Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Text-To-Speech with MLX (Apple Silicon) and opensource models (default QWen3-TTS) locally.
Text-To-Speech with MLX (Apple Silicon) and opensource models (default QWen3-TTS) locally.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Text-To-Speech with MLX (Apple Silicon) and open-source models (default QWen3-TTS) locally. Free and Fast. No API key required. No server required.
mlx: macOS with Apple Silicon brew: used to install deps if not available
bash ${baseDir}/install.sh This script will use brew to install these CLI tools if not available: uv: install python package and run python script mlx_audio: do the real job
To generate audio from text, run this script: bash ${baseDir}/mlx-tts.sh "<text>"
Run the script: Pass the text to be spoken as an argument. Handle Output: The script will output a path to a audio file. Use the message tool to send the audio file to the user as an voice message: { "action": "send", "filePath": "<filepath>" } Example: User: "Say hello world" Agent: Runs bash path/to/mlx-tts.sh "hello world" Receives output: /tmp/folder/audio.ogg Calls message(action="send", filePath="/tmp/folder/audio.ogg", ...)
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.