notebooklm-knowledge-base-organizer（NotebookLM 知识 base organizer）

NotebookLM Knowledge Base Organizer

Prepares files for optimal use in NotebookLM by intelligently selecting and consolidating sources, converting formats, organizing structure, and ensuring compatibility. The primary constraint is NotebookLM's 50-source limit per notebook. When collections exceed this limit, systematic scoring, prioritization, and strategic merging reduce source count without losing valuable information.

When to Use This Skill

• You have 50+ files and need to optimize for NotebookLM's limit
• Preparing documents for a new NotebookLM notebook
• Converting a messy folder into NotebookLM-ready sources
• Files are in unsupported formats (PPTX, XLSX, complex PDFs)
• Documents exceed 500k words or 200MB per file
• Building a knowledge base for research, projects, or learning
• Large document collections (100-300 files) need intelligent prioritization

What This Skill Does

1. Scores and Prioritizes Sources (when >50 detected) using Relevance, Recency, Uniqueness, and Information Density (0-40 scale)
2. Strategic Merging via time-series (daily to monthly), topic-based (related papers to comprehensive guides), and format consolidation (slides + transcript to unified PDF)
3. Converts to Supported Formats (PPTX to PDF, XLSX to CSV, scanned to OCR)
4. Applies Flat Structure with descriptive snake_case naming
5. Removes Duplicates across formats
6. Splits Large Files exceeding 500k words into parts
7. Optimizes for RAG with smaller, focused documents for better retrieval

NotebookLM Supported Formats

Supported:

• PDF (text-selectable, not scanned images)
• Google Docs, Sheets (<100k tokens), Slides (<100 slides)
• Microsoft Word (.docx)
• Text files (.txt,.md)
• Images (PNG, JPEG, TIFF, WEBP)
• Audio (MP3, WAV, AAC, OGG with clear speech)
• URLs (websites, YouTube, Google Drive links)
• Copy-pasted text

Convert These:

• PPTX to PDF
• XLSX to CSV or Google Sheets
• Scanned PDFs to OCR text-selectable PDF
• Large Sheets to CSV (<100k tokens)

File Limits

Per Source:

• 500,000 words max
• 200MB file size max
• No page limit (word limit matters)

Per Notebook (Free):

• 50 sources maximum -- HARD LIMIT
• 100 notebooks total

Prefer many smaller, focused documents over few large ones for better RAG retrieval. The 50-source limit is the primary optimization constraint.

IMPORTANT: Preserve original file timestamps during all operations. Timestamps are essential for understanding latest additions, recent meeting minutes, and key decisions. Use touch -r original converted after conversions. Include dates in ISO format (YYYY-MM-DD) in all filenames.

How to Use

Prepare these files for NotebookLM - convert formats and organize with descriptive names

Convert all PPTX and XLSX files to NotebookLM-compatible formats

Check if any files exceed NotebookLM's 500k word or 200MB limits

Organize this research folder for a NotebookLM knowledge base

Find duplicate content across different file formats

Split this large PDF into NotebookLM-compatible chunks

Instructions

When a user requests NotebookLM organization, follow these steps.

Step 1: Assess and Prioritize Sources

Count and evaluate before proceeding with any organization.

total_sources=$(find . -type f \( -name "*.pdf" -o -name "*.docx" -o -name "*.txt" -o -name "*.md" -o -name "*.csv" \) | wc -l)
echo "Total sources found: $total_sources"

If total exceeds 50:

1. Score all sources using the 4-dimension rubric (Relevance, Recency, Uniqueness, Density, each 0-10). See references/scoring-system.md for the full rubric, assessment commands, and batch scoring script.
2. Rank and select top candidates using the decision matrix. Target 35-40 auto-keep sources initially. See references/prioritization-strategy.md for the selection process and space-based adjustments.
3. Identify merge candidates -- find time-series patterns, topic clusters, and multi-format duplicates: # Time-series opportunities find. -name "*_20[0-9][0-9]_[0-9][0-9]_*" | \ sed 's/_20[0-9][0-9]_[0-9][0-9]_[0-9][0-9]//' | sort | uniq -c | sort -rn # Topic clusters find. -type f -name "*.pdf" | xargs -I {} basename {}.pdf | \ sed 's/_part_[0-9]*//;s/_[0-9][0-9]*$//' | sort | uniq -c | sort -rn | awk '$1 > 2'
4. Execute strategic merges using appropriate patterns. See references/merging-strategies.md for time-series, topic-based, and format consolidation scripts. Preserve timestamps on all merged outputs.
5. Recount and validate the final total is at or below 50 (ideally 48 to reserve slots for future additions).

Step 2: Understand the Scope

Ask clarifying questions:

• What is the topic/purpose of this knowledge base?
• Which directory contains the source materials?
• Target: single notebook or multiple related notebooks?
• Any files that must stay in original format?
• Is this for research, learning, project documentation, or reference?

Step 3: Analyze Current State

Review files for NotebookLM compatibility:

find . -type f -exec file {} \;
find . -type f -exec du -h {} \; | sort -rh
find . -type f | sed 's/.*\.//' | sort | uniq -c | sort -rn
for f in *.pdf; do pdftotext "$f" - | wc -w; done

Categorize findings:

• Compatible as-is: PDF, DOCX, TXT, MD, images
• Needs conversion: PPTX, XLSX, XLS, PPT, scanned PDFs
• Too large: Files >500k words or >200MB
• Duplicates: Same content in different formats
• Merge candidates: Sources identified for consolidation in Step 1

Step 4: Convert Unsupported Formats

PowerPoint to PDF:

soffice --headless --convert-to pdf *.pptx
touch -r original.pptx converted.pdf  # Preserve timestamp

Excel to CSV:

soffice --headless --convert-to csv:"Text - txt - csv (StarCalc)":44,34,UTF8 *.xlsx
touch -r original.xlsx converted.csv  # Preserve timestamp

Scanned PDF to Searchable:

ocrmypdf input.pdf output_searchable.pdf
touch -r input.pdf output_searchable.pdf  # Preserve timestamp
pdftotext output_searchable.pdf - | wc -w  # Verify text extraction

WARNING: Always run touch -r original converted after every conversion to preserve the original file timestamp.

Step 5: Apply Naming

Use this pattern: category_topic_descriptor_YYYY_MM_DD.ext

Examples:

• research_quantum_computing_basics_2025.pdf
• meeting_notes_project_kickoff_2026_01_15.txt
• client_proposal_acme_corp_final.docx
• reference_api_documentation_v2.md
• data_sales_figures_q4_2025.csv

See references/organization-scripts.md for the automated naming script. Preserve timestamps when renaming: use mv (preserves by default) and verify with stat.

Step 6: Split Large Documents

For files >500k words or >200MB:

pdftotext document.pdf - | wc -w  # Check word count
pdftk large.pdf cat 1-500 output large_part_1.pdf
pdftk large.pdf cat 501-1000 output large_part_2.pdf
touch -r large.pdf large_part_1.pdf large_part_2.pdf  # Preserve timestamps

Name parts by content, not arbitrary numbers:

• annual_report_2025_part_1_executive_summary.pdf
• annual_report_2025_part_2_financials.pdf
• annual_report_2025_part_3_appendices.pdf

Step 7: Consolidation Pass

Perform strategic merging to optimize source count. This step is critical when merge candidates were identified in Step 1 or the collection is near the 50-source limit.

Merging is a primary optimization strategy, not a last resort. Three patterns apply:

• Time-series: Combine chronological documents into period summaries (daily to monthly, weekly to quarterly)
• Topic-based: Combine related papers/docs into comprehensive guides with chapter markers
• Format consolidation: Combine slides + transcript + notes for the same event into a single PDF

See references/merging-strategies.md for full merge patterns, scripts (time-series merger, topic-based PDF merger), decision trees, and quality checks.

IMPORTANT: Preserve chronological timestamps in merged content. Add clear date headers within merged files so temporal context is not lost.

Log all merge decisions for inclusion in the organization plan.

Step 8: Implement Flat Structure

NotebookLM works best with flat source lists, no nested folders.

Before:

docs/
  project/
    planning/
      requirements.pdf
    research/
      background.pdf
  reference/
    api_docs.pdf

After:

notebooklm_sources/
  project_requirements_2026.pdf
  project_background_research.pdf
  reference_api_documentation.pdf

See references/organization-scripts.md for the implementation script. Preserve timestamps when copying: use cp -p to maintain original dates.

Step 9: Find and Remove Duplicates

find . -type f -exec md5 {} \; | sort | uniq -d
find . -type f -printf '%f\n' | sed 's/\.[^.]*$//' | sort | uniq -d
for pdf in *.pdf; do echo "=== $pdf ==="; pdftotext "$pdf" - | md5; done | sort

Decision matrix:

• Same content, different formats: keep PDF (best for NotebookLM)
• Same content, different names: keep most descriptive name
• Slight variations: merge into single document if <500k words
• Truly duplicate: delete older version (check timestamps first)

Step 10: Optimize for RAG

NotebookLM uses RAG, which works best with focused documents:

• Split 100-page documents into 3-5 topic-focused files
• Separate chapters/sections into individual sources
• Keep each source focused on one topic/subtopic
• Prefer 20-50 pages per PDF over 200+ page megadocs

Instead of:
  company_handbook_500_pages.pdf

Create:
  handbook_code_of_conduct.pdf
  handbook_benefits_overview.pdf
  handbook_time_off_policy.pdf
  handbook_remote_work_guidelines.pdf
  handbook_career_development.pdf

Step 11: Propose Organization Plan

Present a plan to the user before making changes. The plan should cover current state, source selection strategy (if >50 sources), proposed structure, changes to make, and a compatibility check.

See references/organization-plan-template.md for the full template with sections for prioritization results, merge decisions, and final source count verification.

Step 12: Execute Organization

After user approval, execute all conversions, merges, renames, and structural changes. Log all operations.

See references/organization-scripts.md for the complete execution script with logging and limit verification. Run touch -r after every file operation to preserve original timestamps.

Step 13: Provide Upload Instructions

Provide the user with a summary of organized sources and upload instructions for NotebookLM (direct upload and Google Drive options).

See references/upload-guide.md for the full upload instructions template including maintenance guidance.

Examples

Example 1: Research Paper Collection

User: "Prepare my PhD research papers folder for NotebookLM"

Process:

1. Finds 35 PDFs, 12 DOCX, 8 PPTX across nested folders
2. Converts 8 PPTX to PDF (preserves timestamps)
3. Identifies 2 papers >500k words, splits into parts
4. Renames: smith_2024.pdf to research_quantum_entanglement_smith_2024.pdf
5. Creates flat structure in phd_research_sources/
6. Result: 48 sources ready for upload

Example 2: Company Knowledge Base

User: "Convert our company wiki exports to NotebookLM format"

Split single 145-page PDF by section into 7 focused sources:

• company_overview_history_mission.pdf (8 pages)
• company_policies_hr_guidelines.pdf (28 pages)
• company_product_documentation.pdf (45 pages)
• (4 more topic-focused files)

Result: 7 focused sources instead of 1 large doc. Better RAG retrieval.

Example 3: Excel Data

User: "I have 10 Excel files with research data"

Convert each sheet to separate CSV. Name descriptively: data_survey_responses_2025.csv. Create overview doc: data_overview_methodology.txt. Preserve timestamps on all conversions.

Result: 10 XLSX to 23 CSV files + 1 overview doc.

Example 4: Conference Materials

User: "Organize my conference materials for a knowledge base"

Input: 12 MP3 recordings, 8 PPTX decks, 15 JPG notes, 5 PDFs. Keep MP3 as-is (NotebookLM transcribes on upload). Convert PPTX to PDF. Keep JPGs (NotebookLM reads handwriting via OCR). Apply naming: conf_session_title_speaker_date.ext. Preserve all timestamps.

Result: 40 sources in flat folder.

Example 5: Large Collection (200+ Sources)

For a complete workflow handling 200+ sources (e.g., reducing 237 sources to 48 with strategic merging), see references/large-collection-workflow.md.

Common Patterns

Academic Research

research_[topic]_[author]_[year].pdf
notes_[course]_[topic]_[date].md
textbook_[subject]_chapter_[n]_[title].pdf

Business Projects

project_[name]_requirements.pdf
project_[name]_timeline.csv
meeting_[project]_[date]_notes.txt
client_[name]_proposal_final.docx

Learning/Courses

course_[name]_lecture_[n]_[topic].pdf
course_[name]_readings_week_[n].pdf
course_[name]_assignment_[n].docx

Personal Knowledge Base

article_[topic]_[author]_[date].pdf
book_notes_[title]_[author].md
tutorial_[skill]_[topic].pdf
reference_[tool]_documentation.pdf

Pro Tips

1. Optimize for Search: Use descriptive names with search keywords. Good: tutorial_python_async_programming_advanced.pdf. Bad: tutorial_5.pdf.
2. Topic-Based Splitting: Split large docs by topic, not arbitrary page count. Good: handbook_benefits.pdf, handbook_policies.pdf. Bad: handbook_part_1.pdf, handbook_part_2.pdf.
3. Date Formatting: Use ISO format (YYYY-MM-DD) for sortability. Good: meeting_notes_2026_02_04.txt. Bad: meeting_notes_feb_4_2026.txt.
4. Preserve Source Timestamps: Always maintain original file creation/modification dates. These enable accurate recency scoring and help NotebookLM's RAG weight recent meeting notes, decisions, and additions appropriately. Use touch -r original converted after every conversion.
5. Extract Text from Scans: Scanned PDFs do not work in NotebookLM. Test with pdftotext test.pdf - | head. If blank, run ocrmypdf input.pdf output.pdf.
6. Use Prefixes for Ordering: Add numeric prefixes for logical ordering: 01_project_overview.pdf, 02_project_requirements.pdf.
7. Test Before Bulk Upload: Upload 2-3 files first to verify processing, summaries, and search accuracy. Then upload the rest.

Best Practices Summary

Source Selection and Optimization:

• Always assess total source count first before organizing
• Use scoring rubric for objective prioritization (>50 sources)
• Merge strategically as primary optimization, not last resort
• Prefer quality over quantity: 48 great sources over 50 mediocre ones
• Reserve 2-3 slots for future additions
• Do not merge high-value unique sources (score 35+)
• Do not combine unrelated topics just to hit limits

File Naming:

• Descriptive snake_case with searchable terms and ISO dates
• Keep under 100 characters, no spaces or special characters
• Use dates instead of version numbers

Format Selection:

• PDF for presentations and mixed content
• CSV for spreadsheet data
• DOCX/TXT/MD for text documents
• Always convert PPTX and XLSX before upload

Timestamp Preservation:

• Run touch -r original converted after every conversion
• Use cp -p when copying files to preserve modification dates
• Include ISO dates in filenames for explicit temporal context
• Timestamps drive recency scoring and RAG relevance weighting

Organization Structure:

• Flat structure (one folder, all files)
• Descriptive names include folder context
• Stay under 50 sources per notebook

Implementation Checklist

Phase 1: Assessment and Prioritization

• Identify target notebook topic/purpose
• Locate all source files and count total
• If >50: run scoring rubric for all sources
• If >50: identify and execute strategic merges
• If >50: select top sources using decision matrix (target 48)
• Check file formats, note conversions needed
• Estimate word counts for large files

Phase 2: Conversion and Organization

• Convert unsupported formats (preserve timestamps)
• Apply descriptive snake_case naming
• Split large documents by topic
• Remove duplicates
• Create flat output directory
• Verify all files <200MB and <500k words
• Verify final source count is at or below 50
• Verify timestamps preserved on all converted/moved files

Phase 3: Upload and Verification

• Document selection strategy in organization plan
• Test upload 2-3 files
• Upload remaining sources
• Verify NotebookLM processing and summaries
• Test search functionality
• Confirm all key topics covered despite any source reduction