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 wanshuiyin · Claude Skill · ★ 521
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ARIS-in-AI-Offer safe to install? View the security audit →
ARIS-in-AI-Offer (ARIS in 秋招) Hoping to make your 秋招 (qiūzhāo, Chinese AI campus recruiting season) a little easier 🌱 📖 中文版 (Chinese version): READMECN.md 📚 Jump to a topic — 23 first-party cheat sheets across 7 categories + 1 community-contributed category: 🧠 General / Foundations · 🎯 Post-Training & Reasoning · 🏛️ LLM Architecture & Systems · 🌊 Generative Models — Theory & Tokenizers · 🎨 Generation Systems (Image / Video / 3D / Diffusion Post-Training) · 👁️ Multimodal · 🤖 Agents · 🦾 Embodied AI / 具身智能 Or browse the full 📚 Tutorial Index ↓ · jump to 🌐 ARIS-Homepage ↓. · · [ ML / LLM / diffusion / agent interview cheat sheets for AI 秋招 — generated by ARIS /interview-cheatsheet, rendered by /render-html into single-file HTML, reads anywhere — plus a CV→DB. It is categorized as a Claude Skill with 521 GitHub stars.
ARIS-in-AI-Offer is primarily written in Python. It covers topics such as ai-interview, aris, autumn-recruiting.
You can find installation instructions and usage details in the ARIS-in-AI-Offer GitHub repository at github.com/wanshuiyin/ARIS-in-AI-Offer. The project has 521 stars and 18 forks, indicating an active community.
ARIS-in-AI-Offer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ARIS-in-AI-Offer on Agent Skills Hub include create-llm, mlx-serve, aimirror. 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: