by unibaseio · MCP Server · ★ 55
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install unibaseio-membase # hosted · Python 3.10+ · needs MEMBASE_API_KEY
pip install 'unibaseio-membase[local]' # local · Python 3.12 or 3.13 · needs OPENAI_API_KEYTaken 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 | 55 |
| Forks | 13 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 64/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-20 |
| Created | 2025-03-31 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~15k |
Membase One memory. Every AI. Only yours. Teach it once. Every AI remembers. Website · Docs · Web app · Discord · X Membase is the memory layer for AI agents. It keeps one memory for everything you and your agents know, and every AI you use reads it. You stop re-explaining yourself each time you switch models or tools. Add. Facts, documents, web pages and chat exports from Claude or
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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: