -i "$AUDIO_FILE" -ar 16000 "${AUDIO_FILE%.*}.wav" -y
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AUDIO_FILE="${AUDIO_FILE%.*}.wav"
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else
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echo "❌ Install ffmpeg to convert formats: brew install ffmpeg"
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exit 1
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fi
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fi
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```
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### Step 3: Generate Markdown Output
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**Objective:** Create structured Markdown with metadata, transcription, meeting minutes, and summary.
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**Output Template:**
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```markdown
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# Audio Transcription Report
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## 📊 Metadata
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| Field | Value |
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|-------|-------|
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| **File Name** | {filename} |
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| **File Size** | {file_size} |
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| **Duration** | {duration_hms} |
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| **Language** | {language} ({language_code}) |
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| **Processed Date** | {process_date} |
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| **Speakers Identified** | {num_speakers} |
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| **Transcription Engine** | {engine} (model: {model}) |
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## 📋 Meeting Minutes
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### Participants
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- {speaker_1}
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- {speaker_2}
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- ...
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### Topics Discussed
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1. **{topic_1}** ({timestamp})
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- {key_point_1}
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- {key_point_2}
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2. **{topic_2}** ({timestamp})
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- {key_point_1}
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### Decisions Made
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- ✅ {decision_1}
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- ✅ {decision_2}
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### Action Items
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- [ ] **{action_1}** - Assigned to: {speaker} - Due: {date_if_mentioned}
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- [ ] **{action_2}** - Assigned to: {speaker}
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*Generated by audio-transcriber skill v1.0.0*
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*Transcription engine: {engine} | Processing time: {elapsed_time}s*
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```
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**Implementation:**
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Use Python or bash with AI model (Claude/GPT) for intelligent summarization:
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```python
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def generate_meeting_minutes(segments):
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"""Extract topics, decisions, action items from transcription."""
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# Group segments by topic (simple clustering by timestamps)
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topics = cluster_by_topic(segments)
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# Identify action items (keywords: "should", "will", "need to", "action")
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action_items = extract_action_items(segments)
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# Identify decisions (keywords: "decided", "agreed", "approved")
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decisions = extract_decisions(segments)
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return {
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"topics": topics,
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"decisions": decisions,
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"action_items": action_items
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}
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def generate_summary(segments, max_paragraphs=5):
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"""Create executive summary using AI (Claude/GPT via API or local model)."""
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full_text = " ".join([s["text"] for s in segments])
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# Use Chain of Density approach (from prompt-engineer frameworks)
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summary_prompt = f"""
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Summarize the following transcription in {max_paragraphs} concise paragraphs.
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Focus on key topics, decisions, and action items.
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Transcription:
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{full_text}
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"""
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# Call AI model (placeholder - user can integrate Claude API or use local model)
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summary = call_ai_model(summary_prompt)
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return summary
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```
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**Output file naming:**
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```bash
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# v1.1.0: Use timestamp para evitar sobrescrever
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TIMESTAMP=$(date +%Y%m%d-%H%M%S)
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TRANSCRIPT_FILE="transcript-${TIMESTAMP}.md"
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ATA_FILE="ata-${TIMESTAMP}.md"
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echo "$TRANSCRIPT_CONTENT" > "$TRANSCRIPT_FILE"
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echo "✅ Transcript salvo: $TRANSCRIPT_FILE"
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if [[ -n "$ATA_CONTENT" ]]; then
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echo "$ATA_CONTENT" > "$ATA_FILE"
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echo "✅ Ata salva: $ATA_FILE"
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fi
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```
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#### **SCENARIO A: User Provided Custom Prompt**
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**Workflow:**
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1. **Display user's prompt:**
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```
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📝 Prompt fornecido pelo usuário:
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┌──────────────────────────────────┐
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│ [User's prompt preview] │
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└──────────────────────────────────┘
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```
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2. **Automatically improve with prompt-engineer (if available):**
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```bash
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🔧 Melhorando prompt com prompt-engineer...
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[Invokes: gh copilot -p "melhore este prompt: {user_prompt}"]
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```
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3. **Show both versions:**
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```
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✨ Versão melhorada:
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┌──────────────────────────────────┐
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│ Role: Você é um documentador... │
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│ Instructions: Transforme... │
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│ Steps: 1) ... 2) ... │
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│ End Goal: ... │
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└──────────────────────────────────┘
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📝 Versão original:
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┌──────────────────────────────────┐
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│ [User's original prompt] │
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└──────────────────────────────────┘
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```
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4. **Ask which to use:**
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```bash
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💡 Usar versão melhorada? [s/n] (default: s):
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```
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5. **Process with selected prompt:**
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- If "s": use improved
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- If "n": use original
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#### **LLM Processing (Both Scenarios)**
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Once prompt is finalized:
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```python
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from rich.progress import Progress, SpinnerColumn, TextColumn
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def process_with_llm(transcript, prompt, cli_tool='claude'):
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full_prompt = f"{prompt}\n\n---\n\nTranscrição:\n\n{transcript}"
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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transient=True
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) as progress:
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progress.add_task(
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description=f"🤖 Processando com {cli_tool}...",
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total=None
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)
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if cli_tool == 'claude':
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result = subprocess.run(
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['claude', '-'],
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input=full_prompt,
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capture_output=True,
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text=True,
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timeout=300 # 5 minutes
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)
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elif cli_tool == 'gh-copilot':
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result = subprocess.run(
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['gh', 'copilot', 'suggest', '-t', 'shell', full_prompt],
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capture_output=True,
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text=True,
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timeout=300
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)
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if result.returncode == 0:
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return result.stdout.strip()
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else:
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return None
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```
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**Progress output:**
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```
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🤖 Processando com claude... ⠋
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[After completion:]
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✅ Ata gerada com sucesso!
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```
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#### **Final Output**
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**Success (both files):**
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```bash
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💾 Salvando arquivos...
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✅ Arquivos criados:
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- transcript-20260203-023045.md (transcript puro)
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- ata-20260203-023045.md (processado com LLM)
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🧹 Removidos arquivos temporários: metadata.json, transcription.json
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✅ Concluído! Tempo total: 3m 45s
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```
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**Transcript only (user declined LLM):**
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```bash
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💾 Salvando arquivos...
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✅ Arquivo criado:
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- transcript-20260203-023045.md
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ℹ️ Ata não gerada (processamento LLM recusado pelo usuário)
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🧹 Removidos arquivos temporários: metadata.json, transcription.json
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✅ Concluído!
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```
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### Step 5: Display Results Summary
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**Objective:** Show completion status and next steps.
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**Output:**
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```bash
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echo ""
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echo "✅ Transcription Complete!"
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echo ""
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echo "📊 Results:"
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echo " File: $OUTPUT_FILE"
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echo " Language: $LANGUAGE"
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echo " Duration: $DURATION_HMS"
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echo " Speakers: $NUM_SPEAKERS"
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echo " Words: $WORD_COUNT"
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echo " Processing time: ${ELAPSED_TIME}s"
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echo ""
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echo "📝 Generated:"
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echo " - $OUTPUT_FILE (Markdown report)"
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[if alternative formats:]
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echo " - ${OUTPUT_FILE%.*}.srt (Subtitles)"
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echo " - ${OUTPUT_FILE%.*}.json (Structured data)"
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echo ""
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echo "🎯 Next steps:"
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echo " 1. Review meeting minutes and action items"
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echo " 2. Share report with participants"
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echo " 3. Track action items to completion"
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```
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## Example Usage
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### **Example 1: Basic Transcription**
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**User Input:**
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```bash
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copilot> transcribe audio to markdown: meeting-2026-02-02.mp3
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```
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**Skill Output:**
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```bash
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✅ Faster-Whisper detected (optimized)
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✅ ffmpeg available (format conversion enabled)
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📂 File: meeting-2026-02-02.mp3
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📊 Size: 12.3 MB
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⏱️ Duration: 00:45:32
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🎙️ Processing...
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[████████████████████] 100%
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✅ Language detected: Portuguese (pt-BR)
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👥 Speakers identified: 4
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📝 Generating Markdown output...
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✅ Transcription Complete!
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📊 Results:
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File: meeting-2026-02-02.md
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Language: pt-BR
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Duration: 00:45:32
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Speakers: 4
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Words: 6,842
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Processing time: 127s
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📝 Generated:
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- meeting-2026-02-02.md (Markdown report)
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🎯 Next steps:
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1. Review meeting minutes and action items
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2. Share report with participants
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3. Track action items to completion
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```
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### **Example 3: Batch Processing**
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**User Input:**
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```bash
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copilot> transcreva estes áudios: recordings/*.mp3
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```
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**Skill Output:**
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||||
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||||
```bash
|
||||
📦 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:**
|
||||
```bash
|
||||
copilot> transcribe audio to markdown: conference-keynote.mp3
|
||||
```
|
||||
|
||||
**Skill Output:**
|
||||
|
||||
```bash
|
||||
✅ 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`
|
||||
|
||||
```bash
|
||||
🎙️ 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.
|
||||
Reference in New Issue
Block a user