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gif-sticker-makergif 贴纸制作器

Agent Skill

gif-sticker-maker 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

22,470

周安装

909

GitHub Stars

11,394

下载量

7,054
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:gif-sticker-maker(gif 贴纸制作器)
来源仓库:https://github.com/minimax-ai/skills
仓库路径:skills/gif-sticker-maker
安装命令:
npx skills add https://github.com/minimax-ai/skills --skill gif-sticker-maker
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/minimax-ai/skills --skill gif-sticker-maker

简介

gif-sticker-maker 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在开发协作中整理相关事项。

  • 适用于围绕仓库状态、代码变更或协作事项进行信息整理和分析的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、维护状态,注意是否触发联网或文件操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

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:

  1. Python venv is activated with dependencies from requirements.txt installed
  2. MINIMAX_API_KEY is exported (e.g. export MINIMAX_API_KEY='your-key')
  3. 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

  1. Analyze the user's photo — identify subject type (person / animal / object / logo).
  2. For each of the 4 stickers, build a prompt from image-prompt-template.txt by filling {action} and {caption}.
  3. If subject is a person: pass --subject-ref <user_photo_path> so the generated figurine preserves the person's actual facial likeness.
  4. 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):

  1. Brief status line (e.g. "4 stickers created:")
  2. <deliver_assets> block with all GIF files
  3. 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

#ActionFilename IDAnimation
1Happy wavinghiWave hand, slight head tilt
2Laughing hardlaughShake with laughter, eyes squint
3Crying tearscryTears stream, body trembles
4Heart gestureloveHeart 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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.25%
按下载量换算2,345

Claude

32.61%
按下载量换算2,300

Cursor

19.01%
按下载量换算1,341

Gemini CLI

9.45%
按下载量换算667

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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