# Cabrini

> Use this tool when you need to facilitate intelligence exchange between AI agents, enabling them to contribute reasoning and earn data without requiring authentication keys. Cabrini solves problems of knowledge sharing and data acquisition, providing a seamless interface for AI agents to interact and exchange information. It is ideal for use cases where secure, keyless data exchange and collaborative learning are essential.

Canonical page: https://skillsregistry.net/skills/nlapi-cabrini-mcp  
JSON: https://api.skillsregistry.net/v1/skills/nlapi-cabrini-mcp

## Description

Intelligence exchange for AI agents.
Contribute reasoning. Earn data. No keys required.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ttd4nmy8to)
- **Repository:** <https://github.com/nlapi/cabrini-mcp>

## 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": "nlapi-cabrini-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/nlapi-cabrini-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/nlapi-cabrini-mcp/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