No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by NevaMind-AI · Codex Skill · ★ 457
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memUBot safe to install? View the security audit →
memU Bot The Enterprise-Ready OpenClaw.Your Proactive AI Assistant That Remembers Everything. Why memU Bot • Memory • Enterprise • vs OpenClaw • Quick Start • Platforms • Skills & MCP • Contributing ⭐️ Star the repository If you find memU Bot useful or interesting, a GitHub Star ⭐️ would be greatly appreciat
| Stars | 457 |
| Forks | 51 |
| Language | TypeScript |
| Category | Codex Skill |
| License | AGPL-3.0 |
| Quality Score | 59.630027630397/100 |
| Open Issues | 15 |
| Last Updated | 2026-08-02 |
| Created | 2026-01-27 |
| Platforms | node |
| Est. Tokens | ~17k |
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memUBot is The Enterprise-Ready OpenClaw. Your Proactive AI Assistant That Remembers Everything. It is categorized as a Codex Skill with 457 GitHub stars.
memUBot is primarily written in TypeScript. It covers topics such as agent, agent-memory, openclaw.
You can find installation instructions and usage details in the memUBot GitHub repository at github.com/NevaMind-AI/memUBot. The project has 457 stars and 51 forks, indicating an active community.
memUBot is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to memUBot on Agent Skills Hub include AgentChat, memorix, swarmclaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: