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 0xSojalSec · Agent Tool · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LLMs-local safe to install? View the security audit →
LLMs-local list of awesome platforms, tools, and resources run for LLMs locally Table of Contents Inference platforms Inference engines User Interfaces Large Language Models Explorers, Benchmarks, Leaderboards Model providers Specific models General purpose Coding Multimodal Image Audio Miscellaneous Tools Models Agent Frameworks Model Context Protocol Retrieval-Augmented Generation Coding Agents Computer Use Browser Automation Memory Management Testing, Evaluation, and Observability Research Training and Fine-tuning Miscellaneous Hardware Tutorials Models Prompt Engineering Context Engineering
| Stars | 1,190 |
| Forks | 135 |
| Category | Agent Tool |
| Quality Score | 48.3277902041529/100 |
| Open Issues | 9 |
| Last Updated | 2026-06-10 |
| Created | 2025-12-17 |
| Est. Tokens | ~4k |
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LLMs-local is list of awesome platforms, tools, and resources run for LLMs locally. It is categorized as a Agent Tool with 1.2k GitHub stars.
You can find installation instructions and usage details in the LLMs-local GitHub repository at github.com/0xSojalSec/LLMs-local. The project has 1.2k stars and 135 forks, indicating an active community.
The top alternatives to LLMs-local on Agent Skills Hub include excalidraw-diagram-skill, raptor, chinese-novelist-skill. 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: