# undersheet

> undersheet — ubgb-undersheet. Use this tool when you need to maintain persistent memory for OpenClaw agents across multiple platforms, solving issues of data loss and inconsistency. Undersheet provides a unified thread memory solution, accepting input from various sources and outputting consistent data. It is ideal for use cases requiring cross-platform data persistence, such as tracking conversations or maintaining agent state on Moltbook, Hacker News, Reddit, Discord, and Twitter.

Canonical page: https://skillsregistry.net/skills/ubgb-undersheet  
JSON: https://api.skillsregistry.net/v1/skills/ubgb-undersheet

## Description

Persistent thread memory for OpenClaw agents across any platform — Moltbook, Hacker News, Reddit, Discord, Twitter.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-05-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** communication
- **Updated:** 2026-09-25

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/ubgb-undersheet)

## 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": "ubgb-undersheet"
    }
  }
}
```

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