# imaginator

> imaginator — smartcomputer-ai-imaginator. Use this tool when you need to generate images across multiple models and providers, comparing results in a grid-based interface where prompts are rows and models are columns. It solves problems of inconsistent image generation quality and lack of model comparability, allowing for efficient experimentation and analysis. Ideal for use cases requiring rapid testing and evaluation of image generation models, with git integration for version control and collaboration.

Canonical page: https://skillsregistry.net/skills/smartcomputer-ai-imaginator  
JSON: https://api.skillsregistry.net/v1/skills/smartcomputer-ai-imaginator

## Description

Run image generation experiments across different models and providers.  Work in a grid: prompts are rows, models are columns.

## Trust

- **Trust score (0–1):** 0.74
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** Apache-2.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/smartcomputer-ai/imaginator)

## 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": "smartcomputer-ai-imaginator"
    }
  }
}
```

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