垂直STEM解说视频，基于规范而非生成的动画代码。

从用该插件构建的HTTP解释器八秒钟内：一个时序图，一个节 变化，然后代码块一行一行地穿行。旁白推动节奏。

▶ 看一个完成的视频： 什么是卷积神经网络？ ——这是一篇通过该流程构建的完整解析视频，发布在YouTube Shorts上。

来自捆绑K-平均示例的帧：从实际数据集计算的聚类图， 注释调出，量化比较——而非这些想法的示意。

区别

大多数大型语言模型视频工具要求模型写动画代码，然后运行并希望它能 编译成看起来合适的东西。这就是为什么围绕它们的生态系统充满了 审核循环读取执行日志，以及为什么有公开排行榜来评分执行日志 通常每个模型都会制作一个工作动画。

stem-shorts 要求模型填写一个经过验证的规格。布局是固定的 压缩，渲染器是确定性Python，并且在检查之前先检查规范 平局。最糟糕的结果是一张可能更漂亮的图表。

stem-shorts

Vertical STEM explainer videos, built from a spec instead of generated animation code.

Eight seconds from an HTTP explainer built with this plugin: a sequence diagram, a section change, then a code block walked through line by line. Narration drives the timing.

▶ Watch a finished one: What is a convolutional neural network? — a full explainer published to YouTube Shorts, built with this pipeline.

Frames from the bundled K-Means example: clustering plots computed from an actual dataset, annotated callouts, a quantization comparison — not illustrations of those ideas.

The difference

Most LLM video tools ask the model to write animation code, then run it and hope it compiles into something that looks right. That is why the ecosystem around them is full of reviewer loops that read execution logs, and why there are public leaderboards scoring how often each model produces a working animation.

stem-shorts asks the model to fill in a spec that gets validated. The layout is a fixed contract, the renderers are deterministic Python, and the spec is checked before anything is drawn. The worst outcome is a chart that could be prettier. It is never a broken video.

Three things follow from that, which the code-generation approach struggles to give you:

• Vertical 1080×1920 for TikTok, Reels and Shorts — not a 16:9 lecture recording.
• Timing derived from the real audio. Each line’s narration is measured with ffprobe after it is generated, and the animation is built around those durations. Nothing is estimated.
• No LaTeX distribution. Formulas render through MathJax, so there is no texlive to install and no 4 GB of dependencies.

Quickstart

# In Claude Code
/plugin marketplace add TUDONG05/stem-shorts
/plugin install stem-shorts@stem-shorts

Install the two external tools the pipeline shells out to, then ask for a video:

pip install edge-tts matplotlib numpy   # narration + renderer
# FFmpeg: apt install ffmpeg  /  brew install ffmpeg

Make a video comparing K-Means and Fuzzy C-Means

The skill scaffolds the project, confirms the script with you, generates the narration, renders the visuals and formulas, and assembles the composition. It stops for your approval at each decision point unless you set AUTO_CREATE_VIDEO=1.

To run the bundled example directly instead, see examples/.

Requirements