# dokoro

> dokoro — bypawel-dokoro. Use this tool when you need to manage multi-layer agent memory for various MCP clients, including Claude Code, to solve problems related to working, episodic, semantic, procedural, and affective memory storage and retrieval. It provides a comprehensive interface for inputs and outputs, enabling seamless integration with git and other MCP clients. This tool is ideal for use cases requiring advanced memory management and knowledge retention in AI agents.

Canonical page: https://skillsregistry.net/skills/bypawel-dokoro  
JSON: https://api.skillsregistry.net/v1/skills/bypawel-dokoro

## Description

Multi-layer agent memory MCP server — working, episodic, semantic, procedural & affective — for Claude Code and any MCP client.

## Trust

- **Trust score (0–1):** 0.55
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/byPawel/dokoro)

## 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": "bypawel-dokoro"
    }
  }
}
```

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