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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. Full audit →
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 | 1,816 |
| Forks | 79 |
| Category | Agent Tool |
| Quality Score | 42/100 |
| Open Issues | 5 |
| Last Updated | 2026-06-18 |
| Created | 2025-08-29 |
| Est. Tokens | ~14k |
Awesome-AgenticLLM-RL-Papers This is the Official repo for the survey paper: The Landscape of Agentic Reinforcement Learning for LLMs: A Survey ArXiv – https://arxiv.org/abs/2509.02547 HuggingFace – https://huggingface.co/papers/2509.02547 Citation Sec2.7 Agentic RL: Algorithms Clip corresponds to preventing the policy ratio from moving too far from 1 for ensuring stable updates.
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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.
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