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 KalyanKS-NLP · LLM Plugin · ★ 10.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is llm-engineer-toolkit safe to install? View the security audit →
👨🏻💻 LLM Engineer Toolkit This repository contains a curated list of 120+ LLM libraries category wise. 🚀 How to Read an AI Research Paper Effectively (AI Research Webinar) In this webinar, you will learn a simple yet effective step-by-step approach to read and understand AI research papers without getting overwhelmed. This webinar covers How to get a quick high level overview of the paper? How to dive deep into the paper step-by-step? Common mistakes to avoid while reading papers Skimming vs deep reading: when and how Webinar Format: 45 - 50 min session + 10 - 15 min Q&A Webinar Date & Time: April 8 2026, 9PM (IST) Speaker: Kalyan KS, NLP Consultant and Researcher, 1600+ Citations Register Related Repositories 👨🏻💻[LLM Interview Questions and Answers Hub](https://github.com/KalyanKS-NLP/LLM-Interview-Questio
| Stars | 10,873 |
| Forks | 1,707 |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 55.7221945270308/100 |
| Open Issues | 19 |
| Last Updated | 2026-09-24 |
| Created | 2025-03-09 |
| Est. Tokens | ~25k |
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llm-engineer-toolkit is A curated list of 120+ LLM libraries category wise.. It is categorized as a LLM Plugin with 10.9k GitHub stars.
You can find installation instructions and usage details in the llm-engineer-toolkit GitHub repository at github.com/KalyanKS-NLP/llm-engineer-toolkit. The project has 10.9k stars and 1707 forks, indicating an active community.
llm-engineer-toolkit is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to llm-engineer-toolkit on Agent Skills Hub include gateway, cursor-talk-to-figma-mcp, ruby_llm. 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: