by deep-symbolic-mathematics · Agent Tool · ★ 127
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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 | 127 |
| Forks | 12 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 69/100 |
| Open Issues | 4 |
| Last Updated | 2025-07-31 |
| Created | 2025-01-30 |
| Platforms | python |
| Est. Tokens | ~84k |
: Benchmark for Scientific Equation Discovery or Symbolic Regression with LLMs This is the official repository for the paper "LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models" (ICML 2025 Oral) Overview In this paper, we introduce LLM-SRBench, a comprehensive benchmark with $239$ challenging problems across four scientific domains specifically designed to...
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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: