No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by drakulavich · MCP Server · ★ 76
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is kesha-voice-kit safe to install? View the security audit →
Kesha Voice Kit Give your local tools and LLM agents a voice.Fast speech-to-text, text-to-speech, voice-activity detection, and language detection in one local-first CLI: Apple Silicon CoreML first, ONNX fallback on supported Linux/Windows builds. Transcribe locally — 25 languages, up to 19x faster than Whisper on Apple Silicon, 2.5x on CPU Speak back — text-to-speech in 9 languages Plug into agents — ship voice workflows as CLI commands, an MCP server, an OpenClaw skill, or a <a href
| Stars | 76 |
| Forks | 8 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.1891532444722/100 |
| Open Issues | 5 |
| Last Updated | 2026-10-03 |
| Created | 2026-04-06 |
| Platforms | cli, mcp, node |
| Est. Tokens | ~16k |
These tools work well together with kesha-voice-kit for enhanced workflows:
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kesha-voice-kit is Give your tools a voice — speech to text and back, 25 languages, up to ~19× faster than Whisper. On your machine.. It is categorized as a MCP Server with 76 GitHub stars.
kesha-voice-kit is primarily written in TypeScript. It covers topics such as apple-silicon, asr, bun.
You can find installation instructions and usage details in the kesha-voice-kit GitHub repository at github.com/drakulavich/kesha-voice-kit. The project has 76 stars and 8 forks, indicating an active community.
kesha-voice-kit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to kesha-voice-kit on Agent Skills Hub include DeLive, gno, maestro. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: