# Metis

> Use this tool when you need to organize and recall research papers, field knowledge, and working history, and get answers cited from your own indexed library. Metis solves the problem of information overload and forgotten references by providing a persistent memory of your research, allowing for efficient retrieval of relevant information. It takes in your research library and outputs cited answers, making it ideal for researchers who need to quickly access and build upon their existing knowledge.

Canonical page: https://skillsregistry.net/skills/sveritg-metis  
JSON: https://api.skillsregistry.net/v1/skills/sveritg-metis

## Description

A second brain for researchers — gives Claude persistent memory of your papers, field, and working history, with answers cited from your own indexed library

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-06-12

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/qss2zor9ee)
- **Repository:** <https://github.com/SVerITG/Metis>

## 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": "sveritg-metis"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/sveritg-metis` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/sveritg-metis/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