nautilus-compass: Open memory & reliability layer for AI agents + independent judging/verification — by Nautilus Platform

by chunxiaoxx · MCP Server · ★ 1.3k

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

How to install nautilus-compass

pip install nautilus-compass
git clone https://github.com/chunxiaoxx/nautilus-compass ~/.claude/plugins/nautilus-compass

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 64/100

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. 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.

a2aagent-memoryagentic-aibenchmarkclaude-codecross-agentevaluationllmllm-as-judgelocal-first

Quick Facts

Stars1,254
Forks36
LanguagePython
CategoryMCP Server
Quality Score64/100
Open Issues32
Last Updated2026-10-18
Created2026-04-27
Platformsclaude-code, mcp, python
Est. Tokens~20k

About nautilus-compass

nautilus-compass Open-source memory & reliability layer for AI agents. Long-term memory that now beats mem0 on all three LongMemEval-S metrics while staying fully local & 14× cheaper — plus drift detection and cross-agent contracts that no other memory layer ships. Plugin for Claude Code / Desktop · Cline · Cursor · Continue.dev · Zed · any MCP client.

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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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