Agent-Skills.md

Agent Skills: Transcribe

Transcribe audio or video files to text locally with Whisper - mlx-whisper on macOS with Apple Silicon, openai-whisper elsewhere. Use when the user wants to transcribe a file, get a transcript of a recording, turn a voice note into text, transcribe a meeting, podcast or interview, produce subtitles (SRT/VTT) from audio, or transcribe audio just downloaded with youtube-search. Do NOT use for finding or downloading YouTube videos - use youtube-search; for converting, trimming or extracting audio - use ffmpeg-cli; for live dictation or real-time speech recognition.

UncategorizedID: henkisdabro/wookstar-claude-plugins/transcribe

Repository

henkisdabro/wookstar-claude-plugins
henkisdabroLicense: NOASSERTION
9213

Install this agent skill to your local

pnpm dlx add-skill https://github.com/henkisdabro/wookstar-claude-plugins/tree/HEAD/plugins/media-tools/skills/transcribe

Skill Files

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plugins/media-tools/skills/transcribe/SKILL.md

Skill Metadata

Name
transcribe
Description
Transcribe audio or video files to text locally with Whisper - mlx-whisper on macOS with Apple Silicon, openai-whisper elsewhere. Use when the user wants to transcribe a file, get a transcript of a recording, turn a voice note into text, transcribe a meeting, podcast or interview, produce subtitles (SRT/VTT) from audio, or transcribe audio just downloaded with youtube-search. Do NOT use for finding or downloading YouTube videos - use youtube-search; for converting, trimming or extracting audio - use ffmpeg-cli; for live dictation or real-time speech recognition.

Transcribe

Local speech-to-text with Whisper. On Apple Silicon, mlx-whisper runs on the GPU and is fast (a 15-20 minute file takes a couple of minutes). Elsewhere, openai-whisper does the same job more slowly. Both run through uvx, so nothing is installed permanently.

Steps

  1. Locate the input. Use the path the user gave, or the path printed by the youtube-search download step (--print after_move:filepath). Done when ls confirms the file exists.

  2. Convert only if needed. Both engines read m4a, mp3, wav, flac, ogg, webm and mp4 directly via ffmpeg. Convert first with the ffmpeg-cli skill (from this marketplace's ffmpeg plugin) when the file is an unusual format (.amr, .wma, .caf), or an .opus voice note, which decodes more reliably as m4a:

    ffmpeg -i "input.opus" -c:a aac -b:a 128k "input.m4a"

  3. Pick the language. Pass --language <code> (en, es, fr, ...) whenever the user states it or the context makes it obvious - auto-detection costs time and can misidentify short clips. Omit the flag only when the language is genuinely unknown.

  4. Pick the engine with uname -sm: Darwin arm64 runs mlx-whisper; anything else runs openai-whisper.

  5. Run it (allow up to 600 s; the first run also downloads the model, about 1.5 GB):

    Apple Silicon - mlx-whisper

    OUTPUT_DIR=$(mktemp -d)
uvx --from mlx-whisper mlx_whisper "/path/to/audio.m4a" \
  --model mlx-community/whisper-large-v3-turbo \
  --language en --output-format txt -o "$OUTPUT_DIR"

    Other platforms - openai-whisper (needs ffmpeg on PATH; uses the turbo model by default; CPU-only is slow on long files)

    OUTPUT_DIR=$(mktemp -d)
uvx --from openai-whisper whisper "/path/to/audio.m4a" \
  --model turbo --language en --output_format txt -o "$OUTPUT_DIR"

    Note the flag spelling differs: --output-format for mlx-whisper, --output_format for openai-whisper. Use srt or vtt instead of txt when the user wants subtitles, or all for every format.

    Done when $OUTPUT_DIR/<input-basename>.txt exists and is non-empty.

  6. Deliver the transcript. Print the full verbatim text in the conversation - summarise only when asked. When it runs past about 2000 words, offer to save it as a .txt next to the source file.