# llm-mem

> llm-mem — vsndev3-llm-mem. Use this tool when you need to manage and optimize memory usage for Large Language Models (LLMs), solving issues with model scalability and performance. It provides a framework for efficient memory allocation and deallocation, taking in LLM model configurations as input and outputting optimized memory layouts. Ideal for use cases where LLMs require significant memory resources, such as during training or inference phases.

Canonical page: https://skillsregistry.net/skills/vsndev3-llm-mem  
JSON: https://api.skillsregistry.net/v1/skills/vsndev3-llm-mem

## Description

Memory framework for LLM

## Trust

- **Trust score (0–1):** 0.18
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/vsndev3/llm-mem)

## 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": "vsndev3-llm-mem"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/vsndev3-llm-mem` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/vsndev3-llm-mem/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