# ai-memory-layer

> ai-memory-layer — nishantjlu-ai-memory-layer. Use this tool when you need to manage and optimize AI model memory usage, solving problems like memory overflow and slow performance. It takes in model architectures and training data as inputs and outputs optimized memory allocation configurations. Ideal for use cases where AI models require significant memory resources, such as large-scale deep learning applications.

Canonical page: https://skillsregistry.net/skills/nishantjlu-ai-memory-layer  
JSON: https://api.skillsregistry.net/v1/skills/nishantjlu-ai-memory-layer

## Trust

- **Trust score (0–1):** 0.62
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/NishantJLU/ai-memory-layer)

## 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": "nishantjlu-ai-memory-layer"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/nishantjlu-ai-memory-layer` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/nishantjlu-ai-memory-layer/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