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 NeoLi00 · Claude Skill · ★ 407
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memX safe to install? View the security audit →
English · 中文 · Architecture memX turns completed work into structured, searchable, self-maintained memory, then injects only the evidence an agent needs for the current query. It connects natively to Codex, Claude Code, and OpenClaw, and reaches any MCP-compatible client through the same local memory layer. Benchmarks Suite Scope R@3 success rate LongMemEval-S Long-context memory retrieval 94.2% Real engineering cases 30 cases, each with 20+ turns 100% Architecture Agent support Codex native hooks, MCP hidden by default <img src=
| Stars | 407 |
| Forks | 2 |
| Language | TypeScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 65.1396296722317/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-26 |
| Created | 2026-05-08 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~228k |
These tools work well together with memX for enhanced workflows:
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memX is memX: self-learning, self-maintaining memory plugin for AI agents; native support for claude code, codex, and openclaw. It is categorized as a Claude Skill with 407 GitHub stars.
memX is primarily written in TypeScript. It covers topics such as agent, agent-memory, claude-code.
You can find installation instructions and usage details in the memX GitHub repository at github.com/NeoLi00/memX. The project has 407 stars and 2 forks, indicating an active community.
memX is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to memX on Agent Skills Hub include memorix, remnic, hipocampus. 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: