by zju-vipa · Agent Tool · ★ 409
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 | 409 |
| Forks | 30 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66/100 |
| Open Issues | 3 |
| Last Updated | 2025-10-22 |
| Created | 2024-06-12 |
| Platforms | python |
| Est. Tokens | ~31422k |
Empowering Minecraft Agents with Open-World Skills Official codebase for the paper "Odyssey: Empowering Minecraft Agents with Open-World Skills". This codebase is based on the Voyager framework. Overview Abstract: Recent studies have delved into constructing generalist agents for open-world environments like Minecraft.
These tools work well together with Odyssey for enhanced workflows:
Looking for a Odyssey alternative? If you're comparing Odyssey 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.
Comprehensive Compilation of Real-World LLM & AI Agent Use Cases in Financial Services
A curated list of awesome resources, tools, research papers, and projects related to the concept of Large Lang
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
AIlice is a fully autonomous, general-purpose AI agent.
Shell and coding agent on mcp clients
This is the repository for the Tool Learning survey.
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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: