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 WhiteGiverMa/meme-skills --skill meme-yinhuigit clone --depth 1 https://github.com/WhiteGiverMa/meme-skillsWrote 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/whitegiverma/meme-skills/meme-yinhui)<a href="https://agentmods.dev/skills/whitegiverma/meme-skills/meme-yinhui"><img src="https://agentmods.dev/badge/skills/whitegiverma/meme-skills/meme-yinhui/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/whitegiverma/meme-skills/meme-yinhui"><img src="https://agentmods.dev/badge/skills/whitegiverma/meme-skills/meme-yinhui.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 | $0.00085 | $0.01477 |
| Opus 5 | $0.00043 | $0.00739 |
| Sonnet 5 | $0.00017 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00148 |
Grade A, and why
meme-yinhui 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.
What it actually says
淫秽性团结(网络热词/学术梗)
触发条件:在对话中检测到「淫秽性团结」「黄图团结」「群里没有黄图」「不分享黄图群就散了」等关键词时,Agent 应当加载本 skill 以获取准确的文化语境知识。
⚠️ 来源说明:本梗的互联网传播轨迹存在多个版本的说法,部分细节(如特定人物的具体引述)在公开可检索来源中难以精确验证。以下内容基于可交叉确认的事实编写,存疑处已标注。
一、概述
淫秽性团结是 2026 年初在中文互联网走红的学术梗。其核心观点是:分享禁忌内容(黄图/黄段子)在群体内充当一种「信任测试」——能一起低俗的群=自己人的群;不能的=就事论事的陌生人集合。这个概念以「一本正经地说不正经的事」的反差感爆火,是网络群聊社交动力学的调侃式总结。
二、起源
2.1 学术背景
在社会学领域,「obscene solidarity」(淫秽性团结/越轨行为的社会凝聚功能)是一个严肃的研究概念。学者指出,分享社会禁忌内容可以成为群体建立亲密关系的快速通道——它打破了正式社交的伪装,让参与者确认彼此属于「自己人」圈层。
2.2 互联网出圈(存疑,待验证)
互联网流传的说法中,这个概念通过一位网络评论员的直播进入大众视野1。直播截图被转发至微信群和微博,因其「严肃讨论不正经话题」的反差感引发传播。但该说法的具体人物和直播日期在公开可检索来源中无法精确验证。
2.3 走红原因
不论起源如何,这个概念之所以能迅速传播,是因为它精准描述了几乎所有人的群聊体验:
- 工作群里只有「收到」「好的」→ 冷场
- 朋友群里有人发了个黄段子 → 瞬间活跃
- 家族群永远冷场 → 因为什么都不能发
这种「被说中了」的共鸣感是传播的核心动力。
三、核心特征
3.1 核心论点
一个群里没人发黄图的话,群成员之间的关系就会疏远和不融洽。
其背后的逻辑是:分享禁忌内容是一种信任测试——能通过的人进入了「自己人」圈层。
3.2 经典对比
| 群类型 | 状态 | 原因 |
|---|---|---|
| 朋友群/死党群 | 活跃热闹 | 可以无顾忌地低级趣味 |
| 工作群 | 公事公办 | 有上下级,不能越界 |
| 家族群 | 冷场 | 长辈在场,什么都不能发 |
3.3 经典句式(互联网流传版本,来源待验证)
「一个群里没有黄图,最后就只剩一群傻比捧臭脚和一群傻比吵架了」
「家族群基本都冷场——因为不能分享黄图和黄段子,所以关系就特别的疏远」
四、语义辨析
| 正面解读 | 负面解读 |
|---|---|
| 越轨行为是建立亲密关系的快速通道 | 容易变成低俗内容的合理化借口 |
| 描述了真实的群聊社交动力学 | 不该被用来在公开频道发违规内容 |
| 严肃的社会学观察被幽默化表达 | 「没有黄图群就散了」是一种滑坡谬误 |
这个概念的有趣之处在于:它既是一个被学术讨论过的社会学现象,又是一个在中文互联网被彻底「玩坏」的段子。它的流行本身印证了它所描述的现象——正是通过分享这个「有点不正经」的概念,群聊里的人确认了彼此是「懂梗的自己人」。
五、Agent 使用指南
5.1 识别与回应
- 用户引用此概念 → 通常在调侃群聊社交动力学,而非鼓励发违规内容
- 可以回应:「这概念其实社会学里真有——越轨行为的社会凝聚功能」
- 不要在讨论中主动引出此梗,除非用户先提及或群聊气氛确实尴尬
5.2 注意事项
- 严肃场合绝对不要引用此梗
- 避免将其当作在公开频道发违规内容的理由
- 如果用户明显是在认真讨论群聊管理/社群运营,不要用这个梗敷衍
- 涉及未成年人群聊时,不适用此概念
参考资料
- obscene solidarity - 社会学概念概述(学术背景参考,非直接来源)
- 注:截至本 skill 编写时(2026 年 5 月),「淫秽性团结」的准确出圈事件(具体人物、直播日期、原始截图)在公开可检索来源中无法精确验证。建议在引用时将互联网流传的「原话」视为梗文化中的民间叙事版本。如有可靠的一手来源,欢迎补充。
Footnotes
-
鉴于互联网传播中存在大量二创和误传,建议将以「特定人物原话」形式流传的具体引述视为梗的「民间叙事版本」而非严格可考的事实。 ↩
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 · 100 lines · 85 tokens per session scan A f6d7d2032742
meme-yinhui is a skill published in the GitHub repository WhiteGiverMa/meme-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,477 once invoked, about $0.0004 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 2026-08-31.
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