# DBX-Database-Extreme

> DBX-Database-Extreme — vanshjain-0702-dbx-database-extreme. Use this tool when you need to securely manage multi-tenant data for AI agents, requiring high-density in-memory storage and kernel-level physical isolation. DBX solves the problem of securing sensitive data across multiple tenants by enforcing per-tenant isolation, providing a reliable and secure memory layer. It takes in data inputs and outputs secured, isolated data storage, ideal for use cases where data privacy and security are paramount.

Canonical page: https://skillsregistry.net/skills/vanshjain-0702-dbx-database-extreme  
JSON: https://api.skillsregistry.net/v1/skills/vanshjain-0702-dbx-database-extreme

## Description

DBX is an open-source, high-density in-memory database engine built in Go, engineered specifically to serve as the secure memory layer for multi-tenant AI agents.   Unlike general-purpose databases that rely on logical access controls, DBX enforces **per-tenant physical isolation** at the kernel level. By leveraging Linux Landlock and `SO_PEERCRED`

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/vanshjain-0702/DBX-Database-Extreme)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "vanshjain-0702-dbx-database-extreme"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/vanshjain-0702-dbx-database-extreme` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/vanshjain-0702-dbx-database-extreme/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt