QingyunQian

QingyunQian / SlidesMentor

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SlidesMentor: Teaching-First NotebookLM Prompts from Papers and Codebases

44
0
100% credibility
Found Apr 02, 2026 at 44 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

SlidesMentor transforms research papers and optional codebases into teaching summaries, slide outlines, lecture scripts, and optimized prompts for tools like NotebookLM.

How It Works

1
🔍 Discover SlidesMentor

While prepping a lecture from a dense research paper, you find this handy tool that simplifies papers into teachable stories.

2
📥 Set up the tool

Run a quick setup script to add SlidesMentor to your AI assistant's toolkit, making it ready to help.

3
🗣️ Ask your AI for help

Chat with your AI assistant and say 'Use SlidesMentor to teach this paper', sharing the paper file or link.

4
📝 Add your details

Tell it the audience level, talk length, and if there's related example work to include for a perfect fit.

5
Choose extras
💻
Include examples

Add a folder of related work to make the teaching richer.

❌
Paper only

Stick to just the paper for a focused summary.

6
⏳ Let it create

Your AI works its magic, building summaries, outlines, and ready-to-use prompts.

🎉 Get teaching goodies

Open your new folder of clear summaries, slide plans, scripts, and prompts – perfect for lectures or AI slide makers!.

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Star Growth

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AI-Generated Review

What is SlidesMentor?

SlidesMentor is a shell-based skill for AI agents like Claude Code and Codex that transforms research papers and optional codebases into teaching-first artifacts: a core teaching summary, slide outline, lecture script, NotebookLM-ready prompts, and a quality check report. It solves the mismatch between publication-optimized papers and lecture needs by extracting a single teachable story, reorganizing for pedagogy over sections, and prepping prompts that yield better slide decks in NotebookLM. Users get markdown outputs in a local folder after simple natural-language requests to their agent.

Why is it gaining traction?

It stands out by focusing on teaching workflows—audience-tuned summaries, duration-based slide counts, and code relevance decisions—rather than generic paper summarization. Developers notice the QC report that flags weak artifacts before feeding prompts to NotebookLM, plus optional codebase integration for real-world context. The plain-language agent prompts and one-shot install via shell scripts lower the barrier for quick tests.

Who should use this?

Academic researchers prepping grad seminars from arXiv papers, profs building course lectures with supporting codebases, or dev teams running group meetings on recent ML papers. It's for anyone tired of pasting raw PDFs into NotebookLM and getting abstract-heavy slides.

Verdict

Grab it if you're in the teaching-from-papers loop—solid docs and MIT license make it easy to try, despite 44 stars and 1.0% credibility signaling early-stage experimental status. Tune expectations: great for prompts, but pair with manual deck reviews for polish.

(178 words)

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