Skill

From "anything" to NotebookLM: A Claude Skill that truly automates knowledge processing

Today, with more and more AI tools, the problem that really troubles many people is not "whether there is AI", but: facing an article, an e-book,[…]

11 August, 2026102 Reading15-minute reading
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Today, with more and more AI tools, the question that really troubles many people is no longer "whether there is AI", but:

Faced with an article, an e-book, a podcast, a YouTube video or a bunch of PDFs, how can you quickly turn these content into knowledge that you can truly use?

Traditional methods often require a long assembly line:

Open the web page → copy the content → download the file → convert the format → organize the text → upload NotebookLM → ask questions → summarize → organize them into PPT, Mind Map or Notes.

Each step is not difficult, but it is very time-consuming to combine.

and qiaomu-anything-to-notebooklm This Claude Skill is trying to solve this problem:Automatically acquire, convert, and organize content from different sources, and hand it over to NotebookLM for continued processing, making the process of "content → knowledge products" as automated as possible.

1. What is it?

qiaomu-anything-to-notebooklm It is a Skill for Claude Code. The core concept can be summarized in one sentence:

Anything → NotebookLM → Anything

In other words, you can give it a web page, a Weixin Official Accounts article, a YouTube video, an EPUB e-book, a PDF, or even a podcast, and then tell the AI in natural language what you ultimately want.

For example:

"Make this article into a podcast."

"Make this YouTube video into a PPT."

"Analyze this e-book and tell me the 10 most important points."

"Organize this long X/Twitter thread into a mind map."

The project will be responsible for obtaining content, converting format, uploading it to NotebookLM, and then using NotebookLM's capabilities to generate the final result. The project page currently positions it as a "multi-source content intelligent processor" that supports multiple sources such as Weixin Official Accounts, web pages, YouTube, podcasts, PDFs, Markdown, and Office documents.

This means that it is not building a new chatbot, but rather trying to solve a very important part of the AI workflow:

Let AI not just "answer questions", but automatically process various data in the real world.

2. What it solves is actually the problem of "knowledge transfer"

Many people encounter a problem when using NotebookLM:

NotebookLM is strong, but where does the data come from?

Suppose you are studying "the future development trends of artificial intelligence."

You may also collect:

  • Several Articles on Weixin Official Accounts
  • 10 YouTube videos
  • A podcast
  • An industry report PDF
  • A related e-book
  • Several news reports
  • Some X/Twitter long threads

If everything is sorted out manually, the workload will be very large.

The idea of this Skill is to connect the entire process:

Information source → content acquisition → content cleaning → format conversion → NotebookLM → AI analysis → final product

As a result, many intermediate steps that originally required manual completion were integrated into an automated assembly line.

This is also the most noteworthy aspect of this project.

It's not simply "helping you summarize an article", but building aPersonal knowledge processing channels。

3. The source of support content is very rich

Judging from the project's public documentation, the content sources it supports cover multiple categories.

1. Social media and video

Including:

  • Weixin Official Accounts
  • X/Twitter
  • YouTube
  • small universe
  • Himalayan
  • station B

Among them, YouTube can extract subtitles; some podcasts and videos can be converted into text through transcribing services.

2. normal page

General news, blogs, document pages, etc. can all be used as input.

Users can even give a URL directly without having to manually copy the web content first.

3. E-books and documents

Including:

  • PDF
  • EPUB
  • Markdown
  • TXT

This section is particularly valuable for researchers, students and content creators.

An e-book with hundreds of thousands of words requires a lot of time if it relies entirely on manual reading, excerpts and sorting; and through automatic extraction and NotebookLM's analysis capabilities, a problem-driven reading process can be further established.

4. Office files

The project also supports:

  • Word
  • PowerPoint
  • Excel

This means that it is no longer dealing with just "web articles", but is gradually approaching a common data entry.

4. What really matters is not "how many formats are supported", but automatic recognition

This Skill has a very practical design:

Users do not need to tell AI "This is an article on Weixin Official Accounts,""This is EPUB," and "This is a YouTube video."

Just give the content.

For example:

https://mp.weixin.qq.com/s/xxx

The system can recognize the content as Weixin Official Accounts.

Give it:

https://youtube.com/watch?v=xxx

You can process it according to YouTube content.

If you enter:

/Users/xxx/book.epub

It will be handled as an e-book.

You can even enter something like:

搜索“AI Agent 未来趋势”

Let the system perform search and summary information first.

The logic behind this design is very simple:

Hide technical details and let users just describe "what I want".

This is actually a very important direction for future AI Agent products.

5. Directly from "data" to "products"

If content acquisition solves the "input problem", then NotebookLM solves the "understanding and output problem".

Target outputs supported by the project include:

ˇPodcast

Convert articles, reports, research materials, etc. into content suitable for listening.

Suitable for:

  • Commuting study
  • fragmented reading
  • review materials
  • Long article quick understanding

📊 PPT

Further convert the data into a presentation.

For example:

"Make this industry report into a 20-page PPT."

These abilities are very useful for consulting, reporting, teaching and team sharing.

ˇMind Map

Suitable for handling complex content.

For example, a book, a long article, or a Twitter Thread can be further transformed into a structured knowledge map.

📝 Quiz

If your goal is not to "understand" but to "remember", Quiz is very valuable.

You can convert the data into questions and then test your understanding by answering the questions.

📄Report

This is an output form that is more suitable for research scenarios.

In particular, the "in-depth analysis model" provided by the project will conduct multiple rounds of progressive questions around the content, rather than just generating a simple summary.

6. The most interesting function: Progressive in-depth analysis

project provides a --deep-analysis pattern.

It doesn't simply ask AI:

"Summarize this article."

Instead, three rounds of analysis were designed.

Round 1: Building Overall Awareness

Main solutions:

  • What does the article say?
  • What is the core structure?
  • What are the most important arguments?
  • Are there any anti-common sense or subversive views?

This round is equivalent to building a knowledge map.

Round 2: Dig deeper

Further analysis:

  • What is the author's reasoning logic?
  • What opinions are contradictory?
  • What places are questionable?
  • What are the most important insights?

This step was upgraded from "knowing what the author said" to "understanding why the author said this."

Round 3: Synthesis and rumination

Finally, he asked further:

  • What are the biggest cognitive changes?
  • How does it help practical actions?
  • Why is it worth recommending?
  • How should readers apply this knowledge?

The core idea of this design is:

Don't let AI summarize all at once, but let AI gradually deepen in the same context.

The project documentation clearly states that NotebookLM maintains context within the same session, so subsequent questions can leverage previous answers to form a progressive analysis.

This is closer to the real research workflow than a simple "one-click summary".

7. Its technical architecture is actually a "content pipeline"

From the perspective of the project structure, it can be roughly understood as the following link:

User natural language input

↓

Determine the source of content

↓

Call the corresponding content acquisition tool

↓

Extract and clean content

↓

Unified conversion into a format suitable for processing

↓

Upload NotebookLM

↓

Generating target content

↓

Output MP3 / PDF / JSON and other results

The architecture given by the project README also involves components such as WeChat MCP, web scraping, podcast transcriptions, file conversion, and NotebookLM.

This means that what it really does is string together many tools that were originally independent of each other.

For example:

Weixin Official Accounts

→ Browser simulates grabbing

→ HTML parsing

→ Markdown

→ NotebookLM

→ In-depth analysis

→ Flying Book Document

The podcast might be:

Podcast URL

→ Audio acquisition

→ Speech to text

→ NotebookLM

→ PPT

The entire process ultimately forms an automated workflow.

8. Why is this Skill worth paying attention to?

I think what's really valuable about it is not that it has "another AI tool", but that it reflects that the way AI is used is changing.

In the past, we used AI, usually:

People find information → people organize information → people tell AI → AI answers

The direction of Agent / Skill is:

People propose goals → AI obtains data → AI organizes data → AI analyzes data → AI generates results

There is a huge efficiency difference between the two.

In the former model, people are still the "glue" of the entire process.

In the latter model, AI begins to undertake process choreography.

Therefore,qiaomu-anything-to-notebooklm Can be regarded as a very typicalAI Knowledge Workflow Skill。

The problem it really wants to solve is not "making AI smarter", but:

Let AI complete more steps on its own.

9. Who is it particularly suitable for?

1. content creators

If you need to read a lot every day:

  • news
  • industry report
  • blog
  • video
  • podcast

You can first process these contents centrally, and then let the AI output the article material, opinions and structure.

2. Students and researchers

When facing papers, e-books, reports, and course materials, you can establish a more systematic research process:

Data collection → NotebookLM → Problem analysis → In-depth summary → Knowledge review

Especially suitable for reading a large number of documents.

3. product Manager

You can put:

  • user research
  • industry report
  • Competitive product information
  • news
  • video
  • community discussions

Unified access to a knowledge base and then further analyze trends, opportunities and competitive landscape.

4. Consultants and business analysts

If you need to read a large amount of external data frequently, this automated assembly line can significantly reduce the data collation time.

5. AI heavy users

If you are already using tools such as Claude Code, NotebookLM, MCP, etc., the value of this Skill will be more obvious.

Because it is not an isolated tool, but can become a "content portal" into the entire AI workflow.

10. Installation is actually not complicated

The project provides a relatively direct installation method.

First go to the Claude Skills directory:

cd ~/.claude/skills/
git clone https://github.com/joeseesun/qiaomu-anything-to-notebooklm
cd qiaomu-anything-to-notebooklm

Then run the installation script:

./install.sh

Finally, follow the prompts to configure MCP and restart Claude Code. NotebookLM also requires certification for the first time.

The basic environments required by the project include Python 3.9+ and Git.

If you need to process audio/video transcriptions such as small universe, Himalayas, and B station, you also need to configure the corresponding Get Notes API.

11. A very typical usage scenario

Suppose you find a long industry article.

Previous operations may be:

  1. open the webpage
  2. read the article
  3. reproducing words
  4. Paste to AI
  5. Request AI summary
  6. Let AI do PPT again
  7. Then reorganize the PPT

Now you can express your needs directly as:

把这篇文章做成一份适合团队分享的 PPT，
同时提炼 10 个最重要的观点。

The system is responsible for:

Get articles → extract content → upload NotebookLM → analyze → generate results

This is the most ideal way to use AI Agents:

People are responsible for proposing goals, and machines are responsible for executing processes.

12. Places to Pay Attention

Of course, this project is not a "universal converter".

First, it relies on multiple external components, so the stability of different content sources may differ.

Secondly, NotebookLM's own capabilities, interfaces and limitations will also affect the final result.

In addition, for copyrighted content and paid content, users need to pay special attention to legal and platform rules.

The project README also clearly reminded that the relevant pay wall processing functions are mainly targeted for personal learning and research purposes, and recommended supporting high-quality media and purchasing regular subscriptions.

Therefore, a more reasonable way to understand it is:

It is an automated tool for knowledge processing, not a tool that bypasses content copyright restrictions.

In actual use, priority should be given to the content that you have the right to access and use.

13. What's really worth thinking about is that the next step for AI is not to "answer", but to "execute"

qiaomu-anything-to-notebooklm What I find most interesting about this project is not how many formats it supports, nor how many files it can generate.

What is really worth paying attention to is the thinking behind it:

Turn AI from a "chat tool" to a "workflow executor".

Previously we needed to use separately:

  • browser
  • download tool
  • PDF Converter
  • OCR
  • Audio transcriptions
  • Markdown conversion
  • NotebookLM
  • PPT tool
  • mind mapping tools

Now, you can try to string these capabilities into an assembly line.

What the end user may see is just one sentence:

"Study these materials and give me a PPT."

More than a dozen steps may have been implemented behind the scenes.

This is the meaning of Agent Skill.

Conclusion: From "information explosion" to "knowledge automation"

What is really scarce today is not information.

The Internet generates a large number of articles, videos, podcasts, reports and e-books every day.

What is really scarce is:

Do we have the ability to quickly turn this information into our own knowledge?

qiaomu-anything-to-notebooklm The idea provided is to establish an automated channel from "information" to "knowledge products":

Any source → Automatic access → Content organization → NotebookLM → In-depth analysis → Podcast/ PPT /Mind Map/ Quiz /Report

It does not represent just a Claude Skill, but an increasingly important way of working AI:

Don't just let AI tell you the answer, let AI help you complete the entire process from information to answers.

For people who regularly read, research, write and process large amounts of information, this may be the productivity improvement AI can really bring.

The original warehouse of the project can be found in GitHub：joeseesun/qiaomu-anything-to-notebooklm Check out; the Skill profile page you provide also organizes the purpose and source of the project.