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Turn audio recordings into Markdown documentation with LLM-generated summaries.
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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Original instructions from the publisher’s SKILL.md
## Detailed Guide Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely. ## 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) ## Example Usage ### **Example 1: Basic Transcription** **User Input:** ```bash copilot> transcribe audio to markdown: meeting-2026-02-02.mp3 ``` **Skill Output:** ```bash ✅ 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:** ```bash copilot> transcreva estes áudios: recordings/*.mp3 ``` **Skill Output:** ```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. ## 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.