audio-transcriber
Converts audio files into professional Markdown meeting minutes and summaries using intelligent speech-to-text.
Install
mkdir -p .claude/skills/audio-transcriber && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/5173" && unzip -o skill.zip -d .claude/skills/audio-transcriber && rm skill.zipInstalls to .claude/skills/audio-transcriber
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integrationKey capabilities
- →Transcribe audio files to Markdown
- →Identify speakers and timestamps
- →Generate executive summaries and meeting minutes
- →Convert audio formats using ffmpeg
- →Extract action items and decisions
How it works
It detects installed transcription engines, validates audio formats, processes the audio through Whisper models, and uses LLM integration to structure the output into meeting minutes.
Inputs & outputs
When to use audio-transcriber
- →Transcribe meeting recordings into text
- →Generate executive summaries from lectures
- →Create SRT or VTT subtitles for video content
- →Format raw audio into technical documentation
About this skill
Detailed Guide
Read the detailed guide 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:
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.
When not to use it
- →When high-security or private data cannot be processed locally
- →When audio quality is too low for transcription models
Prerequisites
Limitations
- →Processing time depends on file duration and hardware
- →Requires ffmpeg for format conversion
How it compares
It provides a complete, automated documentation workflow including metadata extraction and structured summaries, rather than just raw text output.
Compared to similar skills
audio-transcriber side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| audio-transcriber (this skill) | 1 | 5mo | Review | Beginner |
| update-release-notes | 1 | 2mo | Review | Advanced |
| blogpost-creator | 1 | 4mo | Review | Beginner |
| markdown-writer | 0 | 10mo | No flags | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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