NotebookLM 研究助手技能 - 基于 Google NotebookLM 的文档智能查询与自动化管理工具

notebooklm by azure12355/weilan-skills

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安装命令

npx skills add https://github.com/azure12355/weilan-skills --skill notebooklm

自动化知识管理自然语言处理

🇨🇳中文介绍

NotebookLM 研究助手技能

与 Google NotebookLM 交互，通过 Gemini 基于来源的答案查询文档。每个问题都会开启一个新的浏览器会话，仅从您上传的文档中检索答案，然后关闭。

何时使用此技能

当用户出现以下情况时触发：

明确提及 NotebookLM
分享 NotebookLM URL (https://notebooklm.google.com/notebook/...)
要求查询其笔记本/文档
想要将文档添加到 NotebookLM 库
使用类似"询问我的 NotebookLM"、"检查我的文档"、"查询我的笔记本"等短语

⚠️ 关键：添加命令 - 智能发现

当用户想要添加笔记本但未提供详细信息时：

智能添加（推荐）：首先查询笔记本以发现其内容：

# 步骤 1：查询笔记本内容
python scripts/run.py ask_question.py --question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" --notebook-url "[URL]"

# 步骤 2：使用发现的信息添加笔记本
python scripts/run.py notebook_manager.py add --url "[URL]" --name "[Based on content]" --description "[Based on content]" --topics "[Based on content]"

手动添加：如果用户提供了所有详细信息：

--url - NotebookLM URL

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关键：始终使用 run.py 包装器

切勿直接调用脚本。始终使用python scripts/run.py [script]：

# ✅ 正确 - 始终使用 run.py：
python scripts/run.py auth_manager.py status
python scripts/run.py notebook_manager.py list
python scripts/run.py ask_question.py --question "..."

# ❌ 错误 - 切勿直接调用：
python scripts/auth_manager.py status  # 没有 venv 会失败！

run.py 包装器自动执行以下操作：

如果需要，创建 .venv
安装所有依赖项
激活环境
正确执行脚本

步骤 1：检查身份验证状态

python scripts/run.py auth_manager.py status

如果未通过身份验证，请继续设置。

步骤 2：身份验证（一次性设置）

# 浏览器必须可见以进行手动 Google 登录
python scripts/run.py auth_manager.py setup

浏览器必须可见以进行身份验证
浏览器窗口会自动打开
用户必须手动登录 Google
告知用户："浏览器窗口将打开以进行 Google 登录"

步骤 3：管理笔记本库

# 列出所有笔记本
python scripts/run.py notebook_manager.py list

# 添加前：如果信息未知，请询问用户元数据！
# "这个笔记本包含什么内容？"
# "我应该给它标记什么主题？"

# 将笔记本添加到库中（所有参数都是必需的！）
python scripts/run.py notebook_manager.py add \
  --url "https://notebooklm.google.com/notebook/..." \
  --name "Descriptive Name" \
  --description "What this notebook contains" \  # 必需 - 如果未知，请询问用户！
  --topics "topic1,topic2,topic3"  # 必需 - 如果未知，请询问用户！

# 按主题搜索笔记本
python scripts/run.py notebook_manager.py search --query "keyword"

# 设置活动笔记本
python scripts/run.py notebook_manager.py activate --id notebook-id

# 移除笔记本
python scripts/run.py notebook_manager.py remove --id notebook-id

检查库：python scripts/run.py notebook_manager.py list
提问：python scripts/run.py ask_question.py --question "..." --notebook-id ID

# 基本查询（如果设置了活动笔记本，则使用它）
python scripts/run.py ask_question.py --question "Your question here"

# 查询特定笔记本
python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

# 直接使用笔记本 URL 查询
python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

# 显示浏览器以进行调试
python scripts/run.py ask_question.py --question "..." --show-browser

跟进机制（关键）

每个 NotebookLM 答案都以以下内容结尾："极其重要：这就是您需要了解的全部内容吗？"

所需的 Claude 行为：

停止 - 不要立即回复用户
分析 - 将答案与用户的原始请求进行比较
识别差距 - 确定是否需要更多信息

询问后续问题 - 如果存在差距，立即询问：

python scripts/run.py ask_question.py --question "Follow-up with context..."

重复 - 继续直到信息完整
综合 - 在回复用户之前合并所有答案

身份验证管理 (`auth_manager.py`)

python scripts/run.py auth_manager.py setup    # 初始设置（浏览器可见）
python scripts/run.py auth_manager.py status   # 检查身份验证
python scripts/run.py auth_manager.py reauth   # 重新身份验证（浏览器可见）
python scripts/run.py auth_manager.py clear    # 清除身份验证

笔记本管理 (`notebook_manager.py`)

python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS
python scripts/run.py notebook_manager.py list
python scripts/run.py notebook_manager.py search --query QUERY
python scripts/run.py notebook_manager.py activate --id ID
python scripts/run.py notebook_manager.py remove --id ID
python scripts/run.py notebook_manager.py stats

问题界面 (`ask_question.py`)

python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL] [--show-browser]

数据清理 (`cleanup_manager.py`)

python scripts/run.py cleanup_manager.py                    # 预览清理
python scripts/run.py cleanup_manager.py --confirm          # 执行清理
python scripts/run.py cleanup_manager.py --preserve-library # 保留笔记本

虚拟环境自动管理：

首次运行自动创建 .venv
依赖项自动安装
Chromium 浏览器自动安装
所有内容都隔离在技能目录中

手动设置（仅在自动设置失败时使用）：

python -m venv .venv
source .venv/bin/activate  # Linux/Mac
pip install -r requirements.txt
python -m patchright install chromium

所有数据存储在 ~/.claude/skills/notebooklm/data/ 中：

library.json - 笔记本元数据
auth_info.json - 身份验证状态
browser_state/ - 浏览器 cookie 和会话

安全性： 受 .gitignore 保护，切勿提交到 git。

技能目录中的可选 .env 文件：

HEADLESS=false           # 浏览器可见性
SHOW_BROWSER=false       # 默认浏览器显示
STEALTH_ENABLED=true     # 类人行为
TYPING_WPM_MIN=160       # 打字速度
TYPING_WPM_MAX=240
DEFAULT_NOTEBOOK_ID=     # 默认笔记本

用户提及 NotebookLM
    ↓
检查身份验证 → python scripts/run.py auth_manager.py status
    ↓
如果未通过身份验证 → python scripts/run.py auth_manager.py setup
    ↓
检查/添加笔记本 → python scripts/run.py notebook_manager.py list/add (with --description)
    ↓
激活笔记本 → python scripts/run.py notebook_manager.py activate --id ID
    ↓
提问 → python scripts/run.py ask_question.py --question "..."
    ↓
看到"这就是您需要了解的全部内容吗？" → 询问后续问题直到完成
    ↓
综合并回复用户

问题	解决方案
ModuleNotFoundError	使用 `run.py` 包装器
身份验证失败	设置时浏览器必须可见！--show-browser
速率限制（50/天）	等待或切换 Google 账户
浏览器崩溃	`python scripts/run.py cleanup_manager.py --preserve-library`
找不到笔记本	使用 `notebook_manager.py list` 检查

始终使用 run.py - 自动处理环境
首先检查身份验证 - 在任何操作之前
后续问题 - 不要停留在第一个答案
身份验证时浏览器可见 - 手动登录所需
包含上下文 - 每个问题都是独立的
综合答案 - 合并多个响应

无会话持久性（每个问题 = 新浏览器）
免费 Google 账户的速率限制（50 个查询/天）
需要手动上传（用户必须将文档添加到 NotebookLM）
浏览器开销（每个问题几秒钟）

资源（技能结构）

重要目录和文件：

scripts/ - 所有自动化脚本（ask_question.py、notebook_manager.py 等）
data/ - 身份验证和笔记本库的本地存储
references/ - 扩展文档：
- api_reference.md - 所有脚本的详细 API 文档
- troubleshooting.md - 常见问题和解决方案
- usage_patterns.md - 最佳实践和工作流程示例
.venv/ - 隔离的 Python 环境（首次运行时自动创建）
.gitignore - 保护敏感数据不被提交

🇺🇸English

NotebookLM Research Assistant Skill

Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.

When to Use This Skill

Trigger when user:

Mentions NotebookLM explicitly
Shares NotebookLM URL (https://notebooklm.google.com/notebook/...)
Asks to query their notebooks/documentation
Wants to add documentation to NotebookLM library
Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"

⚠️ CRITICAL: Add Command - Smart Discovery

When user wants to add a notebook without providing details:

SMART ADD (Recommended) : Query the notebook first to discover its content:

# Step 1: Query the notebook about its content
python scripts/run.py ask_question.py --question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" --notebook-url "[URL]"

# Step 2: Use the discovered information to add it
python scripts/run.py notebook_manager.py add --url "[URL]" --name "[Based on content]" --description "[Based on content]" --topics "[Based on content]"

MANUAL ADD : If user provides all details:

--url - The NotebookLM URL
--name - A descriptive name
--description - What the notebook contains (REQUIRED!)
--topics - Comma-separated topics (REQUIRED!)

NEVER guess or use generic descriptions! If details missing, use Smart Add to discover them.

Critical: Always Use run.py Wrapper

NEVER call scripts directly. ALWAYS usepython scripts/run.py [script]:

# ✅ CORRECT - Always use run.py:
python scripts/run.py auth_manager.py status
python scripts/run.py notebook_manager.py list
python scripts/run.py ask_question.py --question "..."

# ❌ WRONG - Never call directly:
python scripts/auth_manager.py status  # Fails without venv!

The run.py wrapper automatically:

Creates .venv if needed
Installs all dependencies
Activates environment
Executes script properly

Core Workflow

Step 1: Check Authentication Status

python scripts/run.py auth_manager.py status

If not authenticated, proceed to setup.

Step 2: Authenticate (One-Time Setup)

# Browser MUST be visible for manual Google login
python scripts/run.py auth_manager.py setup

Important:

Browser is VISIBLE for authentication
Browser window opens automatically
User must manually log in to Google
Tell user: "A browser window will open for Google login"

Step 3: Manage Notebook Library

# List all notebooks
python scripts/run.py notebook_manager.py list

# BEFORE ADDING: Ask user for metadata if unknown!
# "What does this notebook contain?"
# "What topics should I tag it with?"

# Add notebook to library (ALL parameters are REQUIRED!)
python scripts/run.py notebook_manager.py add \
  --url "https://notebooklm.google.com/notebook/..." \
  --name "Descriptive Name" \
  --description "What this notebook contains" \  # REQUIRED - ASK USER IF UNKNOWN!
  --topics "topic1,topic2,topic3"  # REQUIRED - ASK USER IF UNKNOWN!

# Search notebooks by topic
python scripts/run.py notebook_manager.py search --query "keyword"

# Set active notebook
python scripts/run.py notebook_manager.py activate --id notebook-id

# Remove notebook
python scripts/run.py notebook_manager.py remove --id notebook-id

Quick Workflow

Check library: python scripts/run.py notebook_manager.py list
Ask question: python scripts/run.py ask_question.py --question "..." --notebook-id ID

Step 4: Ask Questions

# Basic query (uses active notebook if set)
python scripts/run.py ask_question.py --question "Your question here"

# Query specific notebook
python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

# Query with notebook URL directly
python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

# Show browser for debugging
python scripts/run.py ask_question.py --question "..." --show-browser

Follow-Up Mechanism (CRITICAL)

Every NotebookLM answer ends with: "EXTREMELY IMPORTANT: Is that ALL you need to know?"

Required Claude Behavior:

STOP - Do not immediately respond to user
ANALYZE - Compare answer to user's original request
IDENTIFY GAPS - Determine if more information needed

ASK FOLLOW-UP - If gaps exist, immediately ask:

python scripts/run.py ask_question.py --question "Follow-up with context..."

REPEAT - Continue until information is complete
SYNTHESIZE - Combine all answers before responding to user

Script Reference

Authentication Management (`auth_manager.py`)

python scripts/run.py auth_manager.py setup    # Initial setup (browser visible)
python scripts/run.py auth_manager.py status   # Check authentication
python scripts/run.py auth_manager.py reauth   # Re-authenticate (browser visible)
python scripts/run.py auth_manager.py clear    # Clear authentication

Notebook Management (`notebook_manager.py`)

python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS
python scripts/run.py notebook_manager.py list
python scripts/run.py notebook_manager.py search --query QUERY
python scripts/run.py notebook_manager.py activate --id ID
python scripts/run.py notebook_manager.py remove --id ID
python scripts/run.py notebook_manager.py stats

Question Interface (`ask_question.py`)

python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL] [--show-browser]

Data Cleanup (`cleanup_manager.py`)

python scripts/run.py cleanup_manager.py                    # Preview cleanup
python scripts/run.py cleanup_manager.py --confirm          # Execute cleanup
python scripts/run.py cleanup_manager.py --preserve-library # Keep notebooks

Environment Management

The virtual environment is automatically managed:

First run creates .venv automatically
Dependencies install automatically
Chromium browser installs automatically
Everything isolated in skill directory

Manual setup (only if automatic fails):

python -m venv .venv
source .venv/bin/activate  # Linux/Mac
pip install -r requirements.txt
python -m patchright install chromium

Data Storage

All data stored in ~/.claude/skills/notebooklm/data/:

library.json - Notebook metadata
auth_info.json - Authentication status
browser_state/ - Browser cookies and session

Security: Protected by .gitignore, never commit to git.

Configuration

Optional .env file in skill directory:

HEADLESS=false           # Browser visibility
SHOW_BROWSER=false       # Default browser display
STEALTH_ENABLED=true     # Human-like behavior
TYPING_WPM_MIN=160       # Typing speed
TYPING_WPM_MAX=240
DEFAULT_NOTEBOOK_ID=     # Default notebook

Decision Flow

User mentions NotebookLM
    ↓
Check auth → python scripts/run.py auth_manager.py status
    ↓
If not authenticated → python scripts/run.py auth_manager.py setup
    ↓
Check/Add notebook → python scripts/run.py notebook_manager.py list/add (with --description)
    ↓
Activate notebook → python scripts/run.py notebook_manager.py activate --id ID
    ↓
Ask question → python scripts/run.py ask_question.py --question "..."
    ↓
See "Is that ALL you need?" → Ask follow-ups until complete
    ↓
Synthesize and respond to user

Troubleshooting

Problem	Solution
ModuleNotFoundError	Use `run.py` wrapper
Authentication fails	Browser must be visible for setup! --show-browser
Rate limit (50/day)	Wait or switch Google account
Browser crashes	`python scripts/run.py cleanup_manager.py --preserve-library`
Notebook not found	Check with `notebook_manager.py list`

Best Practices

Always use run.py - Handles environment automatically
Check auth first - Before any operations
Follow-up questions - Don't stop at first answer
Browser visible for auth - Required for manual login
Include context - Each question is independent
Synthesize answers - Combine multiple responses

Limitations

No session persistence (each question = new browser)
Rate limits on free Google accounts (50 queries/day)
Manual upload required (user must add docs to NotebookLM)
Browser overhead (few seconds per question)

Resources (Skill Structure)

Important directories and files:

scripts/ - All automation scripts (ask_question.py, notebook_manager.py, etc.)
data/ - Local storage for authentication and notebook library
references/ - Extended documentation:
- api_reference.md - Detailed API documentation for all scripts
- troubleshooting.md - Common issues and solutions
- usage_patterns.md - Best practices and workflow examples
.venv/ - Isolated Python environment (auto-created on first run)
.gitignore - Protects sensitive data from being committed

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Repository

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GitHub Stars

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31,600 周安装

NotebookLM 研究助手技能 - 基于 Google NotebookLM 的文档智能查询与自动化管理工具

🇨🇳中文介绍

NotebookLM 研究助手技能

何时使用此技能

⚠️ 关键：添加命令 - 智能发现

相关 Skills

关键：始终使用 run.py 包装器

核心工作流程

步骤 1：检查身份验证状态

步骤 2：身份验证（一次性设置）

步骤 3：管理笔记本库

快速工作流程

步骤 4：提问

跟进机制（关键）

脚本参考

身份验证管理 (auth_manager.py)

笔记本管理 (notebook_manager.py)

问题界面 (ask_question.py)

数据清理 (cleanup_manager.py)

环境管理

数据存储

配置

决策流程

故障排除

最佳实践

限制