by user1342 · Agent Tool · ★ 137
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 137 |
| Forks | 24 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 56/100 |
| Open Issues | 9 |
| Last Updated | 2025-09-04 |
| Created | 2024-11-11 |
| Est. Tokens | ~53k |
Awesome LLM Red Teaming A comprehensive list of tools and resources for red-teaming large language models (LLMs), vulnerability research, and reverse engineering using various techniques like prompt manipulation, adversarial testing, and model interpretation.
Looking for a Awesome-LLM-Red-Teaming alternative? If you're comparing Awesome-LLM-Red-Teaming with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Curiso is an infinite canvas for your thoughts
User friendly CLI tool for AI tasks. Stop thinking about LLMs and prompts, start getting results!
An ML engineering plugin for your coding agents.
Open-source calculator for LLM system requirements.
Turn topics, links, and files into AI-generated research notebooks — summarize, explore, and ask anything.
A curated list of artificial intelligence (AI) tools.
Explore other popular agent tool tools:
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: