# metcore

> metcore — hopenmind-metcore. Use this tool when you need to diagnose and analyze neural memory in large language models (LLMs) to identify potential issues and improve performance. The metcore engine provides a comprehensive set of tools, including a Python library and a 17-tool Model Context Protocol (MCP) server, to solve problems related to neural memory diagnostics and optimization. It accepts LLM models as input and outputs diagnostic reports and performance metrics, making it ideal for use cases where model reliability and efficiency are critical.

Canonical page: https://skillsregistry.net/skills/hopenmind-metcore  
JSON: https://api.skillsregistry.net/v1/skills/hopenmind-metcore

## Description

Neural memory diagnostics engine based on the Markov Embedding Theorem (MET). Includes a Python library, a 17-tool Model Context Protocol (MCP) server for LLMs, and a cross-platform desktop GUI.

## Trust

- **Trust score (0–1):** 0.59
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/hopenmind/metcore)

## 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": "hopenmind-metcore"
    }
  }
}
```

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