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Notebooklm Manager

Community
LeeJuOh
notebooklm-manager

Manages NotebookLM notebooks — query, add, list, search, enable/disable, remove. Use when a notebooklm.google.com URL appears or user mentions NotebookLM. Handles URLs through its own Chrome agent — do not navigate directly. Requires: claude-in-chrome MCP.

Overview

PublisherLeeJuOh
Repositoryclaude-code-zero
Skill namenotebooklm-manager
Stars
53
Forks
4
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeeJuOh on GitHub. Read the source before you install it.

Installation

Install the Notebooklm Manager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeeJuOh/claude-code-zero/tree/main/plugins/notebooklm-connector/skills/notebooklm-manager
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LeeJuOh/claude-code-zero.git /tmp/claude-code-zero
mkdir -p .claude/skills
cp -r /tmp/claude-code-zero/plugins/notebooklm-connector/skills/notebooklm-manager .claude/skills/notebooklm-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Notebooklm Manager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Notebooklm Manager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Notebooklm Manager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

NotebookLM Manager

Query orchestration and notebook registry management.

Instructions

Tool Boundaries

Don't call Chrome MCP tools (mcp__claude-in-chrome__*) directly. All browser interaction goes through the chrome-mcp-query agent via Agent, because the agent owns tab setup, response polling, and error classification. If the agent returns an error, report it instead of attempting Chrome tools yourself.

0. Data Path Resolution (run first)

Read ~/.claude-code-zero/notebooklm-connector/data-path to obtain DATA_DIR. The PreToolUse hook automatically detects install scope (project vs user) and writes the correct path.

  • The file contains a single line: the absolute path to the data directory.
  • Store this as {DATA_DIR} and use it for ALL subsequent file operations.
  • File read error → Tell user to restart the session (hook may not be loaded).

0.1 Config

Read {DATA_DIR}/config.json for user preferences:

  • max_followups: Maximum follow-up queries in coverage analysis (default: 3)
  • max_query_length: Maximum characters per query sent to NotebookLM (default: 40000)
  • language: Preferred response language (null = match user's language)
  • auto_coverage: Enable automatic coverage analysis (default: true)

If the file is missing or a field is absent, use defaults above.

1. Query Detection

Extract from user message:

  • notebook_id: Which notebook (e.g., "claude-docs")
  • question: What to ask

2. Notebook Lookup

Read {DATA_DIR}/library.json to find notebook URL.

  • File not found → Re-run Step 0. If still missing, tell user to restart the session.
  • Not found → Show "Did you mean?" with similar IDs

3. Chat History

Default: clearHistory: false (keep previous context).

Set clearHistory: true only when the user explicitly requests it (e.g., "clear history and query…", "start fresh on this notebook").

3.5 Input Length Validation

NotebookLM has a server-side character limit on chat input (~45,000–50,000 chars). There is no client-side enforcement — the textarea accepts any length, but the backend silently fails to respond beyond the limit, leaving the agent stuck in a polling loop.

Read max_query_length from config (default: 40000).

If question.length > max_query_length:

  1. Condense: Rewrite the question to stay within the limit while preserving intent and key terms.
  2. Inform user: Note that the question was condensed and show the shortened version.
  3. If the question cannot be meaningfully condensed (e.g., a large code block the user wants analyzed), suggest breaking it into smaller, focused queries.

4. Agent Invocation

Agent({
  subagent_type: "notebooklm-connector:chrome-mcp-query",
  prompt: `Execute the workflow: Input parsing → Tab setup → Title extraction → Submit question → Poll response → Output and exit

URL: {url}
Question: {question}
clearHistory: {true/false}

Output the response immediately upon receiving it and exit.`
})

Follow-up queries use the same Agent format with the follow-up question. The agent's STEP 1 automatically reuses the existing tab for the same URL.

4.1 Agent Result Parsing

After Agent returns, check the agent output:

Agent Output ContainsAction
ERROR_TYPE: CHROME_NOT_CONNECTEDShow the agent's "Steps to fix" to the user, stop
ERROR_TYPE: AUTH_REQUIREDTell user to log in to Google in Chrome, stop
ERROR_TYPE: (any other)Show error details from agent output, stop
Agent tool itself errorsInform user the agent could not start. Check plugin installation.
Response is empty or very short (< 20 chars)Inform user that NotebookLM returned no meaningful response. Likely causes: input too long, no relevant content in notebook, or backend timeout. Do NOT proceed to coverage analysis.
Normal response (no ERROR_TYPE, ≥ 20 chars)Proceed to Section 5

5. Coverage Analysis

NotebookLM frequently answers only the first part of multi-topic questions. Without this check, users get incomplete answers and need to re-query manually.

If auto_coverage is false in config, skip to Section 6.

After every successful Agent(chrome-mcp-query) return, check coverage before presenting the answer. The PostToolUse hook will also remind you via COVERAGE_REMINDER.

STEP A: ANALYZE

Re-read user's original message. List ALL keywords/topics.

STEP B: VERIFY

Each keyword: ✅ covered / ❌ missing

STEP C: QUERY (if gaps)

Launch follow-up: Agent(subagent_type: "notebooklm-connector:chrome-mcp-query", same URL, missing topic question) Follow-ups are cheap — the same Chrome tab is reused. Then return to STEP A.

STEP D: COMPLETE

All covered OR max_followups reached → Synthesize and present (Section 6 format). After limit: AskUserQuestion to confirm whether to continue.


6. Response Format

**Notebook**: [Title] (`{id}`)

**Answer**: [response]

---
**Suggested follow-ups**:
- [question 1]
- [question 2]

If language is set in config, present the answer in that language.


Commands

See references/commands.md for full command reference.

CommandDescription
listShow active notebooks
add <url>Smart add (auto-discover)
show <id>Notebook details
search <query>Find notebooks

Storage

The hook picks the data directory per install scope and migrates legacy layouts. Always use {DATA_DIR} from Section 0. File formats: references/schemas.md.


References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Notebooklm Manager AI skill do?

Manages NotebookLM notebooks — query, add, list, search, enable/disable, remove. Use when a notebooklm.google.com URL appears or user mentions NotebookLM. Handles URLs through its own Chrome agent — do not navigate directly. Requires: claude-in-chrome MCP.

Why use Notebooklm Manager on TypingMind?

Because you install it once and use it with any model. Notebooklm Manager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Notebooklm Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeeJuOh/claude-code-zero/tree/main/plugins/notebooklm-connector/skills/notebooklm-manager. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Notebooklm Manager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Notebooklm Manager?

As many as you like. As long as a model supports skills, you can use Notebooklm Manager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Notebooklm Manager AI skill free?

Yes. It is published on GitHub by LeeJuOh under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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