Action figure generator

Skill SamurAIGPT/Generative-Media-Skills/library/visual/action-figure-generator

Multi-modal Generative Media Skills for AI Agents (Claude Code, Cursor, Gemini CLI). High-quality image, video, and audio generation powered by muapi.ai.

Install

npx -y skills add SamurAIGPT/Generative-Media-Skills --skill action-figure-generator

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What its author says it does

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Convert a photo of a person into a custom 3D action figure, complete with collectible toy packaging.

SKILL.md

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Action Figure Generator

Convert a photo of a person into a custom 3D action figure, complete with collectible toy packaging.

Inputs

Name	Type	Required	Default	Description
`user_image`	image_url	yes	—	A clear photo of the person to be turned into an action figure.
`toy_theme`	text	no	superhero	The theme of the action figure (e.g. superhero, doctor, explorer, sci-fi).

Steps

Phase A — Action Figure Creation

If {{user_image}} is not provided, ask the user to upload their photo.

Once the photo is available, submit the plan with ONE step to generate the action figure:

Action Figure Generation — muapi image edit (model=nano-banana-2-edit):
- Reference Image: {{user_image}}
- Prompt: A high-quality 3D stylized action figure based on the person in the input image. The action figure should maintain the person's likeness (face, hairstyle, skin tone) but be rendered as a plastic toy with visible joints and a semi-glossy finish. The character is dressed in a {{toy_theme}} outfit. The figure is displayed inside its original collectible cardboard and plastic blister packaging. The packaging has the person's name printed on it and features clean, modern graphic design. Soft studio lighting, realistic plastic textures, 8k resolution, cinematic look.
- Aspect ratio: 1:1 or 4:5

Present the generated action figure to the user. You can also suggest creating different themes (e.g., changing the character from a doctor to a space explorer).

Trigger Keywords

action figure, custom toy, toy packaging, character toy, collectible figure

Notes for the Executing Agent

This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.