Herramientas

MuseScore 4 command-line wrapper from CLI-Anything: lets agents transpose scores, extract parts, add or remove instruments, inspect score info and export PDF, MIDI, MusicXML, MP3 or braille. Handy for music teachers preparing scores in bulk. Needs MuseScore 4 and Python 3.10+.

Engine for math and explainer animations in Python (Community Edition, ManimCE): write scenes and render them to video with the manim command, letting agents create teaching and science animations. On macOS install cairo and pkg-config first; formulas need LaTeX.

Official skill for OpenMAIC, Tsinghua THU-MAIC's open-source multi-agent classroom: from a topic or PDF it generates a full lesson with slides, quizzes, interactive simulations, project-based learning and AI teacher narration. Use a hosted access code, or self-host with an LLM API key.

CLI version of the VideoCaptioner subtitle assistant: transcribes courses, lectures and interviews, uses an LLM to segment, proofread and translate, then burns subtitles into the video. Free ASR and Bing translation work out of the box; LLM refinement needs an API key. Python 3.10 to 3.12.

Open-source zero-shot TTS via f5-tts_infer-cli: from a few seconds of reference audio it reads Chinese or English text in the same voice, handy for lessons and videos. Needs Python, PyTorch and FFmpeg, ideally a GPU. Pretrained models are non-commercial; get consent before cloning a voice.

Rewrites an article or script as narration, then builds a click-through 16:9 web presentation you can screen-record into an explainer video, with optional synthesized voiceover. Good for lessons, YouTube tutorials and product walkthroughs. Needs Node.js; voiceover needs a MiniMax or OpenAI key.

Open-source alternative to NotebookLM's podcast feature: turns web pages, PDFs, images, YouTube videos or text into two-host conversational audio, useful for lecture notes or articles. Uses Gemini and OpenAI API keys by default, or local models plus free Edge TTS.

Local neural text-to-speech engine that converts text to speech offline, with voices in dozens of languages, handy for listening materials or narrating articles and course slides. Needs Python; download a voice model before first use. The project is currently looking for maintainers.

Turns SRT subtitles into a hand-drawn whiteboard animation on warm paper that follows the narration, exported as MP4, pausing for approval at storyboard, line art and annotation steps. Good for lesson explainers. Needs Python; line art from an image-capable agent or your own.

Turns a topic or article into a narrated explainer video in English or Chinese: research, script, voiceover, storyboard and per-shot animation, with subtitles and chapter cards. Needs Node.js, ffmpeg and Python; takes about 1-3 hours depending on length. Free for non-commercial use only.

From a single topic, the agent writes a storyboard, generates paper-cutout collage visuals, motion, narration, music and subtitles, and assembles a Vox-style explainer or ad with ffmpeg, checking with you on storyboard and style. Needs a prepaid Atlas Cloud API key, ffmpeg and Python.

Local text-to-speech CLI built on the open Kokoro model: batch-converts TXT, EPUB and PDF to MP3 or WAV by chapter, with multiple languages, voice blending and speed control; handy for audio versions of course materials. Needs Python 3.11-3.12 and two model files downloaded first.

A three-stage workflow for math modeling contests like MCM/ICM and CUMCM: analysis, solving and plotting in Python or MATLAB, and a Word (optionally LaTeX) paper, with stage-check subagents and OpenAlex plus AnySearch literature search. Generated papers are for reference only.

Econometrician Pedro Sant'Anna's academic Claude Code setup (fork the repo to use it): Beamer/Quarto lecture slides, paper review and mock refereeing, R/Python analysis and replication packages, with specialist subagents and quality gates. Needs Git and Python; slides need XeLaTeX and Quarto.

Turns secondary-school math and science problems into teaching materials: interactive geometry solution pages, 3D molecular views of chemical reactions, and math explainer videos with Chinese voiceover and bilingual subtitles (needs a Zhipu GLM-TTS key). Requires Python sympy.

Breaks a rough explanation idea into a scene-by-scene storyboard, then writes animation code following best practices for Manim Community or 3Blue1Brown's ManimGL, for math and physics explainers in class demos and educational videos. Needs Python, FFmpeg and LaTeX installed locally.

Google's official Gemini CLI extension with a built-in MCP server: Nano Banana image generation and editing, photo restoration, icons, textures, step-by-step storyboards and diagrams. Needs Node.js and a Gemini API key; for Claude Code or Codex, clone and build the repo first.

Turns Manim animations into step-through slide decks: mark slide breaks in scene code, render, then present with manim-slides or export to HTML, PDF or PPTX, for math and science classes and talks. Scenes are Python (agents can write them); needs Manim, plus LaTeX for formulas.

Uses yt-dlp to pull YouTube subtitles into the agent, so asking it to summarize a video gets you the key points; handy for teachers prepping lessons, researchers and creators. No API key, but needs yt-dlp (Homebrew or WinGet) and Node.js.

MCP server for the Lean 4 theorem prover: lets agents read proof goals, diagnostics and definitions and search theorems via LeanSearch and Loogle, helping mathematicians and students with formal proofs. Needs uv and the Lean toolchain; run it from the Lean project root.

Write a script, speakers and visuals in MulmoScript (JSON or YAML) and the mulmo command generates narration and images and assembles a video, podcast, slide deck or PDF, for explainer videos. Needs Node.js, FFmpeg and an OpenAI API key; other providers optional.

Two skills for the MCM/ICM and China's CUMCM math modeling contests: solver breaks down problems, picks models from a 95+ scenario decision matrix and gives Python or MATLAB templates; paper coaches abstracts, model validation and formatting from winning papers. Needs Python or MATLAB.

Describe the video you want and the agent builds a reproducible edit project: footage, storyboard, bilingual subtitles, transitions, intro and outro, rendering and QA. Needs Node.js and ffmpeg; AI voice and assets need ListenHub. Science mode uses the author's voice and outro by default.

Lets agents understand, explain and generate Max/MSP patches: look up official object docs, explain existing patches, and create and connect objects, for sound design and interactive music teaching. Needs Max 9+, Python, uv and npm; install from source and open the companion patch in Max.