nailong-laugh-shorts

nailong-laugh-shorts is a skill for Claude Code, Codex from Morningstar202604/awesome-skillkit. It costs 124 tokens per session (1,819 once invoked), scanned A, original, Apache-2.0.

A production guide for making short meme videos featuring Nailong, a chubby yellow cartoon dragon, including generated images, animated laughter, audio, themed variants, and captions.

In plain words
What is it for?
Use it to create single videos or batches, animate laughing actions, replace human actions with the character, make costume or colour variants, and prepare captions for publishing.
Why use it?
It provides prompts and a repeatable process for turning the character or human footage into short laughing-dragon videos.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create single videos or batches, animate laughing actions, replace human actions with the character, make costume or colour variants, and prepare captions for publishing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/morningstar202604/awesome-skillkit/nailong-laugh-shorts
Install

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.

Any agent
npx skills add Morningstar202604/awesome-skillkit --skill nailong-laugh-shorts
Clone the repo
git clone --depth 1 https://github.com/Morningstar202604/awesome-skillkit

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for nailong-laugh-shorts

README.md
[![agentmods](https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/nailong-laugh-shorts/github.svg)](https://agentmods.dev/skills/morningstar202604/awesome-skillkit/nailong-laugh-shorts)
Your own site
<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.

agentmods 80×15 button for nailong-laugh-shorts

Your own site · 80×15
<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>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,819 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash f1e1dd2a0ebb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/scenarios/nailong-laugh-shorts/SKILL.md · 134 lines

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"。

Read the full file on GitHub · 134 lines

Changes

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.

  1. 12d ago First seen · 134 lines · 124 tokens per session scan A f1e1dd2a0ebb

Subscribe to this mod's changes

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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