by rzhub · Agent Tool · ★ 139
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 | 139 |
| Forks | 3 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-12 |
| Created | 2026-06-16 |
| Platforms | python |
| Est. Tokens | ~15k |
GateMem -- Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents GateMem evaluates whether memory-augmented LLM agents can remain useful, enforce access control, and honor deletion requests in multi-principal shared-memory environments.
These tools work well together with GateMem for enhanced workflows:
Looking for a GateMem alternative? If you're comparing GateMem 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.
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, ma
[ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?
Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language mo
A unified client for AI providers with built-in agent support.
LLM Agent that leverages cheminformatics tools to provide informed responses.
OpenChatBI is an intelligent chat-based BI tool powered by large language models, designed to help users query
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: