EverMe: Open-source CLI and Agent plugin suite for EverMe — cross-device, cross-Agent personal memory for AI Agents

by EverMind-AI · MCP Server · ★ 74

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install EverMe

npm install -g @everme/cli

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 66/100

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.

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.

agent-pluginsai-memoryclaude-codecodexcursordigital-twindigital-twinsevermeevermindhermes

Quick Facts

Stars74
Forks14
LanguageJavaScript
CategoryMCP Server
LicenseApache-2.0
Quality Score66/100
Open Issues13
Last Updated2026-11-04
Created2026-05-27
Platformsclaude-code, cli, codex, mcp, node
Est. Tokens~16k

About EverMe

EverMe CLI Open-source CLI, MCP server, SDK, and Agent plugins for connecting AI Agents to EverMe. [!IMPORTANT] This repository contains only the open-source EverMe client toolchain and Agent integrations. The EverMe product, web application, and managed backend are separate and are not included in this repository. Product · Website · EverOS engine · Documentation · 中文 <su

Compatible Skills

These tools work well together with EverMe for enhanced workflows:

  • mnemon — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • mainline — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • membrane — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (55%)
  • membrane — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (55%)

EverMe alternative? Top 6 similar tools

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How this security grade is produced

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:

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