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 theaniketgiri · Agent Tool · ★ 358
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is create-llm safe to install? View the security audit →
create-llm CLI tool for scaffolding LLM Creation and training Create production-ready LLM training projects in seconds. Similar to create-next-app but for training custom language models. npm Package • Documentation • Report Bug • Request Feature Why create-llm? Train
| Stars | 358 |
| Forks | 47 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 77.7005657703303/100 |
| Open Issues | 3 |
| Last Updated | 2025-11-08 |
| Created | 2025-08-08 |
| Platforms | cli, node |
| Est. Tokens | ~31k |
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历年ICLR论文和开源项目合集，包含ICLR2021、ICLR2022、ICLR2023、ICLR2024、ICLR2025.
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Bilingual (中文+EN) ML / LLM / diffusion / agent interview cheat sheets for AI 秋招 — generated by ARIS /interview
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create-llm is The fastest way to build and start training your own LLM. CLI tool that scaffolds production-ready PyTorch training projects in seconds. Like create-next-app but for language models.. It is categorized as a Agent Tool with 358 GitHub stars.
create-llm is primarily written in TypeScript. It covers topics such as ai, cli, deep-learning.
You can find installation instructions and usage details in the create-llm GitHub repository at github.com/theaniketgiri/create-llm. The project has 358 stars and 47 forks, indicating an active community.
create-llm is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to create-llm on Agent Skills Hub include AITreasureBox, free-ai-resources-x, ICLR2025-Papers-with-Code. 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: