notebooklm
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ChineseNotebookLM Deep Research & Slide Generator
NotebookLM深度研究与幻灯片生成工具
Primary Workflow
核心工作流程
When user requests a presentation or research on a topic:
当用户请求针对某一主题生成演示文稿或开展研究时:
Step 1: Deep Research via NotebookLM MCP
步骤1:通过NotebookLM MCP开展深度研究
- Invoke the NotebookLM MCP to perform deep research on the topic
- Gather comprehensive sources and insights
- Extract key findings, data points, and citations
- 调用NotebookLM MCP针对指定主题进行深度研究
- 收集全面的研究素材与洞察信息
- 提取关键结论、数据点及引用来源
Step 2: Generate Highly Infographic Slide Presentation
步骤2:生成高信息密度的幻灯片演示文稿
Based on the user's topic outline, create visually-rich infographic slides:
Design Specifications:
- Background: White (#FFFFFF)
- Font: Arial (all text)
- Style: HIGHLY INFOGRAPHIC - visual-first design
- Content: Based exclusively on deep research sources
- Citations: Include source references on each slide
Infographic Elements (REQUIRED on every content slide):
- 📊 Charts & Graphs - Bar charts, pie charts, line graphs for data
- 📈 Statistics Callouts - Large numbers with context (e.g., "87% of users...")
- 🎯 Icons - Use relevant icons for each concept
- 🔲 Flowcharts - Process diagrams, decision trees
- 📋 Comparison Tables - Side-by-side comparisons
- 🗺️ Diagrams - Concept maps, timelines, hierarchies
- 💡 Highlight Boxes - Key takeaways in colored boxes
- ➡️ Visual Hierarchy - Clear information flow
Infographic Design Rules:
- Minimal text - Max 6 bullet points per slide, each under 10 words
- Data visualization - Convert all statistics to visual charts
- Icon-driven - Every section header has an icon
- Color coding - Use consistent colors for categories
- White space - 40% of slide should be empty for clarity
- One idea per slide - Don't overcrowd
Slide Structure:
- Title Slide - Topic name, subtitle, date, hero image/icon
- Agenda/Outline - Visual roadmap with icons
- Content Slides - Infographic-style, one concept per slide
- Key Statistics - Large numbers with visual context
- Key Findings - Summary infographic
- Sources/References - Clean list with icons
基于用户的主题大纲,创建视觉丰富的信息图式幻灯片:
设计规范:
- 背景: 白色(#FFFFFF)
- 字体: Arial(所有文本)
- 风格: 高信息密度——视觉优先设计
- 内容: 完全基于深度研究素材
- 引用: 每张幻灯片均包含来源参考
每张内容幻灯片必填的信息图元素:
- 📊 图表与图形 - 用于数据展示的柱状图、饼图、折线图
- 📈 统计数据标注 - 带有上下文的大字号数字(例如:“87%的用户...”)
- 🎯 图标 - 为每个概念使用相关图标
- 🔲 流程图 - 流程示意图、决策树
- 📋 对比表格 - 并列对比内容
- 🗺️ 示意图 - 概念图、时间线、层级图
- 💡 高亮框 - 彩色框标注的关键要点
- ➡️ 视觉层级 - 清晰的信息流转逻辑
信息图设计规则:
- 极简文本 - 每张幻灯片最多6个项目符号,每个不超过10个单词
- 数据可视化 - 将所有统计数据转换为可视化图表
- 图标驱动 - 每个章节标题都配有图标
- 颜色编码 - 为不同类别使用统一的颜色
- 留白 - 幻灯片40%区域应为空白以保证清晰度
- 单页单主题 - 避免内容过度拥挤
幻灯片结构:
- 标题页 - 主题名称、副标题、日期、主视觉图片/图标
- 议程/大纲页 - 带有图标的视觉化路线图
- 内容页 - 信息图风格,每页一个核心概念
- 关键统计数据页 - 带有视觉上下文的大字号数字
- 关键结论页 - 总结性信息图
- 来源/参考页 - 简洁的图标化列表
Step 3: Output Format
步骤3:输出格式
Generate slides as:
- Markdown format (for review)
- Or HTML/reveal.js format (for presentation)
生成的幻灯片支持以下格式:
- Markdown格式(用于审阅)
- 或HTML/reveal.js格式(用于演示)
NotebookLM API Reference
NotebookLM API参考文档
Complete programmatic access to Google NotebookLM—including capabilities not exposed in the web UI. Create notebooks, add sources (URLs, YouTube, PDFs, audio, video, images), chat with content, generate all artifact types, and download results in multiple formats.
可通过编程方式全面访问Google NotebookLM——包括网页界面未开放的功能。创建笔记本、添加素材(URL、YouTube视频、PDF、音频、视频、图片)、与内容对话、生成各类成果物,并以多种格式下载结果。
Installation
安装方法
From PyPI (Recommended):
bash
pip install notebooklm-pyFrom GitHub (use latest release tag, NOT main branch):
bash
undefined从PyPI安装(推荐):
bash
pip install notebooklm-py从GitHub安装(使用最新发布版本标签,而非main分支):
bash
undefinedGet the latest release tag (using curl)
获取最新发布版本标签(使用curl)
LATEST_TAG=$(curl -s https://api.github.com/repos/teng-lin/notebooklm-py/releases/latest | grep '"tag_name"' | cut -d'"' -f4)
pip install "git+https://github.com/teng-lin/notebooklm-py@${LATEST_TAG}"
⚠️ **DO NOT install from main branch** (`pip install git+https://github.com/teng-lin/notebooklm-py`). The main branch may contain unreleased/unstable changes. Always use PyPI or a specific release tag, unless you are testing unreleased features.
After installation, install the Claude Code skill:
```bash
notebooklm skill installLATEST_TAG=$(curl -s https://api.github.com/repos/teng-lin/notebooklm-py/releases/latest | grep '"tag_name"' | cut -d'"' -f4)
pip install "git+https://github.com/teng-lin/notebooklm-py@${LATEST_TAG}"
⚠️ **请勿从main分支安装**(`pip install git+https://github.com/teng-lin/notebooklm-py`)。main分支可能包含未发布/不稳定的变更。请始终使用PyPI或特定的发布版本标签,除非您正在测试未发布功能。
安装完成后,安装Claude Code技能:
```bash
notebooklm skill installPrerequisites
前置条件
IMPORTANT: Before using any command, you MUST authenticate:
bash
notebooklm login # Opens browser for Google OAuth
notebooklm list # Verify authentication worksIf commands fail with authentication errors, re-run .
notebooklm login重要提示: 在使用任何命令之前,您必须完成身份验证:
bash
notebooklm login # 打开浏览器进行Google OAuth验证
notebooklm list # 验证身份验证是否成功如果命令因身份验证错误失败,请重新运行。
notebooklm loginCI/CD, Multiple Accounts, and Parallel Agents
CI/CD、多账户与并行Agent
For automated environments, multiple accounts, or parallel agent workflows:
| Variable | Purpose |
|---|---|
| Custom config directory (default: |
| Inline auth JSON - no file writes needed |
CI/CD setup: Set from a secret containing your contents.
NOTEBOOKLM_AUTH_JSONstorage_state.jsonMultiple accounts: Use different directories per account.
NOTEBOOKLM_HOMEParallel agents: The CLI stores notebook context in a shared file (). Multiple concurrent agents using can overwrite each other's context.
~/.notebooklm/context.jsonnotebooklm useSolutions for parallel workflows:
- Always use explicit notebook ID (recommended): Pass (for
-n <notebook_id>/waitcommands) ordownload(for others) instead of relying on--notebook <notebook_id>use - Per-agent isolation: Set unique per agent:
NOTEBOOKLM_HOMEexport NOTEBOOKLM_HOME=/tmp/agent-$ID - Use full UUIDs: Avoid partial IDs in automation (they can become ambiguous)
针对自动化环境、多账户或并行Agent工作流:
| 变量 | 用途 |
|---|---|
| 自定义配置目录(默认值: |
| 内联身份验证JSON - 无需写入文件 |
CI/CD设置: 从包含您内容的密钥中设置。
storage_state.jsonNOTEBOOKLM_AUTH_JSON多账户: 为每个账户使用不同的目录。
NOTEBOOKLM_HOME并行Agent: CLI将笔记本上下文存储在共享文件()中。多个并发Agent使用命令可能会互相覆盖对方的上下文。
~/.notebooklm/context.jsonnotebooklm use并行工作流解决方案:
- 始终使用明确的笔记本ID(推荐):传递(用于
-n <notebook_id>/wait命令)或download(用于其他命令),而非依赖--notebook <notebook_id>命令use - Agent级隔离: 为每个Agent设置唯一的:
NOTEBOOKLM_HOMEexport NOTEBOOKLM_HOME=/tmp/agent-$ID - 使用完整UUID: 在自动化中避免使用部分ID(可能会产生歧义)
Agent Setup Verification
Agent设置验证
Before starting workflows, verify the CLI is ready:
- → Should show "Authenticated as: email@..."
notebooklm status - → Should return valid JSON (even if empty notebooks list)
notebooklm list --json - If either fails → Run
notebooklm login
在启动工作流之前,请验证CLI是否已准备就绪:
- → 应显示“Authenticated as: email@...”
notebooklm status - → 应返回有效的JSON(即使笔记本列表为空)
notebooklm list --json - 如果任一命令失败 → 运行
notebooklm login
When This Skill Activates
技能触发条件
Explicit: User says "/notebooklm", "use notebooklm", or mentions the tool by name
Intent detection: Recognize requests like:
- "Create a podcast about [topic]"
- "Summarize these URLs/documents"
- "Generate a quiz from my research"
- "Turn this into an audio overview"
- "Create flashcards for studying"
- "Generate a video explainer"
- "Make an infographic"
- "Create a mind map of the concepts"
- "Download the quiz as markdown"
- "Add these sources to NotebookLM"
显式触发: 用户输入"/notebooklm"、"use notebooklm"或直接提及该工具名称
意图检测: 识别以下类型的请求:
- "创建关于[主题]的播客"
- "总结这些URL/文档"
- "根据我的研究生成测验"
- "将此内容转换为音频概述"
- "创建用于学习的抽认卡"
- "生成视频讲解"
- "制作信息图"
- "创建概念思维导图"
- "以Markdown格式下载测验"
- "将这些素材添加到NotebookLM"
Autonomy Rules
自主运行规则
Run automatically (no confirmation):
- - check context
notebooklm status - - diagnose auth issues
notebooklm auth check - - list notebooks
notebooklm list - - list sources
notebooklm source list - - list artifacts
notebooklm artifact list - - list supported languages
notebooklm language list - - get current language
notebooklm language get - - set language (global setting)
notebooklm language set - - wait for artifact completion (in subagent context)
notebooklm artifact wait - - wait for source processing (in subagent context)
notebooklm source wait - - check research status
notebooklm research status - - wait for research (in subagent context)
notebooklm research wait - - set context (⚠️ SINGLE-AGENT ONLY - use
notebooklm use <id>flag in parallel workflows)-n - - create notebook
notebooklm create - - chat queries
notebooklm ask "..." - - add sources
notebooklm source add
Ask before running:
- - destructive
notebooklm delete - - long-running, may fail
notebooklm generate * - - writes to filesystem
notebooklm download * - - long-running (when in main conversation)
notebooklm artifact wait - - long-running (when in main conversation)
notebooklm source wait - - long-running (when in main conversation)
notebooklm research wait
自动运行(无需确认):
- - 检查上下文
notebooklm status - - 诊断身份验证问题
notebooklm auth check - - 列出笔记本
notebooklm list - - 列出素材
notebooklm source list - - 列出成果物
notebooklm artifact list - - 列出支持的语言
notebooklm language list - - 获取当前语言设置
notebooklm language get - - 设置语言(全局设置)
notebooklm language set - - 等待成果物完成(在子Agent上下文中)
notebooklm artifact wait - - 等待素材处理完成(在子Agent上下文中)
notebooklm source wait - - 检查研究状态
notebooklm research status - - 等待研究完成(在子Agent上下文中)
notebooklm research wait - - 设置上下文(⚠️ 仅支持单Agent - 在并行工作流中使用
notebooklm use <id>标志)-n - - 创建笔记本
notebooklm create - - 对话查询
notebooklm ask "..." - - 添加素材
notebooklm source add
运行前需确认:
- - 破坏性操作
notebooklm delete - - 运行时间长,可能失败
notebooklm generate * - - 写入文件系统
notebooklm download * - - 运行时间长(在主对话中)
notebooklm artifact wait - - 运行时间长(在主对话中)
notebooklm source wait - - 运行时间长(在主对话中)
notebooklm research wait
Quick Reference
快速参考
| Task | Command |
|---|---|
| Authenticate | |
| Diagnose auth issues | |
| Diagnose auth (full) | |
| List notebooks | |
| Create notebook | |
| Set context | |
| Show context | |
| Add URL source | |
| Add file | |
| Add YouTube | |
| List sources | |
| Wait for source processing | |
| Web research (fast) | |
| Web research (deep) | |
| Check research status | |
| Wait for research | |
| Chat | |
| Chat (new conversation) | |
| Chat (specific sources) | |
| Chat (with references) | |
| Get source fulltext | |
| Get source guide | |
| Generate podcast | |
| Generate podcast (JSON) | |
| Generate podcast (specific sources) | |
| Generate video | |
| Generate quiz | |
| Check artifact status | |
| Wait for completion | |
| Download audio | |
| Download video | |
| Download report | |
| Download mind map | |
| Download data table | |
| Download quiz | |
| Download quiz (markdown) | |
| Download flashcards | |
| Download flashcards (markdown) | |
| Delete notebook | |
| List languages | |
| Get language | |
| Set language | |
Parallel safety: Use explicit notebook IDs in parallel workflows. Commands supporting shorthand: , , , . Download commands also support . Other commands use . For chat, use to start fresh conversations (avoids conversation ID conflicts).
-nartifact waitsource waitresearch wait/statusdownload *-a/--artifact--notebook--newPartial IDs: Use first 6+ characters of UUIDs. Must be unique prefix (fails if ambiguous). Works for: , , commands. For automation, prefer full UUIDs to avoid ambiguity.
usedeletewait| 任务 | 命令 |
|---|---|
| 身份验证 | |
| 诊断身份验证问题 | |
| 完整身份验证诊断 | |
| 列出笔记本 | |
| 创建笔记本 | |
| 设置上下文 | |
| 显示上下文 | |
| 添加URL素材 | |
| 添加文件 | |
| 添加YouTube视频 | |
| 列出素材 | |
| 等待素材处理完成 | |
| 快速网页研究 | |
| 深度网页研究 | |
| 检查研究状态 | |
| 等待研究完成 | |
| 对话 | |
| 新对话 | |
| 指定素材对话 | |
| 带引用的对话 | |
| 获取素材全文 | |
| 获取素材指南 | |
| 生成播客 | |
| 生成播客(JSON格式) | |
| 指定素材生成播客 | |
| 生成视频 | |
| 生成测验 | |
| 检查成果物状态 | |
| 等待成果物完成 | |
| 下载音频 | |
| 下载视频 | |
| 下载报告 | |
| 下载思维导图 | |
| 下载数据表 | |
| 下载测验 | |
| 下载测验(Markdown格式) | |
| 下载抽认卡 | |
| 下载抽认卡(Markdown格式) | |
| 删除笔记本 | |
| 列出支持语言 | |
| 获取当前语言 | |
| 设置语言 | |
并行安全: 在并行工作流中使用明确的笔记本ID。支持简写的命令:, , , 。下载命令还支持。其他命令使用。对于对话,使用启动新对话(避免对话ID冲突)。
-nartifact waitsource waitresearch wait/statusdownload *-a/--artifact--notebook--new部分ID: 使用UUID的前6个以上字符。必须是唯一前缀(如果有歧义则会失败)。适用于:, , 命令。在自动化中,优先使用完整UUID以避免歧义。
usedeletewaitCommand Output Formats
命令输出格式
Commands with return structured data for parsing:
--jsonCreate notebook:
$ notebooklm create "Research" --json
{"id": "abc123de-...", "title": "Research"}Add source:
$ notebooklm source add "https://example.com" --json
{"source_id": "def456...", "title": "Example", "status": "processing"}Generate artifact:
$ notebooklm generate audio "Focus on key points" --json
{"task_id": "xyz789...", "status": "pending"}Chat with references:
$ notebooklm ask "What is X?" --json
{"answer": "X is... [1] [2]", "conversation_id": "...", "turn_number": 1, "is_follow_up": false, "references": [{"source_id": "abc123...", "citation_number": 1, "cited_text": "Relevant passage from source..."}, {"source_id": "def456...", "citation_number": 2, "cited_text": "Another passage..."}]}Source fulltext (get indexed content):
$ notebooklm source fulltext <source_id> --json
{"source_id": "...", "title": "...", "char_count": 12345, "content": "Full indexed text..."}Understanding citations: The in references is often a snippet or section header, not the full quoted passage. The / positions reference NotebookLM's internal chunked index, not the raw fulltext. Use to locate citations:
cited_textstart_charend_charSourceFulltext.find_citation_context()python
fulltext = await client.sources.get_fulltext(notebook_id, ref.source_id)
matches = fulltext.find_citation_context(ref.cited_text) # Returns list[(context, position)]
if matches:
context, pos = matches[0] # First match; check len(matches) > 1 for duplicatesExtract IDs: Parse the , , or field from JSON output.
idsource_idtask_id带有标志的命令返回结构化数据以便解析:
--json创建笔记本:
$ notebooklm create "Research" --json
{"id": "abc123de-...", "title": "Research"}添加素材:
$ notebooklm source add "https://example.com" --json
{"source_id": "def456...", "title": "Example", "status": "processing"}生成成果物:
$ notebooklm generate audio "Focus on key points" --json
{"task_id": "xyz789...", "status": "pending"}带引用的对话:
$ notebooklm ask "What is X?" --json
{"answer": "X is... [1] [2]", "conversation_id": "...", "turn_number": 1, "is_follow_up": false, "references": [{"source_id": "abc123...", "citation_number": 1, "cited_text": "Relevant passage from source..."}, {"source_id": "def456...", "citation_number": 2, "cited_text": "Another passage..."}]}素材全文(获取索引内容):
$ notebooklm source fulltext <source_id> --json
{"source_id": "...", "title": "...", "char_count": 12345, "content": "Full indexed text..."}理解引用: 引用中的通常是片段或章节标题,而非完整引用段落。/位置指向NotebookLM的内部分块索引,而非原始全文。使用定位引用:
cited_textstart_charend_charSourceFulltext.find_citation_context()python
fulltext = await client.sources.get_fulltext(notebook_id, ref.source_id)
matches = fulltext.find_citation_context(ref.cited_text) # 返回列表[(context, position)]
if matches:
context, pos = matches[0] # 第一个匹配项;如果len(matches) > 1则检查重复项提取ID: 从JSON输出中解析、或字段。
idsource_idtask_idGeneration Types
生成类型
All generate commands support:
- to use specific source(s) instead of all sources
-s, --source - to set output language (defaults to configured language or 'en')
--language - for machine-readable output (returns
--jsonandtask_id)status - to automatically retry on rate limits with exponential backoff
--retry N
| Type | Command | Options | Download |
|---|---|---|---|
| Podcast | | | .mp3 |
| Video | | | .mp4 |
| Slide Deck | | | |
| Infographic | | | .png |
| Report | | | .md |
| Mind Map | | (sync, instant) | .json |
| Data Table | | description required | .csv |
| Quiz | | | .json/.md/.html |
| Flashcards | | | .json/.md/.html |
所有生成命令支持:
- 使用特定素材而非全部素材
-s, --source - 设置输出语言(默认为配置语言或'en')
--language - 用于机器可读输出(返回
--json和task_id)status - 在遇到速率限制时自动重试,使用指数退避策略
--retry N
| 类型 | 命令 | 选项 | 下载格式 |
|---|---|---|---|
| 播客 | | | .mp3 |
| 视频 | | | .mp4 |
| 幻灯片 | | | |
| 信息图 | | | .png |
| 报告 | | | .md |
| 思维导图 | | (同步,即时生成) | .json |
| 数据表 | | 需要描述信息 | .csv |
| 测验 | | | .json/.md/.html |
| 抽认卡 | | | .json/.md/.html |
Features Beyond the Web UI
超出网页界面的功能
These capabilities are available via CLI but not in NotebookLM's web interface:
| Feature | Command | Description |
|---|---|---|
| Batch downloads | | Download all artifacts of a type at once |
| Quiz/Flashcard export | | Export as JSON, Markdown, or HTML (web UI only shows interactive view) |
| Mind map extraction | | Export hierarchical JSON for visualization tools |
| Data table export | | Download structured tables as CSV |
| Source fulltext | | Retrieve the indexed text content of any source |
| Programmatic sharing | | Manage sharing permissions without the UI |
以下功能仅可通过CLI使用,网页界面不支持:
| 功能 | 命令 | 描述 |
|---|---|---|
| 批量下载 | | 一次性下载所有指定类型的成果物 |
| 测验/抽认卡导出 | | 导出为JSON、Markdown或HTML格式(网页界面仅显示交互式视图) |
| 思维导图提取 | | 导出层级化JSON以便在可视化工具中使用 |
| 数据表导出 | | 以CSV格式下载结构化表格 |
| 素材全文获取 | | 获取任意素材的索引文本内容 |
| 程序化分享 | | 无需通过界面管理分享权限 |
Common Workflows
常见工作流
Research to Podcast (Interactive)
从研究到播客(交互式)
Time: 5-10 minutes total
- — if fails: check auth with
notebooklm create "Research: [topic]"notebooklm login - for each URL/document — if one fails: log warning, continue with others
notebooklm source add - Wait for sources: until all status=READY — required before generation
notebooklm source list --json - (confirm when asked) — if rate limited: wait 5 min, retry once
notebooklm generate audio "Focus on [specific angle]" - Note the artifact ID returned
- Check later for status
notebooklm artifact list - when complete (confirm when asked)
notebooklm download audio ./podcast.mp3
耗时: 总计5-10分钟
- — 如果失败:使用
notebooklm create "Research: [topic]"检查身份验证notebooklm login - 添加每个URL/文档 — 如果某个素材添加失败:记录警告,继续添加其他素材
notebooklm source add - 等待素材处理完成:直到所有素材状态为READY — 生成前必须完成
notebooklm source list --json - (运行前需确认) — 如果遇到速率限制:等待5分钟,重试一次
notebooklm generate audio "Focus on [specific angle]" - 记录返回的成果物ID
- 稍后使用检查状态
notebooklm artifact list - 完成后使用下载(运行前需确认)
notebooklm download audio ./podcast.mp3
Research to Podcast (Automated with Subagent)
从研究到播客(使用子Agent自动化)
Time: 5-10 minutes, but continues in background
When user wants full automation (generate and download when ready):
- Create notebook and add sources as usual
- Wait for sources to be ready (use or check
source wait)source list --json - Run → parse
notebooklm generate audio "..." --jsonfrom outputartifact_id - Spawn a background agent using Task tool:
Task( prompt="Wait for artifact {artifact_id} in notebook {notebook_id} to complete, then download. Use: notebooklm artifact wait {artifact_id} -n {notebook_id} --timeout 600 Then: notebooklm download audio ./podcast.mp3 -a {artifact_id} -n {notebook_id}", subagent_type="general-purpose" ) - Main conversation continues while agent waits
Error handling in subagent:
- If returns exit code 2 (timeout): Report timeout, suggest checking
artifact waitartifact list - If download fails: Check if artifact status is COMPLETED first
Benefits: Non-blocking, user can do other work, automatic download on completion
耗时: 5-10分钟,但在后台运行
当用户需要完全自动化(生成完成后自动下载):
- 按常规方式创建笔记本并添加素材
- 等待素材处理完成(使用或检查
source wait)source list --json - 运行→ 从输出中解析
notebooklm generate audio "..." --jsonartifact_id - 使用Task工具启动后台Agent:
Task( prompt="等待笔记本{notebook_id}中的成果物{artifact_id}完成,然后下载。 使用命令:notebooklm artifact wait {artifact_id} -n {notebook_id} --timeout 600 然后执行:notebooklm download audio ./podcast.mp3 -a {artifact_id} -n {notebook_id}", subagent_type="general-purpose" ) - 主对话继续进行,同时Agent在后台运行
子Agent错误处理:
- 如果返回退出码2(超时):报告超时,建议检查
artifact waitartifact list - 如果下载失败:先检查成果物状态是否为COMPLETED
优势: 非阻塞,用户可同时进行其他操作,完成后自动下载
Document Analysis
文档分析
Time: 1-2 minutes
notebooklm create "Analysis: [project]"- (or URLs)
notebooklm source add ./doc.pdf notebooklm ask "Summarize the key points"notebooklm ask "What are the main arguments?"- Continue chatting as needed
耗时: 1-2分钟
notebooklm create "Analysis: [project]"- (或URL)
notebooklm source add ./doc.pdf notebooklm ask "Summarize the key points"notebooklm ask "What are the main arguments?"- 根据需要继续对话
Bulk Import
批量导入
Time: Varies by source count
notebooklm create "Collection: [name]"- Add multiple sources:
bash
notebooklm source add "https://url1.com" notebooklm source add "https://url2.com" notebooklm source add ./local-file.pdf - to verify
notebooklm source list
Source limits: Max 50 sources per notebook
Supported types: PDFs, YouTube URLs, web URLs, Google Docs, text files, Markdown, Word docs, audio files, video files, images
耗时: 取决于素材数量
notebooklm create "Collection: [name]"- 添加多个素材:
bash
notebooklm source add "https://url1.com" notebooklm source add "https://url2.com" notebooklm source add ./local-file.pdf - 验证导入结果
notebooklm source list
素材限制: 每个笔记本最多50个素材
支持类型: PDF、YouTube URL、网页URL、Google Docs、文本文件、Markdown、Word文档、音频文件、视频文件、图片
Bulk Import with Source Waiting (Subagent Pattern)
带素材等待的批量导入(子Agent模式)
Time: Varies by source count
When adding multiple sources and needing to wait for processing before chat/generation:
- Add sources with to capture IDs:
--jsonbashnotebooklm source add "https://url1.com" --json # → {"source_id": "abc..."} notebooklm source add "https://url2.com" --json # → {"source_id": "def..."} - Spawn a background agent to wait for all sources:
Task( prompt="Wait for sources {source_ids} in notebook {notebook_id} to be ready. For each: notebooklm source wait {id} -n {notebook_id} --timeout 120 Report when all ready or if any fail.", subagent_type="general-purpose" ) - Main conversation continues while agent waits
- Once sources are ready, proceed with chat or generation
Why wait for sources? Sources must be indexed before chat or generation. Takes 10-60 seconds per source.
耗时: 取决于素材数量
当添加多个素材且需要等待处理完成后再进行对话/生成时:
- 使用添加素材以捕获ID:
--jsonbashnotebooklm source add "https://url1.com" --json # → {"source_id": "abc..."} notebooklm source add "https://url2.com" --json # → {"source_id": "def..."} - 启动后台Agent等待所有素材处理完成:
Task( prompt="等待笔记本{notebook_id}中的素材{source_ids}处理完成。 对每个素材执行:notebooklm source wait {id} -n {notebook_id} --timeout 120 当所有素材准备就绪或有素材失败时进行报告。", subagent_type="general-purpose" ) - 主对话继续进行,同时Agent在后台运行
- 素材准备就绪后,继续进行对话或生成操作
为什么要等待素材? 素材必须完成索引后才能进行对话或生成。每个素材需要10-60秒。
Deep Web Research (Subagent Pattern)
深度网页研究(子Agent模式)
Time: 2-5 minutes, runs in background
Deep research finds and analyzes web sources on a topic:
- Create notebook:
notebooklm create "Research: [topic]" - Start deep research (non-blocking):
bash
notebooklm source add-research "topic query" --mode deep --no-wait - Spawn a background agent to wait and import:
Task( prompt="Wait for research in notebook {notebook_id} to complete and import sources. Use: notebooklm research wait -n {notebook_id} --import-all --timeout 300 Report how many sources were imported.", subagent_type="general-purpose" ) - Main conversation continues while agent waits
- When agent completes, sources are imported automatically
Alternative (blocking): For simple cases, omit :
--no-waitbash
notebooklm source add-research "topic" --mode deep --import-all耗时: 2-5分钟,后台运行
深度研究可查找并分析指定主题的网页素材:
- 创建笔记本:
notebooklm create "Research: [topic]" - 启动深度研究(非阻塞):
bash
notebooklm source add-research "topic query" --mode deep --no-wait - 启动后台Agent等待并导入素材:
Task( prompt="等待笔记本{notebook_id}中的研究完成并导入素材。 使用命令:notebooklm research wait -n {notebook_id} --import-all --timeout 300 报告导入的素材数量。", subagent_type="general-purpose" ) - 主对话继续进行,同时Agent在后台运行
- Agent完成后,素材将自动导入
替代方案(阻塞式): 对于简单场景,省略:
--no-waitbash
notebooklm source add-research "topic" --mode deep --import-allBlocks for up to 5 minutes
最多阻塞5分钟
**When to use each mode:**
- `--mode fast`: Specific topic, quick overview needed (5-10 sources, seconds)
- `--mode deep`: Broad topic, comprehensive analysis needed (20+ sources, 2-5 min)
**Research sources:**
- `--from web`: Search the web (default)
- `--from drive`: Search Google Drive
**模式选择:**
- `--mode fast`: 特定主题,需要快速概述(5-10个素材,数秒完成)
- `--mode deep`: 广泛主题,需要全面分析(20+个素材,2-5分钟完成)
**研究来源:**
- `--from web`: 搜索网页(默认)
- `--from drive`: 搜索Google DriveOutput Style
输出风格
Progress updates: Brief status for each step
- "Creating notebook 'Research: AI'..."
- "Adding source: https://example.com..."
- "Starting audio generation... (task ID: abc123)"
Fire-and-forget for long operations:
- Start generation, return artifact ID immediately
- Do NOT poll or wait in main conversation - generation takes 5-45 minutes (see timing table)
- User checks status manually, OR use subagent with
artifact wait
JSON output: Use flag for machine-readable output:
--jsonbash
notebooklm list --json
notebooklm auth check --json
notebooklm source list --json
notebooklm artifact list --jsonJSON schemas (key fields):
notebooklm list --jsonjson
{"notebooks": [{"id": "...", "title": "...", "created_at": "..."}]}notebooklm auth check --jsonjson
{"checks": {"storage_exists": true, "json_valid": true, "cookies_present": true, "sid_cookie": true, "token_fetch": true}, "details": {"storage_path": "...", "auth_source": "file", "cookies_found": ["SID", "HSID", "..."], "cookie_domains": [".google.com"]}}notebooklm source list --jsonjson
{"sources": [{"id": "...", "title": "...", "status": "ready|processing|error"}]}notebooklm artifact list --jsonjson
{"artifacts": [{"id": "...", "title": "...", "type": "Audio Overview", "status": "in_progress|pending|completed|unknown"}]}Status values:
- Sources: →
processing(orready)error - Artifacts: or
pending→in_progress(orcompleted)unknown
进度更新: 每个步骤的简短状态
- "正在创建笔记本'Research: AI'..."
- "正在添加素材:https://example.com..."
- "正在启动音频生成...(任务ID:abc123)"
长时操作的即发即忘模式:
- 启动生成后立即返回成果物ID
- 主对话中不轮询或等待 - 生成需要5-45分钟(见时间表格)
- 用户手动检查状态,或使用子Agent的命令
artifact wait
JSON输出: 使用标志获取机器可读输出:
--jsonbash
notebooklm list --json
notebooklm auth check --json
notebooklm source list --json
notebooklm artifact list --jsonJSON schema(关键字段):
notebooklm list --jsonjson
{"notebooks": [{"id": "...", "title": "...", "created_at": "..."}]}notebooklm auth check --jsonjson
{"checks": {"storage_exists": true, "json_valid": true, "cookies_present": true, "sid_cookie": true, "token_fetch": true}, "details": {"storage_path": "...", "auth_source": "file", "cookies_found": ["SID", "HSID", "..."], "cookie_domains": [".google.com"]}}notebooklm source list --jsonjson
{"sources": [{"id": "...", "title": "...", "status": "ready|processing|error"}]}notebooklm artifact list --jsonjson
{"artifacts": [{"id": "...", "title": "...", "type": "Audio Overview", "status": "in_progress|pending|completed|unknown"}]}状态值:
- 素材:→
processing(或ready)error - 成果物:或
pending→in_progress(或completed)unknown
Error Handling
错误处理
On failure, offer the user a choice:
- Retry the operation
- Skip and continue with something else
- Investigate the error
Error decision tree:
| Error | Cause | Action |
|---|---|---|
| Auth/cookie error | Session expired | Run |
| "No notebook context" | Context not set | Use |
| "No result found for RPC ID" | Rate limiting | Wait 5-10 min, retry |
| Google rate limit | Wait and retry later |
| Download fails | Generation incomplete | Check |
| Invalid notebook/source ID | Wrong ID | Run |
| RPC protocol error | Google changed APIs | May need CLI update |
操作失败时,为用户提供选择:
- 重试操作
- 跳过并继续其他操作
- 调查错误原因
错误决策树:
| 错误 | 原因 | 操作 |
|---|---|---|
| 身份验证/ cookie错误 | 会话过期 | 运行 |
| "No notebook context" | 未设置上下文 | 使用 |
| "No result found for RPC ID" | 速率限制 | 等待5-10分钟后重试 |
| Google速率限制 | 稍后等待并重试 |
| 下载失败 | 生成未完成 | 检查 |
| 无效的笔记本/素材ID | ID错误 | 运行 |
| RPC协议错误 | Google API变更 | 可能需要更新CLI |
Exit Codes
退出码
All commands use consistent exit codes:
| Code | Meaning | Action |
|---|---|---|
| 0 | Success | Continue |
| 1 | Error (not found, processing failed) | Check stderr, see Error Handling |
| 2 | Timeout (wait commands only) | Extend timeout or check status manually |
Examples:
- returns 1 if source not found or processing failed
source wait - returns 2 if timeout reached before completion
artifact wait - returns 1 if rate limited (check stderr for details)
generate
所有命令使用一致的退出码:
| 代码 | 含义 | 操作 |
|---|---|---|
| 0 | 成功 | 继续 |
| 1 | 错误(未找到、处理失败) | 检查stderr,参考错误处理部分 |
| 2 | 超时(仅等待命令) | 延长超时时间或手动检查状态 |
示例:
- 如果素材未找到或处理失败返回1
source wait - 如果在超时前未完成返回2
artifact wait - 如果遇到速率限制返回1(检查stderr获取详细信息)
generate
Known Limitations
已知限制
Rate limiting: Audio, video, quiz, flashcards, infographic, and slide deck generation may fail due to Google's rate limits. This is an API limitation, not a bug.
Reliable operations: These always work:
- Notebooks (list, create, delete, rename)
- Sources (add, list, delete)
- Chat/queries
- Mind-map, study-guide, report, data-table generation
Unreliable operations: These may fail with rate limiting:
- Audio (podcast) generation
- Video generation
- Quiz and flashcard generation
- Infographic and slide deck generation
Workaround: If generation fails:
- Check status:
notebooklm artifact list - Retry after 5-10 minutes
- Use the NotebookLM web UI as fallback
Processing times vary significantly. Use the subagent pattern for long operations:
| Operation | Typical time | Suggested timeout |
|---|---|---|
| Source processing | 30s - 10 min | 600s |
| Research (fast) | 30s - 2 min | 180s |
| Research (deep) | 15 - 30+ min | 1800s |
| Notes | instant | n/a |
| Mind-map | instant (sync) | n/a |
| Quiz, flashcards | 5 - 15 min | 900s |
| Report, data-table | 5 - 15 min | 900s |
| Audio generation | 10 - 20 min | 1200s |
| Video generation | 15 - 45 min | 2700s |
Polling intervals: When checking status manually, poll every 15-30 seconds to avoid excessive API calls.
速率限制: 音频、视频、测验、抽认卡、信息图和幻灯片生成可能因Google的速率限制而失败。这是API限制,而非Bug。
可靠操作: 以下操作始终有效:
- 笔记本(列出、创建、删除、重命名)
- 素材(添加、列出、删除)
- 对话/查询
- 思维导图、学习指南、报告、数据表生成
不可靠操作: 以下操作可能因速率限制失败:
- 音频(播客)生成
- 视频生成
- 测验和抽认卡生成
- 信息图和幻灯片生成
解决方法: 如果生成失败:
- 检查状态:
notebooklm artifact list - 5-10分钟后重试
- 作为备选方案使用NotebookLM网页界面
处理时间差异很大。 对于长时操作使用子Agent模式:
| 操作 | 典型耗时 | 建议超时时间 |
|---|---|---|
| 素材处理 | 30秒 - 10分钟 | 600秒 |
| 快速研究 | 30秒 - 2分钟 | 180秒 |
| 深度研究 | 15 - 30+分钟 | 1800秒 |
| 笔记 | 即时 | 不适用 |
| 思维导图 | 即时(同步) | 不适用 |
| 测验、抽认卡 | 5 - 15分钟 | 900秒 |
| 报告、数据表 | 5 - 15分钟 | 900秒 |
| 音频生成 | 10 - 20分钟 | 1200秒 |
| 视频生成 | 15 - 45分钟 | 2700秒 |
轮询间隔: 手动检查状态时,每15-30秒轮询一次,避免过多API调用。
Language Configuration
语言配置
Language setting controls the output language for generated artifacts (audio, video, etc.).
Important: Language is a GLOBAL setting that affects all notebooks in your account.
bash
undefined语言设置控制生成成果物(音频、视频等)的输出语言。
重要提示: 语言是全局设置,会影响您账户中的所有笔记本。
bash
undefinedList all 80+ supported languages with native names
列出80+种支持的语言及本地名称
notebooklm language list
notebooklm language list
Show current language setting
显示当前语言设置
notebooklm language get
notebooklm language get
Set language for artifact generation
设置成果物生成语言
notebooklm language set zh_Hans # Simplified Chinese
notebooklm language set ja # Japanese
notebooklm language set en # English (default)
**Common language codes:**
| Code | Language |
|------|----------|
| `en` | English |
| `zh_Hans` | 中文(简体) - Simplified Chinese |
| `zh_Hant` | 中文(繁體) - Traditional Chinese |
| `ja` | 日本語 - Japanese |
| `ko` | 한국어 - Korean |
| `es` | Español - Spanish |
| `fr` | Français - French |
| `de` | Deutsch - German |
| `pt_BR` | Português (Brasil) |
**Override per command:** Use `--language` flag on generate commands:
```bash
notebooklm generate audio --language ja # Japanese podcast
notebooklm generate video --language zh_Hans # Chinese videoOffline mode: Use flag to skip server sync:
--localbash
notebooklm language set zh_Hans --local # Save locally only
notebooklm language get --local # Read local config onlynotebooklm language set zh_Hans # 简体中文
notebooklm language set ja # 日语
notebooklm language set en # 英语(默认)
**常用语言代码:**
| 代码 | 语言 |
|------|----------|
| `en` | 英语 |
| `zh_Hans` | 中文(简体) |
| `zh_Hant` | 中文(繁體) |
| `ja` | 日本語 |
| `ko` | 한국어 |
| `es` | Español - 西班牙语 |
| `fr` | Français - 法语 |
| `de` | Deutsch - 德语 |
| `pt_BR` | Português (Brasil) - 巴西葡萄牙语 |
**命令级覆盖:** 在生成命令中使用`--language`标志:
```bash
notebooklm generate audio --language ja # 日语播客
notebooklm generate video --language zh_Hans # 中文视频离线模式: 使用标志跳过服务器同步:
--localbash
notebooklm language set zh_Hans --local # 仅本地保存
notebooklm language get --local # 仅读取本地配置Troubleshooting
故障排除
bash
notebooklm --help # Main commands
notebooklm auth check # Diagnose auth issues
notebooklm auth check --test # Full auth validation with network test
notebooklm notebook --help # Notebook management
notebooklm source --help # Source management
notebooklm research --help # Research status/wait
notebooklm generate --help # Content generation
notebooklm artifact --help # Artifact management
notebooklm download --help # Download content
notebooklm language --help # Language settingsDiagnose auth: - shows cookie domains, storage path, validation status
Re-authenticate:
Check version:
Update skill:
notebooklm auth checknotebooklm loginnotebooklm --versionnotebooklm skill installbash
notebooklm --help # 主命令帮助
notebooklm auth check # 诊断身份验证问题
notebooklm auth check --test # 完整身份验证验证(含网络测试)
notebooklm notebook --help # 笔记本管理帮助
notebooklm source --help # 素材管理帮助
notebooklm research --help # 研究状态/等待帮助
notebooklm generate --help # 内容生成帮助
notebooklm artifact --help # 成果物管理帮助
notebooklm download --help # 内容下载帮助
notebooklm language --help # 语言设置帮助诊断身份验证: - 显示cookie域、存储路径、验证状态
重新身份验证:
检查版本:
更新技能:
notebooklm auth checknotebooklm loginnotebooklm --versionnotebooklm skill install