# memento

> memento — wmyung-memento. Use this tool when you need to manage and share knowledge across multiple AI agents, storing and retrieving facts in a SQLite database and tracking changes through a Git wiki. It solves the problem of agent memory and collaboration, providing a zero-dependency Python CLI interface for input and output. Ideal for use cases requiring a simple, dependency-free multi-agent memory system with Git version control.

Canonical page: https://skillsregistry.net/skills/wmyung-memento  
JSON: https://api.skillsregistry.net/v1/skills/wmyung-memento

## Description

Agent memory: SQLite fact store + Git wiki + keyword bridge. Multi-agent memory system for AI agents. No embeddings, no vector DB. Zero-dependency Python CLI with MCP server.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** database
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/wmyung/memento)

## 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": "wmyung-memento"
    }
  }
}
```

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