by atria-asi · LLM Plugin · ★ 620
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. 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 | 620 |
| Forks | 33 |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 55/100 |
| Open Issues | 8 |
| Last Updated | 2026-10-23 |
| Created | 2026-09-12 |
| Est. Tokens | ~20k |
中文 | English Atria Dawn From Research Questions to Verifiable Results <img alt="Website" src="https://img.shields.io/badge/Website-atria--asi.ai-1A73E8?logo=goo
These tools work well together with Atria-Dawn for enhanced workflows:
Looking for a Atria-Dawn alternative? If you're comparing Atria-Dawn with other llm plugin tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi C
The LLM Anti-Framework
AIlice is a fully autonomous, general-purpose AI agent.
Shell and coding agent on mcp clients
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allow
Explore other popular llm plugin 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: