Notebooklm

NotebookLM Local File Analyzer

Analyze your local documents with Google NotebookLM's AI to get source-grounded insights, risk assessments, and actionable recommendations. Upload your files once, then query them repeatedly for different perspectives.

When to Use This Skill

Use this skill when user:

• Has local business documents (strategy plans, financial reports, proposals)
• Wants AI analysis of specific documents with source grounding
• Needs risk assessment, competitive analysis, or business insights
• Wants to analyze multiple related documents together
• Needs to extract actionable insights from business documentation

Quick Start

Step 1: One-Time Setup

python scripts/setup_notebooklm.py

Step 2: Analyze Your Files

Batch Analysis (recommended):

python scripts/batch_analyzer.py "your/folder" --pattern "*.md"

Single File Analysis:

python scripts/local_analyzer.py "file.md" --upload

Query Uploaded Documents:

python scripts/quick_query.py "What are the key risks in this business plan?" --notebook-url "notebook-url"

Core Workflows

Workflow 1: Business Document Analysis

Upload business documents and get strategic insights:

# Analyze business strategy files
python scripts/batch_analyzer.py "Business/Strategy" --pattern "*.md"

# Upload high-priority files to NotebookLM
python scripts/local_analyzer.py "strategy_plan.md" --upload

# Get strategic insights
python scripts/quick_query.py "Identify 3 competitive advantages and implementation challenges" --notebook-url "url"

Workflow 2: Financial Analysis

Analyze financial documents for risks and opportunities:

# Find financial documents
python scripts/batch_analyzer.py "Finance" --pattern "*.md"

# Query for financial insights
python scripts/quick_query.py "What are the key financial risks and ROI projections?" --notebook-url "url"

Workflow 3: Risk & Compliance Analysis

Get risk assessments and compliance insights:

python scripts/quick_query.py "What compliance or regulatory issues should be addressed?" --notebook-url "url"
python scripts/quick_query.py "Identify top 5 risks and mitigation strategies" --notebook-url "url"

Helper Scripts (Black Box Usage)

scripts/batch_analyzer.py

Analyze entire directories and identify high-value files:

python scripts/batch_analyzer.py "directory" --pattern "*.md" --output "analysis_report.md"

Features:

• File categorization: Business Strategy, Financial, Technical, Legal, Marketing
• Priority identification: Highlights high-value files for upload
• Workflow guidance: Provides step-by-step analysis recommendations
• Report generation: Creates structured analysis reports

scripts/local_analyzer.py

Upload and analyze individual files:

python scripts/local_analyzer.py "file.md" --upload
python scripts/local_analyzer.py "file.md" --notebook-url "url" --question "Custom question"

Features:

• Upload guidance: Step-by-step NotebookLM upload instructions
• File analysis: Provides metadata and size information
• Custom queries: Supports targeted analysis questions

scripts/quick_query.py

Query uploaded documents:

python scripts/quick_query.py "question" --notebook-url "url"

Features:

• Direct querying: Ask specific questions about uploaded documents
• Source grounding: Get citation-backed answers from your files
• Unicode handling: Works across different operating systems

Powerful Use Cases

Business Strategy Analysis

# Upload strategy documents
python scripts/local_analyzer.py "strategy_document.md" --upload

# Get strategic insights
python scripts/quick_query.py "What competitive advantages does this strategy establish?" --notebook-url "url"
python scripts/quick_query.py "Identify 3-5 actionable insights and implementation timeline" --notebook-url "url"

Financial Risk Assessment

# Upload financial documents
python scripts/local_analyzer.py "financial_report.md" --upload

# Get financial analysis
python scripts/quick_query.py "Summarize financial implications and ROI projections" --notebook-url "url"
python scripts/quick_query.py "What are the top financial risks and mitigation strategies?" --notebook-url "url"

Proposal & Contract Analysis

# Upload legal/business documents
python scripts/local_analyzer.py "proposal_document.md" --upload

# Get compliance insights
python scripts/quick_query.py "What compliance or regulatory issues should be addressed?" --notebook-url "url"
python scripts/quick_query.py "Identify potential legal risks and recommended safeguards" --notebook-url "url"

Standard Operating Procedure (SOP)

Phase 1: Document Discovery

1. Run batch analysis on your document directory:

   python scripts/batch_analyzer.py "your/document/folder" --pattern "*.md"
   

2. Review categorization - identify high-value files by category
3. Select priority documents - focus on strategy, financial, and legal documents

Phase 2: Document Upload

1. Go to NotebookLM (https://notebooklm.google.com)
2. Create new notebook with descriptive name (e.g., "Business Analysis Q4")
3. Upload priority documents identified in Phase 1
4. Group related documents (strategy + financial + legal) for better context
5. Copy notebook URL for querying

Phase 3: Intelligence Extraction

Ask targeted questions based on document type:

Strategy Documents:

• "What are the key competitive advantages and market opportunities?"
• "Identify implementation challenges and recommended solutions"
• "What are the success metrics and milestones?"

Financial Documents:

• "Summarize key financial metrics and projections"
• "What are the primary financial risks and mitigation strategies?"
• "What ROI and growth opportunities are identified?"

Legal/Compliance Documents:

• "What compliance requirements and deadlines must be met?"
• "Identify potential legal risks and recommended safeguards"
• "What regulatory issues need immediate attention?"

Proposals/Contracts:

• "What are the key obligations and deliverables?"
• "Identify potential risks and negotiation points"
• "What success criteria and performance metrics are defined?"

Phase 4: Action Planning

1. Synthesize insights across related documents
2. Create action item lists from identified recommendations
3. Develop mitigation strategies for identified risks
4. Establish monitoring for key metrics and milestones

Common Pitfalls

❌ Don't use for simple document reading - just use Read tool ❌ Don't upload sensitive personal data - NotebookLM is a Google service ❌ Don't expect real-time data - analysis based on uploaded documents ❌ Don't ignore file size limits - check NotebookLM upload limits ❌ Don't forget to organize documents - group related files for better analysis

✅ Always upload related documents together - better context for analysis ✅ Use specific, targeted questions - better than general queries ✅ Batch analyze first - identify high-value files before uploading ✅ Create separate notebooks - organize by project or document type ✅ Follow up with specific questions - dig deeper into insights

Best Practices

1. Batch analyze first - identify which documents deserve AI analysis
2. Group related documents - upload strategy + financial + legal docs together
3. Ask specific questions - "What are the risks?" vs "Analyze this"
4. Create focused notebooks - one per project or business area
5. Use follow-up questions - each query can build on previous context
6. Extract actionable insights - focus on what you can act on
7. Document findings - save key insights for future reference

File Type Support

Recommended formats:

• Markdown (.md) - Best for structured documents
• PDF - Reports, contracts, formal documents
• Word (.docx) - Business documents and proposals
• Plain text (.txt) - Notes and documentation

Optimal for analysis:

• Business plans and strategy documents
• Financial reports and budgets
• Legal agreements and contracts
• Project proposals and specifications
• Market research and analysis

Troubleshooting

Integration Notes

• Claude Code: Use for analyzing local document repositories
• Claude API: Automate document analysis workflows
• Claude.ai: Manual document upload and analysis interface
• Enterprise: Integrate with document management systems for automated analysis