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 iamtouchskyer · MCP Server · ★ 141
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memex safe to install? View the security audit →
memex Persistent memory for AI coding agents. Your agent remembers what it learned across sessions. English Español English Every time your AI agent finishes a task, it saves insights as atomic knowledge cards with . Next session, it recalls relevant cards before starting work — building on what it already knows instead of starting from scratch. No vector database, no embeddings — just markdown files your agent (and you) can read. Supported platforms All platforms share the same directory. A card written in Claude Code is instantly available in Cursor, Codex, or any other client. Prerequisites VS Code / Co
| Stars | 141 |
| Forks | 40 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.6906755063653/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-08 |
| Created | 2026-03-20 |
| Platforms | claude-code, cli, codex, mcp, node, vscode |
| Est. Tokens | ~18k |
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memex is Zettelkasten-based persistent memory for AI coding agents. Works with Claude Code, Cursor, VS Code Copilot, Codex, Windsurf & any MCP client. No vector DB — just markdown + git sync.. It is categorized as a MCP Server with 141 GitHub stars.
memex is primarily written in TypeScript. It covers topics such as agent-memory, ai-agent, ai-coding.
You can find installation instructions and usage details in the memex GitHub repository at github.com/iamtouchskyer/memex. The project has 141 stars and 40 forks, indicating an active community.
memex is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to memex on Agent Skills Hub include Overture, roampal-core, compose-skill. 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: