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

Run batch analysis on your document directory:

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

Review categorization - identify high-value files by category

Select priority documents - focus on strategy, financial, and legal documents

Phase 2: Document Upload

Go to NotebookLM ()

Create new notebook with descriptive name (e.g., "Business Analysis Q4")

Upload priority documents identified in Phase 1

Group related documents (strategy + financial + legal) for better context

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

Synthesize insights across related documents

Create action item lists from identified recommendations

Develop mitigation strategies for identified risks

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

Batch analyze first - identify which documents deserve AI analysis

Group related documents - upload strategy + financial + legal docs together

Ask specific questions - "What are the risks?" vs "Analyze this"

Create focused notebooks - one per project or business area

Use follow-up questions - each query can build on previous context

Extract actionable insights - focus on what you can act on

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

Problem

Solution

Too many files found	Use specific patterns: --pattern "*strategy*.md"
Upload failed	Check file size limits and format compatibility
Generic answers	Ask more specific questions about business impact
Analysis too broad	Focus on specific aspects: risks, opportunities, compliance
Missing context	Upload related documents together for better analysis
Encoding errors	Scripts automatically handle Unicode issues

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