by Avinash-jetwani · MCP Server · ★ 130
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install -g jevmem
claude plugin install jevmem@jevmemTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 130 |
| Forks | 9 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 5 |
| Last Updated | 2026-10-19 |
| Created | 2026-09-22 |
| Platforms | claude-code, codex, mcp, node |
| Est. Tokens | ~17k |
jevmem Automatic project memory for Claude Code. Also works with Cursor and Codex. What it does https://github.com/user-attachments/assets/ed77849e-db1c-4c05-9ad8-4cab0b3968a2 Saves decisions, constraints, bugs and todos from your Claude Code chats into , automatically. When you change your mind, the old line is marked superseded, not deleted.
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Overture is an open-source, locally running web interface delivered as an MCP (Model Context Protocol) server
Local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev.
Reusable project context and specialist workflows for the AI tools you already use. Agents, skills, rules and
Project memory for long-running AI work across agents, models, and sessions. Keep context, decisions, progress
Compiles AI agent traces and truns them into reusable context.
Compile completed AI agent runs into reusable, cited context.
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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: