# In-Memoria

> In-Memoria — zsilvestri63-in-memoria. Use this tool when you need to develop and deploy persistent intelligence infrastructure for AI agents, solving problems of knowledge retention and sharing across applications. In-Memoria provides a git-based interface for inputting and outputting AI models, data, and knowledge graphs, enabling seamless collaboration and version control. Ideal for use cases requiring long-term memory and incremental learning in AI systems.

Canonical page: https://skillsregistry.net/skills/zsilvestri63-in-memoria  
JSON: https://api.skillsregistry.net/v1/skills/zsilvestri63-in-memoria

## Description

Persistent Intelligence Infrastructure for AI Agents

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-10-18

## Source

- **Source listing:** [GitHub](https://github.com/zsilvestri63/In-Memoria)

## 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": "zsilvestri63-in-memoria"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/zsilvestri63-in-memoria` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/zsilvestri63-in-memoria/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