According to AlphaMoat, as of 2026-10, Notebooklm has 15,648 downloads and 20 stars — ranked by downloads #1,789 among 10,914 AI skills in this ranking, and #113 of 745 in Knowledge.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| ClawHub ↗ | 9.5K | 16 | - |
| SkillHub ↗ | 15.6K | 20 | — |
Platforms count differently and update at different times.
Use the §python scripts/run.py§ wrapper to query documents in NotebookLM and add, search or select notebooks. Adding a notebook requires its URL, name, description and topics. First inspect the page or use Smart Add to query the notebook for its content; ask the user for missing details. Never guess or use a generic description. Initial authentication requires the user to log in to Google in a visible browser; the user must upload documents manually. Each question opens a fresh browser session.
Profile published 2026-10-26
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 15.6K | — | 20 |
| 2026-09 | 10.3K | — | 20 |
| 2026-08 | 9.3K | +3.1% | 20 |
| 2026-07 | 9.0K | — | 19 |
| 2026-05 | 7.9K | — | 18 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: notebooklm description: Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses. --- # 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:
```bash
# 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 use `python scripts/run.py [script]`:**
```bash
# ✅ 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:
1. Creates `.venv` if needed
2. Installs all dependencies
3. Activates environment
4. Executes script properly
## Core Workflow
### Step 1: Check Authentication Status
```bash
python scripts/run.py auth_manager.py status
```
If not authenticated, proceed to setup.
### Step 2: Authenticate (One-Time Setup)
```bash
# 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
```bash
# 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
1. Check library: `python scripts/run.py notebook_manager.py list`
2. Ask question: `python scripts/run.py ask_question.py --question "..." --notebook-id ID`
### Step 4: Ask Questions
```bash
# 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:**
1. **STOP** - Do not immediately respond to user
2. **ANALYZE** - Compare answer to user's original request
3. **IDENTIFY GAPS** - Determine if more information needed
4. **ASK FOLLOW-UP** - If gaps exist, immediately ask:
```bash
python scripts/run.py ask_question.py --question "Follow-up with context..."
```
5. **REPEAT** - Continue until information is complete
6. **SYNTHESIZE** - Combine all answers before responding to user
## Script Reference
### Authentication Management (`auth_manager.py`)
```bash
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`)
```bash
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`)
```bash
python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL] [--show-browser]
```
### Data Cleanup (`cleanup_manager.py`)
```bash
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):
```bash
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:
```env
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
1. **Always use run.py** - Handles environment automatically
2. **Check auth first** - Before any operations
3. **Follow-up questions** - Don't stop at first answer
4. **Browser visible for auth** - Required for manual login
5. **Include context** - Each question is independent
6. **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
通过公开搜索URL在16个搜索引擎(7个国内直连+9个国际)中做跨引擎检索、结果去重与多源交叉验证,无需API Key,内置零依赖Python脚本。Use when 用户要求“搜一下/查资料/多找几个来源/核实消息/找公众号文章/排查报错/找论文或开源项目”时。输入自然语言查询词,输出带来源链接与可信度标注的结构化结果。不适用于私有数据检索、批量抓取、需要登录后可见的内容。
Manage notes and knowledge-base content through the IMA OpenAPI.
A summarizer that condenses long text, documents, and web pages into key points and keywords.
通过“查一下、搜一下、搜索、找一下”等自然语言检索公开网页,整理来源并交付横版 PDF、Markdown 与标题链接表。支持用户主动开启、查看和取消技能收藏,保留原报告取回。
A required guide for routing web requests to the right tool.
AI HOT (aihot.virxact.com) 中文 AI 资讯查询 Skill。当用户想知道"今天 AI 圈有什么"、"AI 日报"、"AI HOT"、"AI 资讯"、"AI 热点"、"最近 AI"、"OpenAI/Anthropic/Google 最近发布了什么"、"AI hot today"、"AI...
全球12亿文献知识库(8千万中文期刊可下载)——通过 SmartLib 开放平台 API 提供中外文学术文献检索与下载能力,覆盖 8000 万篇授权中文期刊全文 + 12.28 亿条全球文献元数据(期刊 7.19 亿 / 专利 2.15 亿 / 会议 7155 万 / 学位论文 2473 万 / 标准 268 万)。 能力:中英文关键词检索、文献详情、中文期刊 PDF 全文下载、外文 OA 文献十级渠道免费下载(不消耗配额)、智能关键词扩展、核心期刊优先排序、相关性重排、引文追溯、分类号检索。 配额:首次使用自动注册,免费 100 次检索 + 10 次下载 / 月;耗尽自动弹出套餐(体验卡 / 个人版月 / 专业版月 / 单篇下载 / 下载包),可说「升级 / 充值」唤起;企业 / 机构定制联系 [email protected]。 触发:用户表达"查论文""找文献""检索学术""搜期刊""查专利""找标准""下论文""写文献综述""找参考文献""查 SCI/EI"等意图时启用;也适用"帮我找关于 XX 的论文""写文献综述""找几篇引用支撑论点"。英文:"find papers" "search literature" "write literature review" "find supporting citations"。 调用前必须先用 /consume 获取 consume_token,再凭 token 调 /search(每次计费接口调用都需一次 /consume)。
Calls markitdown through uvx to turn many file types into Markdown.
Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.