# Octomil

> Use this tool when you need to efficiently deploy and optimize machine learning models on edge devices, solving problems of costly and inefficient model integration. It takes in machine learning models and codebases as inputs, and outputs automated hardware-aware configurations and optimized code for local inference. Use Octomil when developing IoT or edge-based applications requiring enhanced privacy and reduced costs.

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

## Description

Manage, optimize, and deploy machine learning models to edge devices with automated hardware-aware configurations. Generate, review, and test code using local inference to reduce costs and enhance privacy. Benchmark model performance and scan codebases to identify the most efficient on-device integration points.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** devops-ci
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/octomil/octomil)

## 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": "octomil-octomil"
    }
  }
}
```

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

---
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