Best use case
openai-whisper is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Local speech-to-text with the Whisper CLI (no API key).
Teams using openai-whisper should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openai-whisper-andy27725/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How openai-whisper Compares
| Feature / Agent | openai-whisper | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Local speech-to-text with the Whisper CLI (no API key).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Whisper (CLI) Use `whisper` to transcribe audio locally. Quick start - `whisper /path/audio.mp3 --model medium --output_format txt --output_dir .` - `whisper /path/audio.m4a --task translate --output_format srt` Notes - Models download to `~/.cache/whisper` on first run. - `--model` defaults to `turbo` on this install. - Use smaller models for speed, larger for accuracy.
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