# claudemem

> claudemem — zelinewang-claudemem. Use this tool when you need to retain information across multiple conversations, enabling seamless follow-ups and context-aware interactions. Claudemem solves the problem of knowledge loss between conversations, allowing for more personalized and efficient exchanges. It accepts input in the form of key-value pairs and outputs stored values based on given keys, making it ideal for use cases requiring persistent memory and contextual understanding.

Canonical page: https://skillsregistry.net/skills/zelinewang-claudemem  
JSON: https://api.skillsregistry.net/v1/skills/zelinewang-claudemem

## Description

Persistent memory that survives across conversations.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** other
- **Updated:** 2026-09-14

## Source

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

## 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": "zelinewang-claudemem"
    }
  }
}
```

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