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reallygood83 / notebooklm-llm-wiki-flow

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NotebookLM to LLM Wiki to Obsidian to qmd workflow scaffold and local-first research automation

10
9
100% credibility
Found Apr 22, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python CLI tool that automates processing NotebookLM outputs into structured LLM Wiki pages in Obsidian vaults, with qmd indexing for retrieval.

How It Works

1
🔍 Discover the knowledge organizer

You find this handy tool that turns quick AI research notes into a lasting personal wiki for better decisions.

2
📥 Get it ready in moments

Run a simple setup script that prepares everything in a private folder on your computer.

3
🔐 Sign into your AI notebook

Open your browser once to connect your Google NotebookLM account so it can gather research.

4
📁 Point to your notes folder

Tell it where your Obsidian vault lives, creating a special wiki area if needed.

5
🚀 Launch your first research

Give it a topic like comparing company policies and some web links, then sit back as it works.

6
✨ See new wiki pages appear

Fresh notes, comparisons, checklists, and entity summaries land safely in your wiki.

🏆 Build your reusable knowledge base

Now you have organized, searchable insights ready for future use, growing smarter over time.

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AI-Generated Review

What is notebooklm-llm-wiki-flow?

This Python CLI scaffolds a local-first automation flow: Google NotebookLM LLM outputs to structured LLM Wiki in Obsidian, then qmd indexing for search. Feed URLs or PDFs into NotebookLM—an LLM with RAG for active learning and collaborative tutoring—generate reports, mindmaps, and Q&A, then extract high-signal content into entity pages, comparisons, checklists, and raw sources. Solves dumping ephemeral NotebookLM results by building reusable knowledge layers with provenance.

Why is it gaining traction?

NotebookLM GitHub integration via notebooklm-py beats manual workflows, with Claude slash commands (/note-wiki) for one-shot runs and YAML for reusable flows. Staged writes, JSON manifests, and doctor checks ensure safe, auditable automation—plus qmd updates for instant search. Deterministic indexing and Obsidian kit make notebooklm folder llm management feel like a local NotebookLM vs LLM powerhouse.

Who should use this?

Policy researchers comparing Anthropic vs OpenAI (run-policy-compare), AI governance teams in education/healthcare tracking vendor terms, Obsidian users automating notebooklm github repo as source imports. Fits repeatable analysis like enterprise compliance checklists or vertical AI risk flows.

Verdict

Early at 10 stars and 1.0% credibility, but strong docs, GitHub Actions CI, mypy, and pytest coverage make it reliable scaffolding. Use if NotebookLM GitHub py automation aligns—skip for production without more battle tests.

(178 words)

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