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 →
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OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problem with Reasoning Large Language Model Chinese Version 中文版本 Abstract With the rise of artificial intelligence (AI), applying large language models (LLMs) to mathematical problem-solving has attracted increasing attention. Most existing approaches attempt to improve Operations Research (OR) optimization problem-solving through prompt engineering or fine-tuning strategies for LLMs. However, these methods are fundamentally constrained by the limited capabilities of non-reasoning LLMs
| Stars | 98 |
| Forks | 18 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 42.692/100 |
| Open Issues | 2 |
| Last Updated | 2026-06-23 |
| Created | 2025-03-08 |
| Platforms | python |
| Est. Tokens | ~14k |
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or_llm_agent is OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM. It is categorized as a AI Tool with 98 GitHub stars.
or_llm_agent is primarily written in Python.
You can find installation instructions and usage details in the or_llm_agent GitHub repository at github.com/bwz96sco/or_llm_agent. The project has 98 stars and 18 forks, indicating an active community.
The top alternatives to or_llm_agent on Agent Skills Hub include SciToolAgent, redesigned-pancake, claude-code-voice-skill. 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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