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 rubra-ai · Agent Tool · ★ 154
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中文  ｜  English  Rubra Rubra is a collection of open-weight, tool-calling LLMs. Rubra enhances the top open-weight large language models with tool-calling capability. The ability to call user-defined external tools in a deterministic manner while reasoning and chatting makes Rubra models ideal for agentic use cases. All models are enhanced from the top open-source LLMs with further post-training and methods that effectively teach instruct-tuned models new skills while mitigating catastrophic forgetting. For easy use, we extend popular inferencing projects, allowing you to run Rubra models easily. Enhanced Models 32,00
| Stars | 154 |
| Forks | 17 |
| Language | Makefile |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 58.4317161410857/100 |
| Open Issues | 7 |
| Last Updated | 2024-10-24 |
| Created | 2024-01-12 |
| Est. Tokens | ~214k |
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rubra is Open Weight, tool-calling LLMs. It is categorized as a Agent Tool with 154 GitHub stars.
rubra is primarily written in Makefile. It covers topics such as ai, ai-agents, function-calling.
You can find installation instructions and usage details in the rubra GitHub repository at github.com/rubra-ai/rubra. The project has 154 stars and 17 forks, indicating an active community.
rubra is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to rubra on Agent Skills Hub include llm-functions, dexto, openai-assistant-swarm. 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.
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