gif-sticker-maker

SKILL.md

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_KEY is exported (e.g. export MINIMAX_API_KEY='your-key')

ffmpeg is 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):

python3 scripts/minimax_image.py "<prompt>" -o output/sticker_hi.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_laugh.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_cry.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_love.png --ratio 1:1 --subject-ref <photo>

--subject-ref only works for person subjects (API limitation: type=character).
For animals/objects/logos, omit the flag and rely on text description.

Step 2: Animate Each Image → Video

Tool: scripts/minimax_video.py with --image flag (image-to-video mode)

For each sticker image, build a prompt from video-prompt-template.txt, then:

python3 scripts/minimax_video.py "<prompt>" --image output/sticker_hi.png -o output/sticker_hi.mp4
python3 scripts/minimax_video.py "<prompt>" --image output/sticker_laugh.png -o output/sticker_laugh.mp4
python3 scripts/minimax_video.py "<prompt>" --image output/sticker_cry.png -o output/sticker_cry.mp4
python3 scripts/minimax_video.py "<prompt>" --image output/sticker_love.png -o output/sticker_love.mp4

All 4 calls are independent — run concurrently.

Step 3: Convert Videos → GIF

Tool: scripts/convert_mp4_to_gif.py

python3 scripts/convert_mp4_to_gif.py output/sticker_hi.mp4 output/sticker_laugh.mp4 output/sticker_cry.mp4 output/sticker_love.mp4

Outputs GIF files alongside each MP4 (e.g. sticker_hi.gif).

Step 4: Deliver

Output format (strict order):

Brief status line (e.g. "4 stickers created:")

<deliver_assets> block with all GIF files

NO text after deliver_assets

<deliver_assets>
<item><path>output/sticker_hi.gif</path></item>
<item><path>output/sticker_laugh.gif</path></item>
<item><path>output/sticker_cry.gif</path></item>
<item><path>output/sticker_love.gif</path></item>
</deliver_assets>

Default Actions

#
Action
Filename ID
Animation

1
Happy waving
hi
Wave hand, slight head tilt

2
Laughing hard
laugh
Shake with laughter, eyes squint

3
Crying tears
cry
Tears stream, body trembles

4
Heart gesture
love
Heart hands, eyes sparkle

See references/captions.md for multilingual caption defaults.

Rules

Detect user's language, all outputs follow it

Captions MUST come from captions.md matching user's language column — never mix languages

All image prompts must be in English regardless of user language (only caption text is localized)

<deliver_assets> must be LAST in response, no text after