# offline-llama

> offline-llama — and-ray-m-offline-llama. Use this tool when you need to run AI models independently of internet connectivity, enabling continuous operation in offline environments. It solves problems of internet dependency and data privacy by autonomously managing local Llama models. The offline-llama tool accepts local model inputs and outputs predictions, making it ideal for use cases requiring self-sustaining AI capabilities.

Canonical page: https://skillsregistry.net/skills/and-ray-m-offline-llama  
JSON: https://api.skillsregistry.net/v1/skills/and-ray-m-offline-llama

## Description

Autonomously manage and use local Ollama models for continuous operation without internet dependency.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-05-20

## 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/and-ray-m-offline-llama)

## 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": "and-ray-m-offline-llama"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/and-ray-m-offline-llama` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/and-ray-m-offline-llama/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