Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add guanyang/super-publisher --skill wechat-sticker-makergit clone --depth 1 https://github.com/guanyang/super-publisherWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/guanyang/super-publisher/wechat-sticker-maker)<a href="https://agentmods.dev/skills/guanyang/super-publisher/wechat-sticker-maker"><img src="https://agentmods.dev/badge/skills/guanyang/super-publisher/wechat-sticker-maker/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/guanyang/super-publisher/wechat-sticker-maker"><img src="https://agentmods.dev/badge/skills/guanyang/super-publisher/wechat-sticker-maker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.01251 |
| Opus 5 | $0.00030 | $0.00626 |
| Sonnet 5 | $0.00012 | $0.00250 |
| Haiku 4.5 | $0.00006 | $0.00125 |
Grade A, and why
wechat-sticker-maker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
微信表情包制作工具 (WeChat Sticker Maker)
本 Skill 旨在帮助用户快速将设计好的网格拼图(如六宫格、九宫格、十二宫格)自动裁剪并生成符合微信表情开放平台规范的素材。
核心功能
- 自动裁剪:支持 2x3, 3x2, 3x3, 3x4, 4x3 等多种网格布局。
- 规范转换:
- 表情主图:统一调整为 240x240 像素 (PNG)。
- 聊天页图标:统一调整为 50x50 像素 (PNG)。
- 含义词生成:自动生成
meta.txt文件,预留“含义词”填写位置,方便批量管理。 - 信息模板生成:自动生成
info.txt文件,包含【表情名称】、【表情介绍】、【一句话简介】的填写模板及字数限制提示。 - 候选素材生成:自动提取第1张表情,生成符合规范的【头像/封面图候选】(240x240) 和 【聊天页图标候选】(50x50)。
- 自动命名:按照微信规范自动编号 (01, 02, ...)。
使用指南
1. 快速开始 (Quick Start)
无需手动安装依赖,直接运行脚本即可。工具会自动创建虚拟环境 (.venv) 并安装所需依赖。
# 基本用法:自动处理并生成
./skills/wechat-sticker-maker/scripts/run.sh /path/to/your/grid_image.png
# 常用选项:
# - 指定布局 (例如 3x3)
./skills/wechat-sticker-maker/scripts/run.sh /path/to/image.png --layout 3x3
# - 指定输出目录
./skills/wechat-sticker-maker/scripts/run.sh /path/to/image.png --output ./my_stickers
2. (可选) 手动安装
如果您希望手动管理环境:
python3 -m venv .venv
source .venv/bin/activate
pip install -r skills/wechat-sticker-maker/requirements.txt
python3 skills/wechat-sticker-maker/scripts/make_stickers.py ...
3. 输出结果
脚本将在指定的输出目录下生成两个子文件夹和多个文件。
- 默认输出路径:如果用户未指定,默认创建并输出到
output/stickers_[theme]目录(其中[theme]根据该表情包的具体主题进行替换,如output/stickers_cat)。 - 自定义输出路径:用户也可以通过
--output指定自定义输出目录。 - 输出内容:
main/: 存放 表情主图 (240x240)icon/: 存放 表情缩略图标 (50x50, 这里的icon指每张表情的缩略图,非聊天页单一图标)meta.txt: 含义词配置表 (格式:01.png [请输入表情含义])info.txt: 专辑信息模板 (包含名称、简介模板)cover_candidate.png: 封面图候选 (240x240, 取自第1张)chat_icon_candidate.png: 聊天页图标候选 (50x50, 取自第1张)
输出目录自定义: Agent 在调用脚本时应传入 --output 参数:如果用户指定了路径,则使用用户指定的路径;如果未指定,则默认使用 output/stickers_[theme] 格式的路径。生成完毕后,向用户呈报生成在该目录下的具体文件路径与文件预览。
重要:自动完善元数据 (Meta-Data Auto-Population)
当运行完本脚本后,Agent 必须根据所生成的表情包主题、画面内容或所使用的模板,自动修改并填充 info.txt 与 meta.txt 中的占位符:
- 补充
info.txt:- 【表情名称】:结合主题起一个生动的名字(不超过8个汉字,无标点)。
- 【表情介绍】:描述这套表情包的风格和适用场景(不超过80个汉字)。
- 【一句话简介】:提炼一个吸睛的宣传语(不超过11个汉字,无标点)。
- 补充
meta.txt:- 将每一行的
[请输入表情含义]替换为该表情图对应的具体动作或情绪含义(如:01.png 收到、02.png 摸鱼等,通常为 2-4 个字)。
- 将每一行的
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 79 lines · 60 tokens per session scan A 18b54a1f9cdc
wechat-sticker-maker is a skill published in the GitHub repository guanyang/super-publisher (30 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,251 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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