# Wax

> Wax — christopherkarani-wax. Use this tool when you need to optimize AI agent performance with a single-file, on-device memory layer, providing sub-millisecond response times and eliminating server dependencies. Wax solves latency and complexity issues in AI applications, particularly on Apple Silicon devices. It offers a simple, Metal-optimized interface with pure Swift implementation and git integration.

Canonical page: https://skillsregistry.net/skills/christopherkarani-wax  
JSON: https://api.skillsregistry.net/v1/skills/christopherkarani-wax

## Description

Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift

## Trust

- **Trust score (0–1):** 0.05
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/christopherkarani/Wax)

## 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": "christopherkarani-wax"
    }
  }
}
```

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