by Knuckles92 · MCP Server · ★ 248
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/Knuckles92/OpenWhisperTaken from the project's README on GitHub.
Flagged: sudo usage. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 248 |
| Forks | 41 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67/100 |
| Last Updated | 2026-11-07 |
| Created | 2024-11-25 |
| Platforms | mcp, python |
| Est. Tokens | ~19k |
OpenWhisper A desktop app for dictation, audio-file transcription, and meeting notes on Windows, macOS, and Linux. Transcribe with local speech models, a speech server you run, or a cloud API, then optionally use AI, including models you run yourself, to clean up the text.
Looking for a OpenWhisper alternative? If you're comparing OpenWhisper with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Open-source AI voice typing for macOS, Windows, and Linux. Press a hotkey, speak naturally, get polished text
Voice dictation menu-bar app for macOS. Press a hotkey, speak, transcript appears at the cursor. Whisper runs
Open-source AI meeting copilot - real-time transcription, echo cancellation, and AI assistance. Captures syste
System audio capture + multi-provider ASR + local-first AI review workspace. Floating live captions, 12 ASR ba
Self-hosted meeting notes with no bot in the call. Records Meet, Teams and Zoom from the browser, transcribes
Kesha listens and talks back: local speech to text and text to speech for agents and scripts, fastest on Apple
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: