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 icebaker · Claude Skill · ★ 126
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ruby-nano-bots safe to install? View the security audit →
Nano Bots 💎 🤖 An implementation of the Nano Bots specification with support for Anthropic Claude, Cohere Command, Google Gemini, Maritaca AI Sabiá, Mistral AI, Ollama, OpenAI ChatGPT, and others, with support for calling tools (functions). https://user-images.githubusercontent.com/113217272/238141567-c58a240c-7b67-4b3b-864a-0f49bbf6e22f.mp4 TL;DR and Quick Start ruby require
| Stars | 126 |
| Forks | 14 |
| Language | Lua |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 70.0659387974935/100 |
| Open Issues | 7 |
| Last Updated | 2024-06-23 |
| Created | 2023-05-11 |
| Platforms | claude-code, gemini |
| Est. Tokens | ~33k |
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ruby-nano-bots is Ruby Implementation of Nano Bots: small, AI-powered bots that can be easily shared as a single file, designed to support multiple providers such as Anthropic Claude, Cohere Command, Google Gemini, Mar. It is categorized as a Claude Skill with 126 GitHub stars.
ruby-nano-bots is primarily written in Lua. It covers topics such as anthropic, anthropic-claude, chatgpt.
You can find installation instructions and usage details in the ruby-nano-bots GitHub repository at github.com/icebaker/ruby-nano-bots. The project has 126 stars and 14 forks, indicating an active community.
ruby-nano-bots is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ruby-nano-bots on Agent Skills Hub include cai, unsaged, tinychat. 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: