# Painter (Canvas Drawing)

> Use this tool when you need to create or edit basic images through natural language commands, such as drawing shapes or exporting images as PNG or raw pixel data. It provides a simple interface for canvas manipulation, enabling AI assistants to generate visual content. Ideal for use cases where AI-driven image creation or editing is required, with inputs including drawing commands and outputs including PNG images or raw pixel data.

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

## Description

A drawing tool server that provides AI assistants with canvas manipulation capabilities through a simple interface. Built with TypeScript, it enables creating canvases, drawing filled rectangles with custom colors, and exporting the results as PNG images or raw pixel data. Particularly useful for AI assistants that need to create or edit basic images through natural language commands, as demonstrated by the included cowboy drawing example.

## Trust

- **Trust score (0–1):** 0.97
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/flrngel-painter)
- **Repository:** <https://github.com/flrngel/mcp-painter>

## 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": "flrngel-painter"
    }
  }
}
```

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