Herramientas

Turns the current folder into a teaching workspace you can resume across conversations: asks your learning goal, gathers reliable sources, builds short HTML lessons and cheat sheets, and tracks progress. Good for self-study and lesson prep; invoke manually (/teach), no API key.

Multi-agent simulated hedge fund: agents modeled on investors like Buffett and Munger work with quant and risk modules to analyze stocks, with backtesting. Good for teaching investment frameworks. Needs Financial Datasets and model API keys; no real trading, not investment advice.

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.

Personalized tutoring system from HKU's HKUDS: build a knowledge base from textbooks for Q&A, step-by-step solutions and quizzes, for teachers and self-learners. The deeptutor command outputs JSON for agents like Claude Code. Needs Python 3.11+, Node.js 20+ and an LLM API key.

Official Anthropic plugin that solves competition math such as IMO and Putnam problems, with adversarial checks by an independent verifier that only sees the clean proof. Gives up explicitly rather than bluffing when unsure, and outputs LaTeX/PDF once verified.

Compiles Markdown chapters into an online book with a table of contents, full-text search and themes, good for manuals, lecture notes and course material; agents can edit chapter files and rebuild to preview. Maintained by the Rust team; install a prebuilt binary or via cargo.

Unofficial Python library and CLI for driving Google NotebookLM (now Gemini Notebook): bulk-import sources, ask cited questions, and generate and download podcasts, videos, slides, quizzes and flashcards. Ships an agent skill. Relies on undocumented APIs and needs a Google sign-in.

Turns Markdown outlines into interactive HTML mind maps you can collapse and zoom, with inlined assets for offline use and a watch mode that refreshes live. Good for mapping requirements, lesson planning and organizing knowledge.

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.

Twelve research skills from Prof. Yuyu Luo's team at HKUST (Guangzhou), built on a decade of top-venue publishing and reviewing: idea evaluation, intro and body writing, polishing, surveys, pre-submission review, figures, Draw.io rebuilds and rebuttals. Non-commercial license.

Turns outlines or scripts into web slides that present in the browser (not .pptx files), with 36 themes and 15 full deck templates; press S for presenter mode with script and timer. Good for lessons, training and reports. Plain local files; no account or API key needed.

Turns articles, reports, papers or notes into a .pptx of full-slide images with speaker notes, in 12 styles such as lectures and thesis defenses; slide text is not editable. Needs Python 3; uses Codex's built-in image generation, or an OpenAI-compatible image API key in Claude Code.

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.

Unofficial nlm CLI and MCP server for NotebookLM (now Gemini Notebook): bulk-create notebooks, import web pages and docs, and generate audio overviews, quizzes, flashcards, mind maps and slides for teaching and study. Relies on undocumented APIs and a Google sign-in, so it may break.

Unofficial CLI and MCP that let agents work with NotebookLM (now Gemini Notebook): bulk-import sources, ask questions with citations, and generate audio overviews, quizzes, flashcards, mind maps and slides. Uses undocumented APIs and may break; needs a Google sign-in and uv.

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.

From ISCAS's Chinese Information Processing Lab: Claude Code or Codex write slides from your brief, render each page to check layout, and export an editable PPTX, for lectures and academic talks. Needs macOS, Linux or WSL with Python (uv), npm and LibreOffice; install per the README.

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.

Builds Markdown and Jupyter notebooks into online textbook sites with a TOC, math, citations and cross-references, plus publication-quality PDF export; popular for course notes. A Jupyter subproject installed via pipx or npm; PDF export needs Typst or LaTeX.

Recognizes images or whole PDFs (including scans) with math formulas and tables into Markdown and LaTeX, strong on English and Chinese and supporting 80+ languages, handy for digitizing exams, lecture notes and study notes. An open-source Mathpix alternative that runs locally with Python.

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.

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.

Adapts an explanation to its audience, from a 5-year-old to a high schooler, college student, parent or executive, adjusting vocabulary, analogies, tone and depth. Useful for teachers, parents and explaining expert topics to laypeople. No account or extra software needed.