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 memorax-ai · Codex Skill · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memorax-code safe to install? View the security audit →
Never lose context. Never start over. Beyond code, it remembers how your architecture evolves and how your engineering unfolds. English · 简体中文 Make Every Interaction the Starting Point for the Next Coding agents are good at the task in front of them, but a new session often starts without the architecture, failed attempts, repository rules, or working preferences established before it. MemoraX Code gives Codex and Claude Code a shared memory lay
| Stars | 1,345 |
| Forks | 62 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 73.385376892804/100 |
| Open Issues | 16 |
| Last Updated | 2026-09-20 |
| Created | 2026-08-01 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~15k |
These tools work well together with memorax-code for enhanced workflows:
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memorax-code is A memory plugin for AI coding that turns engineering experience, repository knowledge, and your way of working into memory that remains useful in future tasks.. It is categorized as a Codex Skill with 1.3k GitHub stars.
memorax-code is primarily written in JavaScript. It covers topics such as ai-agents, claude-code, codex.
You can find installation instructions and usage details in the memorax-code GitHub repository at github.com/memorax-ai/memorax-code. The project has 1.3k stars and 62 forks, indicating an active community.
memorax-code is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to memorax-code on Agent Skills Hub include projectmem, Overture, deepcontext-mcp. 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: