feat: Add NotebookLM skills (50-53) for Claude Code and Desktop

Add 4 specialized NotebookLM skills based on notebooklm-py library:

- 50-notebooklm-agent: Q&A agent for notebook queries with citations
- 51-notebooklm-automation: Full notebook/source/artifact management
- 52-notebooklm-studio: Content generation (podcasts, videos, quizzes)
- 53-notebooklm-research: Web/Drive research and source discovery

Each skill includes:
- README.md: Overview and quick start
- code/CLAUDE.md: Claude Code version (concise)
- desktop/SKILL.md: Claude Desktop version (with YAML frontmatter)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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# NotebookLM Agent
Q&A agent that answers user questions based on NotebookLM notebooks. Leverages NotebookLM's Gemini-powered analysis to provide grounded, citation-backed responses from uploaded sources.
## Use Cases
- Ask questions about documents in a specific notebook
- Get summaries with source citations
- Compare information across multiple sources
- Research assistant workflows
## Prerequisites
```bash
pip install notebooklm-py
playwright install chromium
notebooklm login # One-time browser auth
```
## Quick Start
```bash
# Set active notebook
notebooklm use <notebook_id>
# Ask questions
notebooklm ask "What are the key findings?"
notebooklm ask "Compare approaches from different sources"
# Get citations with JSON output
notebooklm ask "Summarize the methodology" --json
```
## Versions
| Version | Path | Purpose |
|---------|------|---------|
| Claude Code | `code/` | CLAUDE.md for Claude Code integration |
| Claude Desktop | `desktop/` | SKILL.md for Claude Desktop skills |
## Related Skills
- [51-notebooklm-automation](../51-notebooklm-automation/) - Full automation workflows
- [52-notebooklm-studio](../52-notebooklm-studio/) - Content generation (podcasts, videos)
- [53-notebooklm-research](../53-notebooklm-research/) - Research and source discovery

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# NotebookLM Agent - Claude Code
Q&A agent that answers questions using NotebookLM's Gemini-powered analysis with source citations.
## Prerequisites
```bash
pip install notebooklm-py
playwright install chromium
notebooklm login # One-time auth
```
## Commands
```bash
# List notebooks
notebooklm list
# Set context
notebooklm use <notebook_id>
# Ask questions
notebooklm ask "What are the key findings?"
notebooklm ask "Elaborate on point 2" # continues conversation
notebooklm ask "New topic" --new # new conversation
# With citations (JSON output)
notebooklm ask "Summarize" --json
# Query specific sources
notebooklm ask "Compare" -s source1 -s source2
```
## Autonomy
**Auto-run:** `list`, `status`, `source list`, `ask`
**Ask first:** `delete`, `source add`
## JSON Output Format
```json
{
"answer": "Response with [1] [2] citations",
"references": [
{"source_id": "...", "citation_number": 1, "cited_text": "..."}
]
}
```
## Error Recovery
| Error | Fix |
|-------|-----|
| No context | `notebooklm use <id>` |
| Auth error | `notebooklm login` |

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---
name: notebooklm-agent
description: |
Q&A agent for NotebookLM notebooks. Ask questions and get grounded, citation-backed answers from your sources.
Triggers: ask NotebookLM, query notebook, research question, 노트북 질문, NotebookLM 에이전트.
---
# NotebookLM Agent
Q&A agent that answers questions using NotebookLM's Gemini-powered analysis. Returns grounded responses with source citations.
## Prerequisites
NotebookLM CLI must be installed and authenticated:
```bash
pip install notebooklm-py
playwright install chromium
notebooklm login
```
## When This Skill Activates
- User asks "ask NotebookLM about X"
- User wants to "query my notebook"
- User needs "research answers from sources"
- Korean: "노트북LM에서 찾아줘", "노트북 질문"
## Quick Reference
| Task | Command |
|------|---------|
| List notebooks | `notebooklm list` |
| Set active notebook | `notebooklm use <id>` |
| Ask question | `notebooklm ask "question"` |
| New conversation | `notebooklm ask "question" --new` |
| With citations | `notebooklm ask "question" --json` |
| Specific sources | `notebooklm ask "q" -s src1 -s src2` |
## Workflow
### 1. Select Notebook
```bash
# List available notebooks
notebooklm list
# Set context (use partial ID)
notebooklm use abc123
```
### 2. Ask Questions
```bash
# Simple question
notebooklm ask "What are the main findings?"
# Follow-up (continues conversation)
notebooklm ask "Can you elaborate on point 2?"
# New conversation
notebooklm ask "Different topic" --new
# Query specific sources only
notebooklm ask "Compare these two" -s source1_id -s source2_id
```
### 3. Get Structured Output
For citations and references, use `--json`:
```bash
notebooklm ask "Summarize the methodology" --json
```
Returns:
```json
{
"answer": "The methodology involves... [1] [2]",
"references": [
{"source_id": "abc...", "citation_number": 1, "cited_text": "..."},
{"source_id": "def...", "citation_number": 2, "cited_text": "..."}
]
}
```
## Autonomy Rules
**Run automatically:**
- `notebooklm list` - view notebooks
- `notebooklm status` - check context
- `notebooklm source list` - view sources
- `notebooklm ask "..."` - answer questions
**Ask before running:**
- `notebooklm delete` - destructive operations
- `notebooklm source add` - modifies notebook
## Tips
1. **Set context first**: Always `use` a notebook before asking
2. **Use --json for citations**: Get structured references for research
3. **Continue conversations**: Omit `--new` for follow-up questions
4. **Filter sources**: Use `-s` to query specific documents only
## Error Handling
| Error | Solution |
|-------|----------|
| "No notebook context" | Run `notebooklm use <id>` |
| Auth error | Run `notebooklm login` |
| Source not found | Check `notebooklm source list` |
## Related Skills
- **notebooklm-automation**: Full notebook management
- **notebooklm-studio**: Generate podcasts, videos, quizzes
- **notebooklm-research**: Add sources and research workflows