Vertical STEM 설명 동영상, 생성 된 애니메이션 코드 대신 spec에서 내장.

이 플러그인에 내장 된 HTTP 설명자에서 초 : 순서도, 섹션 변경, 다음 코드 블록을 통해 행을 통해 걸어. 항해.

▶ ** 완료된 것:** convolutional neural 네트워크는 무엇입니까? — YouTube Shorts에 게시 된 전체 설명자, 이 파이프라인에 내장.

• 번들 K-Means 예제의 프레임 : 실제 데이터 세트에서 계산 된 클러스터링 플로트, anotated callouts, 정량 비교 — 그 아이디어의 일러스트.*

차이

대부분의 LLM 비디오 도구는 모델에 묻습니다 ** 애니메이션 코드를 작성 **, 다음 실행하고 그것을 희망 오른쪽 보이는 무언가로 컴파일한다. 그 주변의 생태계가 가득하다는 이유 실행 로그를 읽는 리뷰어 루프, 왜 공개 리더 보드가 어떻게 득점 각 모델은 작업 애니메이션을 생산합니다.

stem-shorts는 유효성**를 얻는 spec에 **필요한 모형을 요구합니다. 레이아웃은 고정 계약, 렌더링자는 deterministic Python이며, 사양은 모두 확인됩니다. 그려. 최악의 결과는 prettier 될 수있는 차트입니다.

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