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 hiyouga · Agent Tool · ★ 88
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🔒 Is MathRuler safe to install? View the security audit →
MathRuler A light-weight tool for evaluating LLMs in rule-based ways. Installation We use vLLM to accelerate the generation. Datasets MATH: 500 problems. GSM8K: 1319 problems. AIME24: 30 problems. AIME25: 30 problems. Generate Example output: Processed prompts: 100% 500/500 [00:36<00:00, 13.75it/s, est. speed input: 15765.84 toks/s, output: 5299.80 toks/s] Optional Arguments jsonpath (str): path to the eval file, defaults to savepath (str): path to the predicted file, defaults to nshot (int): number of few-shot examples, defaults to demosplit (str): split to build few-shot examples, defaults to system (str): system message for generation, defaults to temperature (float): decode temperature value, defaults to topp (float): decode top p value, defaults to maxtokens (int): maximum number of generated tokens, defaults to sam
| Stars | 88 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 65.3882738358056/100 |
| Open Issues | 1 |
| Last Updated | 2025-06-19 |
| Created | 2024-12-31 |
| Platforms | python |
| Est. Tokens | ~313k |
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MathRuler is A light-weight tool for evaluating LLMs in rule-based ways.. It is categorized as a Agent Tool with 88 GitHub stars.
MathRuler is primarily written in Python.
You can find installation instructions and usage details in the MathRuler GitHub repository at github.com/hiyouga/MathRuler. The project has 88 stars and 12 forks, indicating an active community.
MathRuler is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to MathRuler on Agent Skills Hub include tutor-skills, repren. 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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