Initial commit: The Ultimate Antigravity Skills Collection (58 Skills)
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skills/notebooklm/references/usage_patterns.md
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skills/notebooklm/references/usage_patterns.md
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# NotebookLM Skill Usage Patterns
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Advanced patterns for using the NotebookLM skill effectively.
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## Critical: Always Use run.py
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**Every command must use the run.py wrapper:**
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```bash
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# ✅ CORRECT:
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python scripts/run.py auth_manager.py status
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python scripts/run.py ask_question.py --question "..."
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# ❌ WRONG:
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python scripts/auth_manager.py status # Will fail!
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```
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## Pattern 1: Initial Setup
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```bash
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# 1. Check authentication (using run.py!)
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python scripts/run.py auth_manager.py status
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# 2. If not authenticated, setup (Browser MUST be visible!)
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python scripts/run.py auth_manager.py setup
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# Tell user: "Please log in to Google in the browser window"
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# 3. Add first notebook - ASK USER FOR DETAILS FIRST!
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# Ask: "What does this notebook contain?"
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# Ask: "What topics should I tag it with?"
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python scripts/run.py notebook_manager.py add \
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--url "https://notebooklm.google.com/notebook/..." \
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--name "User provided name" \
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--description "User provided description" \ # NEVER GUESS!
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--topics "user,provided,topics" # NEVER GUESS!
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```
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**Critical Notes:**
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- Virtual environment created automatically by run.py
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- Browser MUST be visible for authentication
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- ALWAYS discover content via query OR ask user for notebook metadata
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## Pattern 2: Adding Notebooks (Smart Discovery!)
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**When user shares a NotebookLM URL:**
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**OPTION A: Smart Discovery (Recommended)**
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```bash
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# 1. Query the notebook to discover its content
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python scripts/run.py ask_question.py \
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--question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" \
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--notebook-url "[URL]"
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# 2. Use discovered info to add it
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python scripts/run.py notebook_manager.py add \
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--url "[URL]" \
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--name "[Based on content]" \
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--description "[From discovery]" \
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--topics "[Extracted topics]"
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```
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**OPTION B: Ask User (Fallback)**
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```bash
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# If discovery fails, ask user:
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"What does this notebook contain?"
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"What topics does it cover?"
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# Then add with user-provided info:
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python scripts/run.py notebook_manager.py add \
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--url "[URL]" \
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--name "[User's answer]" \
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--description "[User's description]" \
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--topics "[User's topics]"
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```
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**NEVER:**
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- Guess what's in a notebook
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- Use generic descriptions
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- Skip discovering content
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## Pattern 3: Daily Research Workflow
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```bash
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# Check library
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python scripts/run.py notebook_manager.py list
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# Research with comprehensive questions
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python scripts/run.py ask_question.py \
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--question "Detailed question with all context" \
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--notebook-id notebook-id
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# Follow-up when you see "Is that ALL you need to know?"
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python scripts/run.py ask_question.py \
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--question "Follow-up question with previous context"
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```
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## Pattern 4: Follow-Up Questions (CRITICAL!)
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When NotebookLM responds with "EXTREMELY IMPORTANT: Is that ALL you need to know?":
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```python
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# 1. STOP - Don't respond to user yet
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# 2. ANALYZE - Is answer complete?
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# 3. If gaps exist, ask follow-up:
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python scripts/run.py ask_question.py \
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--question "Specific follow-up with context from previous answer"
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# 4. Repeat until complete
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# 5. Only then synthesize and respond to user
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```
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## Pattern 5: Multi-Notebook Research
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```python
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# Query different notebooks for comparison
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python scripts/run.py notebook_manager.py activate --id notebook-1
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python scripts/run.py ask_question.py --question "Question"
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python scripts/run.py notebook_manager.py activate --id notebook-2
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python scripts/run.py ask_question.py --question "Same question"
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# Compare and synthesize answers
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```
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## Pattern 6: Error Recovery
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```bash
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# If authentication fails
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python scripts/run.py auth_manager.py status
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python scripts/run.py auth_manager.py reauth # Browser visible!
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# If browser crashes
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python scripts/run.py cleanup_manager.py --preserve-library
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python scripts/run.py auth_manager.py setup # Browser visible!
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# If rate limited
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# Wait or switch accounts
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python scripts/run.py auth_manager.py reauth # Login with different account
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```
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## Pattern 7: Batch Processing
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```bash
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#!/bin/bash
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NOTEBOOK_ID="notebook-id"
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QUESTIONS=(
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"First comprehensive question"
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"Second comprehensive question"
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"Third comprehensive question"
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)
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for question in "${QUESTIONS[@]}"; do
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echo "Asking: $question"
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python scripts/run.py ask_question.py \
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--question "$question" \
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--notebook-id "$NOTEBOOK_ID"
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sleep 2 # Avoid rate limits
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done
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```
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## Pattern 8: Automated Research Script
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```python
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#!/usr/bin/env python
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import subprocess
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def research_topic(topic, notebook_id):
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# Comprehensive question
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question = f"""
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Explain {topic} in detail:
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1. Core concepts
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2. Implementation details
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3. Best practices
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4. Common pitfalls
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5. Examples
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"""
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result = subprocess.run([
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"python", "scripts/run.py", "ask_question.py",
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"--question", question,
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"--notebook-id", notebook_id
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], capture_output=True, text=True)
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return result.stdout
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```
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## Pattern 9: Notebook Organization
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```python
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# Organize by domain - with proper metadata
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# ALWAYS ask user for descriptions!
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# Backend notebooks
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add_notebook("Backend API", "Complete API documentation", "api,rest,backend")
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add_notebook("Database", "Schema and queries", "database,sql,backend")
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# Frontend notebooks
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add_notebook("React Docs", "React framework documentation", "react,frontend")
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add_notebook("CSS Framework", "Styling documentation", "css,styling,frontend")
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# Search by domain
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python scripts/run.py notebook_manager.py search --query "backend"
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python scripts/run.py notebook_manager.py search --query "frontend"
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```
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## Pattern 10: Integration with Development
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```python
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# Query documentation during development
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def check_api_usage(api_endpoint):
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result = subprocess.run([
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"python", "scripts/run.py", "ask_question.py",
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"--question", f"Parameters and response format for {api_endpoint}",
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"--notebook-id", "api-docs"
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], capture_output=True, text=True)
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# If follow-up needed
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if "Is that ALL you need" in result.stdout:
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# Ask for examples
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follow_up = subprocess.run([
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"python", "scripts/run.py", "ask_question.py",
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"--question", f"Show code examples for {api_endpoint}",
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"--notebook-id", "api-docs"
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], capture_output=True, text=True)
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return combine_answers(result.stdout, follow_up.stdout)
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```
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## Best Practices
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### 1. Question Formulation
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- Be specific and comprehensive
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- Include all context in each question
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- Request structured responses
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- Ask for examples when needed
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### 2. Notebook Management
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- **ALWAYS ask user for metadata**
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- Use descriptive names
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- Add comprehensive topics
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- Keep URLs current
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### 3. Performance
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- Batch related questions
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- Use parallel processing for different notebooks
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- Monitor rate limits (50/day)
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- Switch accounts if needed
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### 4. Error Handling
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- Always use run.py to prevent venv issues
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- Check auth before operations
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- Implement retry logic
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- Have fallback notebooks ready
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### 5. Security
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- Use dedicated Google account
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- Never commit data/ directory
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- Regularly refresh auth
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- Track all access
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## Common Workflows for Claude
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### Workflow 1: User Sends NotebookLM URL
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```python
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# 1. Detect URL in message
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if "notebooklm.google.com" in user_message:
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url = extract_url(user_message)
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# 2. Check if in library
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notebooks = run("notebook_manager.py list")
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if url not in notebooks:
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# 3. ASK USER FOR METADATA (CRITICAL!)
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name = ask_user("What should I call this notebook?")
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description = ask_user("What does this notebook contain?")
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topics = ask_user("What topics does it cover?")
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# 4. Add with user-provided info
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run(f"notebook_manager.py add --url {url} --name '{name}' --description '{description}' --topics '{topics}'")
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# 5. Use the notebook
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answer = run(f"ask_question.py --question '{user_question}'")
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```
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### Workflow 2: Research Task
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```python
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# 1. Understand task
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task = "Implement feature X"
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# 2. Formulate comprehensive questions
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questions = [
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"Complete implementation guide for X",
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"Error handling for X",
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"Performance considerations for X"
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]
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# 3. Query with follow-ups
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for q in questions:
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answer = run(f"ask_question.py --question '{q}'")
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# Check if follow-up needed
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if "Is that ALL you need" in answer:
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# Ask more specific question
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follow_up = run(f"ask_question.py --question 'Specific detail about {q}'")
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# 4. Synthesize and implement
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```
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## Tips and Tricks
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1. **Always use run.py** - Prevents all venv issues
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2. **Ask for metadata** - Never guess notebook contents
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3. **Use verbose questions** - Include all context
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4. **Follow up automatically** - When you see the prompt
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5. **Monitor rate limits** - 50 queries per day
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6. **Batch operations** - Group related queries
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7. **Export important answers** - Save locally
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8. **Version control notebooks** - Track changes
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9. **Test auth regularly** - Before important tasks
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10. **Document everything** - Keep notes on notebooks
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## Quick Reference
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```bash
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# Always use run.py!
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python scripts/run.py [script].py [args]
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# Common operations
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run.py auth_manager.py status # Check auth
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run.py auth_manager.py setup # Login (browser visible!)
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run.py notebook_manager.py list # List notebooks
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run.py notebook_manager.py add ... # Add (ask user for metadata!)
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run.py ask_question.py --question ... # Query
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run.py cleanup_manager.py ... # Clean up
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```
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**Remember:** When in doubt, use run.py and ask the user for notebook details!
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