by JosefAlbers · Agent Tool · ★ 79
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/JosefAlbers/Roy
Taken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 79 |
| Forks | 4 |
| Language | Jupyter Notebook |
| Category | Agent Tool |
| Quality Score | 62/100 |
| Last Updated | 2023-10-25 |
| Created | 2023-10-03 |
| Est. Tokens | ~25k |
Roy Roy is a lightweight alternative to for developing advanced multi-agent systems using language models. It aims to simplify and democratize the development of emergent collective intelligence. Features Model Agnostic: Use any LLM, no external APIs required. Defaults to a 4-bit quantized wizard-coder-python model for efficiency.
Looking for a Roy alternative? If you're comparing Roy with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
An introduction to the world of AI Agents
Autogen studio docker
A LLM powered agent for bioinformatics 🤖
Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dyna
Action library for AI Agent
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: