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 mohitsoni48 · Claude Skill · ★ 278
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is TurboLLM safe to install? View the security audit →
TurboLLM Run any local LLM engine, auto-tuned to your GPU — with a polished web UI and an OpenAI/Anthropic-compatible API. Bring your own llama.cpp fork. No compiling. No Electron. No Python. Point Claude Code at your own machine in one command — fully offline. = 22" / <!-- Brand: shipped app icon web/p
| Stars | 278 |
| Forks | 40 |
| Language | TypeScript |
| Category | Claude Skill |
| Quality Score | 65.0085588868489/100 |
| Open Issues | 9 |
| Last Updated | 2026-10-03 |
| Created | 2026-06-12 |
| Platforms | browser, claude-code, node |
| Est. Tokens | ~19k |
These tools work well together with TurboLLM for enhanced workflows:
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TurboLLM is Run any local LLM engine, auto-tuned to your GPU — polished web UI + OpenAI/Anthropic-compatible API. Point Claude Code at your own machine in one command. No Electron, no Python, offline-first.. It is categorized as a Claude Skill with 278 GitHub stars.
TurboLLM is primarily written in TypeScript. It covers topics such as ai, anthropic-api, claude-code.
You can find installation instructions and usage details in the TurboLLM GitHub repository at github.com/mohitsoni48/TurboLLM. The project has 278 stars and 40 forks, indicating an active community.
The top alternatives to TurboLLM on Agent Skills Hub include flock, open-assistant-api, promptmask. 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: