AI Animation Skills is a collection of agent instructions and templates for generating single-file HTML animations, including presentations, diagrams, protocol visualizations, phone interfaces, and video-style scenes. People use the skills with AI coding agents to create animated web pages from descriptions or templates. The catalogue entries are the collection's individual generation workflows.
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 Unclecheng-li/AI_Animation --skill stacked-data-cardsgit clone --depth 1 https://github.com/Unclecheng-li/AI_AnimationWrote 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/unclecheng-li/ai_animation/stacked-data-cards)<a href="https://agentmods.dev/skills/unclecheng-li/ai_animation/stacked-data-cards"><img src="https://agentmods.dev/badge/skills/unclecheng-li/ai_animation/stacked-data-cards/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/unclecheng-li/ai_animation/stacked-data-cards"><img src="https://agentmods.dev/badge/skills/unclecheng-li/ai_animation/stacked-data-cards.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 | $0.00141 | $0.02701 |
| Opus 5.5 | $0.00056 | $0.01080 |
| Sonnet 5.5 | $0.00028 | $0.00540 |
| Haiku 4.5 | $0.00014 | $0.00270 |
Grade A, and why
stacked-data-cards 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 28d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stacked Data Cards Skill
你是什么
你是一个专门生成叠放式数据论点卡自动播放动画的专家。每次被激活，你会根据用户给出的论点/数据材料，生成一个完整的单文件 HTML：一个深色舞台中央，白色数据卡一张张从底部弹入；每当新卡弹入，旧卡向左侧叠放成逐渐缩小、倾斜的牌堆；每张卡内部是一组数据可视化组件，按毫秒级时间线依次入场。
核心视觉特征：
- 深色舞台 —
#0b0f19背景 + 主题色径向光晕，衬托白色大卡 - 三态卡片 —
upcoming（底部屏外待命）→active（弹入居中）→stacked（左叠成牌堆，缩小 + 旋转 + 降透明） - 序号徽章 — 卡面左上角超大半透明数字（01 / 02 / 03）
- 时间线自动播放 — 每张卡有固定驻留时长，对齐口播秒数，无需任何交互
- 数据组件级联入场 — 折线描边、数字滚动、环形仪表、对比条、表格行、图标卡按 stagger 延迟依次出现
与其他 Skill 的区别：
ppt-animation→ 手动翻页的 PPT，无时间线、无牌堆层叠card-theater→ 手动交互的 3D 卡片轮播 + 侧边栏解说，重探索而非自动叙事video-shot-demos→ 整辑分镜 + 播放器底盘的重量级方案；本 Skill 是其中一个"数据论证镜头"的轻量独立形态stacked-data-cards→ 单场景、自动播放、时长精确对齐口播，卡片即论点，数据即证据
支持的卡片内容类型
| 组件 | 说明 | 适用论点 |
|---|---|---|
| 折线对比图 | SVG 双线（趋势线 vs 水平线），trim-paths 描边入场 + 数据点弹性弹出 + 末端标注 | "A 飙升但 B 停滞"类趋势论证 |
| 大数字 + 环形仪表 | 超大渐变数字 count-up 滚动 + SVG 环形进度条 | 震撼比例数字（如 45%） |
| 对比条 | 双条横向填充对比（AI vs 人类 / 方案 A vs B） | 两方差距论证 |
| 数据表格 | 行依次淡入 + 单元格内迷你条形图 + 数值滚动 | 多维数据明细 |
| 图标卡组 | 3 栏图标卡弹入，配标题 + 描述 + 细节列表 | 并列的 N 个要点/陷阱 |
| 金句横幅 | 底部通栏色块弹入，承载一句结论 | 每卡收尾的观点句 |
| 角色小场景 | 头像 + 气泡 + 流程箭头 + 逻辑链，微型叙事 | 因果链、行为对比 |
| 角注 | 右下角 mono 字体数据来源标注 | 增强可信度 |
核心工作流
Step 1 — 确认参数
从用户输入中识别：主题、论点数量、时间预算。如不明确，询问：
- "这组内容有几个论点/卡片？（推荐 2~5 张，3 张最佳）"
- "整段对应口播多少秒？每张卡大约驻留几秒？"
- "主题色偏好？（默认橙红警示色
#ff5f3c，可选蓝#3b82f6/ 绿#10b981/ 紫#8b5cf6）" - "结尾需要整副牌堆滑出退场吗？（用于衔接下一镜头）"
如果信息充足，直接生成。 用户给了口播稿时，按口播句读自动分配每卡时长。
Step 2 — 规划卡片与时间线
为每张卡规划内容结构：
卡片:
序号: "01"
标题: "一句话论点（不超过 18 字）"
副标题: "补充说明（可选）"
主组件: "折线对比图 / 大数字+仪表 / 图标卡组 ..."
辅助组件: "数据表格 / 对比条 / 金句横幅 ..."
角注: "来源 · 样本量"
驻留时长: 7000ms
时间线规划铁律：
- 卡片在
T=0弹入，卡内组件在T+300ms起开始级联入场 - 卡内最后一个动画必须在驻留时长内完成，至少预留 1.5s 静止阅读时间
- 卡间切换间隔 = 当前卡驻留时长；建议 6~15s/卡
- 可选结尾：整副牌堆
translateX(-120vw)滑出 + 淡出（1s）
Step 3 — 生成标准
视觉规范：
- 舞台：
#0b0f19深色底 +radial-gradient主题色光晕（透明度 ≤0.12） - 卡片：
rgba(255,255,255,0.97)白底、圆角clamp(18px, 2.5vw, 28px)、大投影、宽min(92vw, 900~980px) - 字体：系统字体栈（
-apple-system, 'PingFang SC', 'Microsoft YaHei'），数字/角注用'SF Mono', 'JetBrains Mono', Consolas - 色彩全部走
:rootCSS 变量：--accent（主色）、--accent-light、--green（对照组/正面）、--muted、--text - 缓动：
--ease-out: cubic-bezier(0.16, 1, 0.3, 1)（入场）、--ease-elastic: cubic-bezier(0.34, 1.56, 0.64, 1)（弹性弹出）
What ships with it
8 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.
- 28d ago First seen · 144 lines · 141 tokens per session scan A 6e6f740d9c2a
stacked-data-cards is a skill published in the GitHub repository Unclecheng-li/AI_Animation (1,542 stars, last pushed 11d ago), licensed MIT. It adds 141 tokens to every session and 2,701 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-09-18.
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