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-assets-designergit 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-assets-designer)<a href="https://agentmods.dev/skills/guanyang/super-publisher/wechat-sticker-assets-designer"><img src="https://agentmods.dev/badge/skills/guanyang/super-publisher/wechat-sticker-assets-designer/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-assets-designer"><img src="https://agentmods.dev/badge/skills/guanyang/super-publisher/wechat-sticker-assets-designer.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 | $0.00056 | $0.02102 |
| Opus 5 | $0.00028 | $0.01051 |
| Sonnet 5 | $0.00011 | $0.00420 |
| Haiku 4.5 | $0.00006 | $0.00210 |
Grade A, and why
wechat-sticker-assets-designer 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WeChat Sticker Assets Designer (微信表情包配套素材设计师)
本 Skill 旨在协助 Agent 扮演专业的设计师角色，分析用户已有的表情包 IP 形象，并严格按照微信表情开放平台的规格标准，依次生成横幅 (Banner)、赞赏引导图和赞赏致谢图。
🎨 核心约束 (Global Constraints)
- IP 角色一致性 (Character Consistency)：
- 在生成所有 3 张图片时，必须严格保留并继承用户参考图中的 IP 核心特征（例如：发型、配饰、五官比例、经典色调等）。必须确保是同一个角色在不同场景下的演绎。
- 严禁包含 Emoji 表情：
- 所有生成的图像画面中，绝对不能出现任何 Emoji 符号（如 😂, ❤️, 👍 等）。
- 图画格式规范：
- 在构建绘图 Prompt 时，明确描述干净、扁平、矢量插画等有利于呈现 PNG 质感的风格词，确保边缘整洁。
- 单步依次输出 (Sequential Output)：
- 必须按照工作流，一张接一张独立调用工具生成并展示，严禁将多张图合并拼接成一张长图。
📏 图像生成规格 (Detailed Specs)
第一张：微信横幅 (Banner)
- 尺寸比例：
750x400像素 (宽长比约 1.875:1，Prompt 中设置比例，如--ar 15:8或1.875:1 aspect ratio)。 - 画面内容：
- 设计一个以该 IP 角色为核心、具有故事性和丰富细节的完整场景（如：角色在太空探险、在森林露营或在桌前创作等）。
- 色调活泼明朗。
- 背景约束 (极重要)：背景严禁使用白色，也严禁使用透明背景，必须有饱满的带色彩背景，以在微信浅色界面中形成清晰的视觉边界。
- 文字约束：画面中严禁出现任何文字或乱码。
第二张：赞赏引导图 (Solicitation Image)
- 尺寸比例：
750x560像素 (宽长比约 1.34:1，Prompt 中设置比例，如--ar 4:3或1.34:1 aspect ratio)。 - 画面内容：
- 角色需要展现出“期待支持”、“可爱卖萌”或“双手捧碗/求打赏”的神态与动作，增强赞赏吸引力。
- 背景约束：严禁使用白色或接近白色的浅色背景与边缘。背景必须为实色且深于微信页面底色。
- 文案要求：
- 必须在画面合适位置完美融合中文文字：“您的赞赏是我们最大的动力！”。
- 文字的字体、颜色和风格需与整体画面插画风格保持高度一致。
第三张：赞赏致谢图 (Acknowledgment Image)
- 尺寸比例：
750x750像素 (1:1 正方形比例，Prompt 中设置比例--ar 1:1)。 - 画面内容：
- 角色展现出“极其感激”、“庆祝”、“撒花欢呼”或“双手比心致谢”的开心状态。
- 画面色彩丰富饱满，营造出喜庆、欢快的互动氛围，激发分享欲。
- 背景约束：严禁使用白色或接近白色的浅色背景与边缘。
- 文案要求：
- 必须在画面合适位置完美融合中文文字：“感谢您的慷慨赞赏！”。
- 文字的排版和设计需与画面插画风格保持和谐。
🛠️ 使用指南 (Usage Guide)
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。
# 横幅 Banner (目标 750x400，采用居中 center 裁剪)
./skills/wechat-sticker-assets-designer/scripts/run.sh "PATH_TO_BANNER" --width 750 --height 400 --anchor center
# 赞赏引导图 (目标 750x560，采用靠上 top 裁剪以防切掉顶部文字)
./skills/wechat-sticker-assets-designer/scripts/run.sh "PATH_TO_SOLICITATION" --width 750 --height 560 --anchor top
# 赞赏致谢图 (目标 750x750，采用居中 center 缩放)
./skills/wechat-sticker-assets-designer/scripts/run.sh "PATH_TO_ACKNOWLEDGMENT" --width 750 --height 750 --anchor center
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 · 117 lines · 56 tokens per session scan A f3d9df23a9ce
wechat-sticker-assets-designer is a skill published in the GitHub repository guanyang/super-publisher (30 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 2,102 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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