Gif Sticker Maker
Convert photos (people, pets, objects, logos) into 4 animated GIF stickers with captions.
Install
npx skills add MiniMax-AI/skills --skill gif-sticker-maker -g -a claude-code -y- Repository
- MiniMax-AI/skills13.8k
- Category
- Design
- Updated
- 7mo ago
- License
- MIT
- Context cost
- Lightabout 1,096 tokens
- Runs scripts
- Yes, review before use
What it does
Convert photos (people, pets, objects, logos) into 4 animated GIF stickers with captions. Use when: user wants to create cartoon stickers, GIF expressions, emoji packs, animated avatars, or convert photos to Funko Pop / Pop Mart blind box style animations. Triggers: sticker, GIF, cartoon, emoji, expression pack, avatar animation.
GIF Sticker Maker
Convert user photos into 4 animated GIF stickers (Funko Pop / Pop Mart style).
Style Spec
- Funko Pop / Pop Mart blind box 3D figurine
- C4D / Octane rendering quality
- White background, soft studio lighting
- Caption: black text + white outline, bottom of image
Prerequisites
Before starting any generation step, ensure:
- Python venv is activated with dependencies from requirements.txt installed
MINIMAX_API_KEYis exported (e.g.export MINIMAX_API_KEY='your-key')ffmpegis available on PATH (for Step 3 GIF conversion)
If any prerequisite is missing, set it up first. Do NOT proceed to generation without all three.
Workflow
Step 0: Collect Captions
Ask user (in their language):
"Would you like to customize the captions for your stickers, or use the defaults?"
- Custom: Collect 4 short captions (1, 3 words). Actions auto-match caption meaning.
- Default: Look up captions table by detected user language. Never mix languages.
Step 1: Generate 4 Static Sticker Images
Tool: scripts/minimax_image.py
- Analyze the user's photo, identify subject type (person / animal / object / logo).
- For each of the 4 stickers, build a prompt from image-prompt-template.txt by filling
{action}and{caption}. - If subject is a person: pass
--subject-ref <user_photo_path>so the generated figurine preserves the person's actual facial likeness. - Generate (all 4 are independent, run concurrently):
