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 Morningstar202604/awesome-skillkit --skill nailong-laugh-shortsgit clone --depth 1 https://github.com/Morningstar202604/awesome-skillkitWrote 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/morningstar202604/awesome-skillkit/nailong-laugh-shorts)<a href="https://agentmods.dev/skills/morningstar202604/awesome-skillkit/nailong-laugh-shorts"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/nailong-laugh-shorts/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/morningstar202604/awesome-skillkit/nailong-laugh-shorts"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/nailong-laugh-shorts.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 | $0.00124 | $0.01819 |
| Opus 5 | $0.00062 | $0.00910 |
| Sonnet 5 | $0.00025 | $0.00364 |
| Haiku 4.5 | $0.00012 | $0.00182 |
Grade A, and why
nailong-laugh-shorts 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nailong Laugh Shorts ("大笑奶龙" production manual)
Risk note (one line, then we work): 公开发布使用该商业 IP 形象属未授权使用, 非盈利个人号现实后果通常是限流/下架/账号处罚而非诉讼,商用则必被追责—— 自行权衡;发布时勾选"内容由 AI 生成"能明显降低处置概率。
The look that makes people laugh, in order of importance: ①头小身大的扭曲比例 ②仰天捧腹的抖动 ③高饱和明黄 ④呆萌与癫狂的表情反差。
Inputs
| Input | Required | Default | Notes |
|---|---|---|---|
| pipeline | no | A | A=表情包成精(图→视频);B=真人动作套壳 |
| skin/theme | no | 原味黄 | 节日/职业/变色等变体主题 |
| episode_count | no | 1 |
系列化建议一次 ≥5 条 |
Ask ONCE only if user gave nothing at all:
要哪种做法?A:生成一张捧腹大笑形象让它动起来(简单快); B:拿真人魔性动作视频换成这个形象(更还原梗的扭曲感)。 可选:做几期、要不要换装系列。
Character description library (copy-paste prompts)
Base body (official-ish look):
一只圆滚滚的黄色卡通小恐龙,大大的白色椭圆肚皮,短小的四肢和尾巴, 呆萌的大眼睛,Q 版 3D 卡通渲染,高饱和明黄色,纯色背景,全身正面。
Laughing variant (the meme look):
同一只黄色小恐龙仰天捧腹大笑,头向后仰,两只短手抱着肚子, 肚皮剧烈抖动,眼睛笑成两条缝,嘴张到最大,身体比例夸张—— 头小肚子极大,动态模糊的抖动感。
Mutated proportions (the "奶蛙" distortion that makes it funnier):
同一角色但比例刻意失调:头部缩小、身体拉长放大,四肢细短乱蹬, 五官挤在脸下半部,扭曲滑稽,橡皮质感抖动。
Skin matrix ideas for series: 黄金圣衣版 / 西装上班版 / 春节红灯笼版 / 西瓜皮版 / 深夜emo关灯版。每张皮肤 = base prompt + 一句皮肤描述。
Pipeline A: 表情包成精(最快出片)
Step 1: 生成静态大笑图
用任意文生图工具跑上面的 laughing variant prompt。 Expected: 单角色、正面或微侧、肚子占比大、无多余肢体。 失败分支:多角色/肢体崩坏 → 加 "single character, simple pose" 重生成。
Step 2: 图生视频
把图喂给图生视频工具(即梦/可灵等),动作指令:
角色保持位置不变,仰天大笑,肚子剧烈抖动弹跳,身体前后摇摆, 循环动画。
Expected: 3–5 秒无缝循环感素材。失败分支:动作僵硬 → 改指令强调 "rubber-hose wobble, exaggerated squash and stretch" 再抽卡 1–2 次。
Step 3: 配笑声与字幕
笑声制作:自己对着手机录一段大笑 → 变声器处理(升调 20–30% + 轻微机械感
- 按四四拍断句),剪出 3–8 秒可循环版本。不要直接搬运别人视频里的原声 ——平台查重会判搬运限流,自制同款效果才是自己的资产。 剪辑:开头 0.5 秒内笑声炸响 → 视频循环 2–3 遍 → 大字标题压屏。
Pipeline B: 真人动作套壳(更还原原梗的扭曲感)
Step 1: 动作源
找一段魔性真人动作(军体拳、社会摇、广场舞、摔跤倒地)。自己拍最稳; 用网络素材注意只取动作参考、不保留任何人脸画面。
Step 2: 动作迁移
用支持视频生视频/动作迁移的工具(即梦、可灵等):参考图用 Step-A1 的 形象图 + 动作视频作驱动。Expected: 角色复刻动作且比例被 AI 拉歪—— 这种失控感正是原梗好笑的核心,别修它。 失败分支:完全不像原动作 → 换轮廓更简单的动作重跑;太像正常动画不搞笑 → prompt 加 "distorted proportions, head shrinking, belly expanding"。
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 · 134 lines · 124 tokens per session scan A f1e1dd2a0ebb
nailong-laugh-shorts is a skill published in the GitHub repository Morningstar202604/awesome-skillkit (1 stars, last pushed 2d ago), licensed Apache-2.0. It adds 124 tokens to every session and 1,819 once invoked, about $0.0006 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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