# emem

> Use this tool when you need to access a reliable and cite-able memory of geographic locations, as it provides a content-addressed and signed record of every place on Earth, accepting location inputs and returning verified information outputs, ideal for applications requiring accurate and trustworthy geographic data. It solves problems related to data inconsistency and unreliability in geographic information systems. Use emem in contexts where verified location data is crucial, such as research, mapping, and navigation applications.

Canonical page: https://skillsregistry.net/skills/vortxai-emem  
JSON: https://api.skillsregistry.net/v1/skills/vortxai-emem

## Description

Cite-able, content-addressed, signed memory of every place on Earth.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/vortxai/emem)

## 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": "vortxai-emem"
    }
  }
}
```

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