---
name: notebooklm-research
description: |
  Research and source discovery for NotebookLM. Web/Drive research, auto-import, and source text extraction.
  Triggers: research topic, find sources, web research, 리서치, 자료 조사, NotebookLM 연구.
---

# NotebookLM Research

Research workflows for NotebookLM: web research, Drive search, auto-import, and source content extraction.

## Prerequisites

```bash
pip install notebooklm-py
playwright install chromium
notebooklm login
```

## When This Skill Activates

- "Research [topic] in NotebookLM"
- "Find sources about X"
- "Do web research on Y"
- "Search my Drive for documents"
- Korean: "리서치 해줘", "자료 찾아줘", "웹 검색"

## Research Modes

| Mode | Sources Found | Time | Use Case |
|------|---------------|------|----------|
| `fast` | 5-10 | seconds | Quick overview |
| `deep` | 20+ | 2-5 min | Comprehensive research |

## Quick Reference

### Web Research

```bash
# Fast research (default)
notebooklm source add-research "artificial intelligence trends"

# Deep research with auto-import
notebooklm source add-research "climate change policy" --mode deep --import-all

# Deep research (non-blocking, wait separately)
notebooklm source add-research "topic" --mode deep --no-wait
notebooklm research wait --import-all
```

### Drive Research

```bash
# Search Google Drive
notebooklm source add-research "quarterly report" --from drive

# Deep Drive search
notebooklm source add-research "project docs" --from drive --mode deep
```

### Research Status

```bash
# Check ongoing research
notebooklm research status

# Wait for completion
notebooklm research wait
notebooklm research wait --import-all  # Auto-import found sources
```

## Source Content Extraction

```bash
# Get indexed fulltext
notebooklm source fulltext <source_id>
notebooklm source fulltext <source_id> --json

# Get AI-generated guide
notebooklm source guide <source_id>
```

## Workflow: Research to Analysis

```bash
# 1. Create notebook
notebooklm create "AI Research Project"

# 2. Run deep research
notebooklm source add-research "large language models 2024" --mode deep --no-wait

# 3. Wait and import (can spawn subagent for this)
notebooklm research wait --import-all

# 4. Verify sources
notebooklm source list

# 5. Start analysis
notebooklm ask "What are the key trends?"
```

## Subagent Pattern for Deep Research

For non-blocking deep research:

```python
# Main conversation
notebooklm source add-research "topic" --mode deep --no-wait

# Spawn subagent to wait
Task(
  prompt="Wait for research in notebook {id} and import sources.
          Use: notebooklm research wait -n {id} --import-all --timeout 300
          Report how many sources were imported.",
  subagent_type="general-purpose"
)
```

## Autonomy Rules

**Auto-run:**
- `notebooklm research status`
- `notebooklm source list`
- `notebooklm source fulltext`
- `notebooklm source guide`

**Ask first:**
- `notebooklm source add-research` (modifies notebook)
- `notebooklm research wait --import-all` (long-running)

## Tips

1. **Use deep mode** for comprehensive research
2. **Use --no-wait** for non-blocking operations
3. **Spawn subagent** for long waits
4. **Check research status** before importing

## Error Handling

| Error | Solution |
|-------|----------|
| No results | Try different keywords |
| Timeout | Extend timeout or check status |
| Rate limit | Wait and retry |