# memxplore

> memxplore — qingbo1011-memxplore. Use this tool when you need to explore and analyze agent memory for research and development purposes, solving problems related to memory optimization and debugging. It provides an executable reference implementation and workbench for experimentation, accepting input from git repositories and outputting insights into memory usage and performance. Ideal for use cases requiring in-depth memory analysis and optimization in AI agents.

Canonical page: https://skillsregistry.net/skills/qingbo1011-memxplore  
JSON: https://api.skillsregistry.net/v1/skills/qingbo1011-memxplore

## Description

Executable agent memory reference implementation and research workbench

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** Apache-2.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/qingbo1011/memxplore)

## 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": "qingbo1011-memxplore"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/qingbo1011-memxplore` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/qingbo1011-memxplore/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