# experiments-ml

> experiments-ml — romanthekat-experiments-ml. Use this tool when you need to design and run simple machine learning experiments with Multi-Component Protocols (MCPs) and agentic frameworks, solving problems in automated decision-making and agent-based systems. It takes in experimental parameters and code repositories as inputs and outputs results and insights, utilizing git for version control. Ideal for researchers and developers in AI and machine learning fields requiring a flexible experimentation framework.

Canonical page: https://skillsregistry.net/skills/romanthekat-experiments-ml  
JSON: https://api.skillsregistry.net/v1/skills/romanthekat-experiments-ml

## Description

Simple experiments with MCPs and agentic frameworks

## Trust

- **Trust score (0–1):** 0.91
- **Verification tier:** verified
- **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/romanthekat/experiments-ml)

## 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": "romanthekat-experiments-ml"
    }
  }
}
```

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