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 oripress · Agent Tool · ★ 112
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AlgoTune safe to install? View the security audit →
Website | Paper How good are language models at coming up with new algorithms? To try to answer this, we built a benchmark, AlgoTune, comprised of 154 widely used math, physics, and computer science functions. For each function, the goal is to write code that produces the same outputs as the original function, while being faster. In addition to the benchmark, we also provide an agent, AlgoTuner, which allows language models to easily optimize code. ✨ New: AlgoTune can now be easily run on AWS with just an OpenRouter API key and AWS credentials. Try it out! 🚀 Quick Start Run AlgoTuner (stand-alone mode)
| Stars | 112 |
| Forks | 14 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.238284222667/100 |
| Open Issues | 2 |
| Last Updated | 2026-06-24 |
| Created | 2025-07-02 |
| Platforms | python |
| Est. Tokens | ~15k |
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AlgoTune is AlgoTune is a NeurIPS 2025 benchmark made up of 154 math, physics, and computer science problems. The goal is write code that solves each problem, and is faster than existing implementations.. It is categorized as a Agent Tool with 112 GitHub stars.
AlgoTune is primarily written in Python. It covers topics such as code-agent, code-generation, code-optimization.
You can find installation instructions and usage details in the AlgoTune GitHub repository at github.com/oripress/AlgoTune. The project has 112 stars and 14 forks, indicating an active community.
AlgoTune is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AlgoTune on Agent Skills Hub include DM-Code-Agent, PairCoder, Roy. 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: