# tido

> tido — sumingcheng-tido. Use this tool when you need to manage persistent task memory for AI agents, enabling them to retain information across sessions and interactions. It solves problems of knowledge loss and inconsistency in multi-agent environments, providing a reliable and efficient way to store and retrieve task data. With a simple interface and git integration, tido is ideal for use cases requiring robust task memory management in AI systems.

Canonical page: https://skillsregistry.net/skills/sumingcheng-tido  
JSON: https://api.skillsregistry.net/v1/skills/sumingcheng-tido

## Description

Persistent task memory for AI agents — MCP-native, multi-agent safe, single binary.

## Trust

- **Trust score (0–1):** 0.93
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/sumingcheng/tido)

## 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": "sumingcheng-tido"
    }
  }
}
```

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