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 munkim · Agent Tool · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is monoid safe to install? View the security audit →
Turn your APIs into AI Agents 🧐 What is Monoid? Monoid is a platform where you can build AI Agents directly on top of your APIs. Skip writing boilerplate code around LLMs and simply focus on optimizing your APIs and customizing your Agents. By giving Agents access to your APIs, they can: 🕵️♂️ Fetch relevant context about your problem or the user's request 🔁 Take actions on users' behalf This platform has several features to make it painless to create AI Agents: 🔌 Plug and play with different LLMs and Agent Types with the click of a button 📬 Postman-like interface for turning your APIs into Actions, where you can choose which parameters the Agent controls 🏖️ Action Sandbox to "talk" to your API in natural language, where you can simulate an Agent who only has one Action 🤖 Agent Sandbox to simulate and test your AI Agent before you deploy it 🪆 Use Agents as Actions within other Agents, so that they can collaborate and solve more complex problems 🤝 Action Hub and Agent Hub to allow the community to share its creations and build off each other's work [ servers. Ships with declarative tools/resources,
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monoid is Turn APIs into AI Agents. It is categorized as a Agent Tool with 52 GitHub stars.
monoid is primarily written in TypeScript. It covers topics such as ai, apis, hub.
You can find installation instructions and usage details in the monoid GitHub repository at github.com/munkim/monoid. The project has 52 stars and 17 forks, indicating an active community.
monoid is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to monoid on Agent Skills Hub include Awesome-AI-For-Security, jentic-mini, mcp-ts-template. 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: