XLSX Smart Extractor

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name: xlsx-smart-extractor description: Extract and analyze Excel workbooks (1MB-50MB+) with minimal token usage through local extraction, sheet-based chunking, and persistent caching. Preserves formulas, cell formatting, and complex table structures for compliance matrices, financial models, and large datasets. license: MIT compatibility: claude-desktop metadata: version: 2.2.0 author: Diego Consolini category: productivity runtime: python3 dependencies: requirements.txt

Excel Workbook Analyzer

When to Use This Skill

Use this skill when:

• User provides an Excel file path (.xlsx, .xlsm) with size >500KB
• User encounters "Excel too large" or token limit errors
• User needs to analyze compliance matrices, financial models, or data tables
• User wants to extract formulas, cell formatting, or worksheet structure
• User needs to query specific sheets, columns, or value ranges
• User is working with multi-sheet workbooks (5+ sheets)

Capabilities

1. Local Excel Extraction (Zero LLM Involvement)

• Extract all worksheets using openpyxl
• Preserve cell formulas (not just values)
• Capture cell formatting (colors, borders, number formats)
• Extract merged cell information
• Preserve hyperlinks and comments
• Extract data validation rules
• Cache extraction for instant reuse

2. Sheet-Based Semantic Chunking

• Intelligent chunking by:
  • Individual worksheets (1 sheet = 1 chunk if small)
  • Column groups for wide tables (A-E, F-J, etc.)
  • Row ranges for long tables (rows 1-100, 101-200, etc.)
  • Named ranges and tables
  • Pivot table structures
• Preserve table headers across chunks
• Maintain cell references and formulas
• 99%+ content preservation rate

3. Efficient Querying

• Search by:
  • Sheet name
  • Column headers
  • Cell value patterns
  • Formula patterns
  • Named ranges
• Filter by:
  • Data type (numbers, text, dates, formulas)
  • Cell formatting (colors, borders, fonts)
  • Value ranges (>1000, <0, etc.)
• 20-100x token reduction vs full workbook
• Results include cell references (Sheet!A1, Sheet!B10:E20)

4. Structure Analysis

• Detect workbook structure:
  • Number of sheets and row/column counts
  • Named ranges and defined names
  • Pivot tables and charts
  • Data connections and external links
  • Protected sheets and workbook protection
• Generate workbook summary:
  • Sheet names and purposes (inferred)
  • Key tables and data ranges
  • Formula complexity metrics
  • Data validation rules
• Identify compliance matrix patterns:
  • Control IDs and descriptions
  • Evidence columns
  • Status/completion tracking

5. Formula and Calculation Preservation

• Extract formulas as text (e.g., "=SUM(A1:A10)")
• Preserve formula references across sheets
• Detect circular references
• Extract array formulas
• Preserve calculated column definitions

6. Persistent Caching (v2.0.0 Unified System)

⚠️ IMPORTANT: Cache Location

Extracted content is stored in a user cache directory, NOT the working directory:

Cache locations by platform:

• Linux/Mac: ~/.claude-cache/xlsx/{workbook_name}_{hash}/
• Windows: C:\Users\{username}\.claude-cache\xlsx\{workbook_name}_{hash}\

Why cache directory?

1. Persistent caching: Extract once, query forever - even across different projects
2. Cross-project reuse: Same workbook analyzed from different projects uses the same cache
3. Performance: Subsequent queries are instant (no re-extraction needed)
4. Token optimization: 20-100x reduction by loading only relevant sheets/columns

Cache contents:

• full_workbook.json - Complete workbook data
• sheet_{name}.json - Individual sheet files
• named_ranges.json - Named ranges and tables
• metadata.json - Workbook metadata
• manifest.json - Cache manifest

Accessing cached content:

# List all cached workbooks
python scripts/query_xlsx.py list

# Query cached content
python scripts/query_xlsx.py search {cache_key} "search query"

# Find cache location (shown in extraction output)
# Example: ~/.claude-cache/xlsx/ComplianceMatrix_a1b2c3d4/

To extract files to working directory:

# Option 1: Use --output-dir flag during extraction
python3 scripts/extract_xlsx.py workbook.xlsx --output-dir ./extracted

# Option 2: Copy from cache manually
cp -r ~/.claude-cache/xlsx/{cache_key}/* ./extracted_content/

Note: Cache is local and not meant for version control. Keep original Excel files in the repository and extract locally on each development machine (one-time operation).

Workflow

Step 1: Extract Excel Workbook

# Extract to cache (default)
python3 scripts/extract_xlsx.py /path/to/workbook.xlsx

# Extract and copy to working directory (interactive prompt)
python3 scripts/extract_xlsx.py /path/to/workbook.xlsx
# Will prompt: "Copy files? (y/n)"
# Will ask: "Keep cache? (y/n)"

# Extract and copy to specific directory (no prompts)
python3 scripts/extract_xlsx.py /path/to/workbook.xlsx --output-dir ./extracted

What happens:

1. Open workbook with openpyxl
2. Extract metadata (author, created date, modified date, sheet count)
3. Iterate through all worksheets:
   • Extract cell values, formulas, formatting
   • Extract merged cells and data validation
   • Extract comments and hyperlinks
4. Extract named ranges and defined names
5. Close workbook and save to cache (~/.claude-cache/xlsx/)
6. Return cache key (e.g., ComplianceMatrix_a8f9e2c1)

Output files:

• full_workbook.json - All sheets with full data
• sheets/*.json - Individual sheet data files
• formulas.json - All formulas extracted
• metadata.json - Workbook metadata
• named_ranges.json - Named ranges and tables
• manifest.json - Extraction summary

Performance:

• 1MB workbook: ~5 seconds
• 10MB workbook: ~30 seconds
• 50MB workbook: ~2 minutes
• Cache reuse: <1 second

Step 2: Chunk Workbook Content

python3 scripts/chunk_sheets.py <cache_key>

What happens:

1. Load extracted workbook data
2. Analyze each sheet:
   • Detect table structures (headers, data rows)
   • Identify optimal chunk boundaries
   • Preserve headers across chunks
3. Create chunks based on:
   • Sheet size (small sheets = single chunk)
   • Column groups (wide tables split by column ranges)
   • Row ranges (long tables split by row ranges)
   • Named ranges (preserve as single chunks)
4. Calculate tokens per chunk (estimation)
5. Save chunk index and individual chunk files

Output files:

• chunks/index.json - Chunk metadata and locations
• chunks/chunk_001.json - Individual chunk data
• chunks/chunk_002.json - ...

Statistics:

• Content preservation: >99%
• Avg tokens per chunk: 500-2000
• Token reduction: 20-100x

Step 3: Query Excel Content

# Search by keyword
python3 scripts/query_xlsx.py search <cache_key> "password policy"

# Get specific sheet
python3 scripts/query_xlsx.py sheet <cache_key> "Controls"

# Get cell range
python3 scripts/query_xlsx.py range <cache_key> "Sheet1!A1:E10"

# Get workbook summary
python3 scripts/query_xlsx.py summary <cache_key>

What happens:

1. Load chunk index
2. Filter chunks based on query:
   • Keyword search: scan all chunks for text matches
   • Sheet query: return only chunks from that sheet
   • Range query: extract specific cell range
   • Summary: return workbook metadata and structure
3. Return matching chunks with:
   • Sheet name and cell references
   • Cell values and formulas
   • Token count
   • Match relevance score

Results format:

Found 3 result(s) for query: "password policy"

1. Sheet: Controls
   Range: A5:E5
   Relevance: 100%
   Content:
     A5: "AC-2"
     B5: "Password Policy Implementation"
     C5: "Configure password complexity..."
     D5: "Evidence.docx"
     E5: "Complete"
   Tokens: 85

2. Sheet: Evidence
   Range: B12:C12
   Relevance: 95%
   Content:
     B12: "Password policy documented"
     C12: "2025-10-15"
   Tokens: 32

Total tokens: 117 (vs 45,892 full workbook = 392x reduction)

Use Cases

1. Compliance Matrix Analysis

Scenario: ISO 27001 compliance tracking spreadsheet (5MB, 12 sheets, 500 controls)

Workflow:

1. Extract workbook: python3 scripts/extract_xlsx.py iso27001_controls.xlsx
2. Query specific control: python3 scripts/query_xlsx.py search iso27001_controls_a8f9e2 "A.9.2.1"
3. Get evidence status: python3 scripts/query_xlsx.py sheet iso27001_controls_a8f9e2 "Evidence"

Benefits:

• Find specific controls in seconds (vs scanning full 5MB file)
• Extract only relevant sections (20x token reduction)
• Preserve cell references for traceability

2. Financial Model Analysis

Scenario: Revenue projection model (15MB, 8 sheets, complex formulas)

Workflow:

1. Extract workbook: python3 scripts/extract_xlsx.py revenue_model.xlsx
2. Get summary: python3 scripts/query_xlsx.py summary revenue_model_f3a8c1
3. Analyze formulas: python3 scripts/query_xlsx.py search revenue_model_f3a8c1 "formula:SUM"
4. Get specific calculation: python3 scripts/query_xlsx.py range revenue_model_f3a8c1 "Projections!A1:Z50"

Benefits:

• Understand model structure without loading full file
• Extract formulas for review (not just values)
• Analyze specific scenarios (Sheet1 vs Sheet2)

3. Security Audit Log Analysis

Scenario: Security event export (20MB, 50,000 rows, 30 columns)

Workflow:

1. Extract workbook: python3 scripts/extract_xlsx.py security_logs.xlsx
2. Get data validation rules: python3 scripts/query_xlsx.py summary security_logs_b9d2e1
3. Query failed logins: python3 scripts/query_xlsx.py search security_logs_b9d2e1 "failed"
4. Get specific date range: python3 scripts/query_xlsx.py range security_logs_b9d2e1 "Logs!A1:F1000"

Benefits:

• Query massive logs without hitting token limits
• Filter by keywords (100x token reduction)
• Extract specific time ranges

Technical Details

Supported Formats

• .xlsx (Excel 2007+ XML format)
• .xlsm (Excel macro-enabled workbook)
• .xltx (Excel template)
• .xltm (Excel macro-enabled template)

Not supported:

• .xls (Excel 97-2003 binary format - use xlrd separately)
• .xlsb (Excel binary workbook - use pyxlsb separately)
• .ods (OpenDocument spreadsheet - use odfpy separately)

Data Extraction Details

Cell Values:

• Text, numbers, dates, booleans
• Error values (#DIV/0!, #N/A, etc.)
• Blank cells (preserved for structure)

Cell Formatting:

• Font (name, size, color, bold, italic)
• Fill (background color, pattern)
• Border (style, color)
• Number format (currency, percentage, date, custom)
• Alignment (horizontal, vertical, text wrap)

Formulas:

• Regular formulas (=SUM(A1:A10))
• Array formulas ({=SUM(A1:A10*B1:B10)})
• Shared formulas (Excel optimization)
• External references (to other workbooks)

Workbook Structure:

• Sheet names and visibility (hidden, very hidden)
• Sheet order and colors
• Named ranges (workbook and sheet scope)
• Defined names (formulas, constants)
• Data validation rules
• Conditional formatting rules (basic detection)
• Protection status (workbook and sheet)

Chunking Strategy

Small sheets (< 1000 cells):

• Single chunk per sheet
• Preserves entire sheet structure

Wide tables (> 20 columns):

• Split by column groups (A-J, K-T, U-Z)
• Repeat row headers in each chunk
• Preserve row numbers

Long tables (> 500 rows):

• Split by row ranges (1-250, 251-500, etc.)
• Repeat column headers in each chunk
• Preserve column letters

Named ranges:

• Always preserve as single chunks
• Even if range spans multiple natural chunks

Token Estimation

Tokens estimated using character count / 4 (approximation):

• Text cell: ~1 token per word
• Number cell: ~1 token
• Formula: ~2-5 tokens depending on complexity
• Cell formatting: ~1-2 tokens per formatted attribute

Actual token usage may vary with model (Claude uses different tokenizer than GPT).

Error Handling

Common Errors

1. File Not Found

Error: Excel file not found: /path/to/file.xlsx

Solution: Verify file path and permissions.

2. Corrupted Workbook

Error: Failed to open workbook: zipfile.BadZipFile

Solution: File may be corrupted. Try opening in Excel and re-saving.

3. Password Protected

Error: Workbook is password protected

Solution: openpyxl cannot open password-protected files. Remove protection first.

4. External Data Connections

Warning: Workbook contains external data connections (ignored)

Solution: External connections are not extracted. Only static data is preserved.

5. Unsupported Features

Warning: Pivot tables detected but not fully extracted
Warning: Charts detected but not extracted
Warning: VBA macros detected but not extracted

Solution: These features are noted in metadata but not extracted in detail.

Performance Considerations

Memory Usage

• 1MB workbook: ~5MB memory (5x expansion for JSON)
• 10MB workbook: ~50MB memory
• 50MB workbook: ~250MB memory

Large workbook handling:

• Process sheets sequentially (not all at once)
• Use generator patterns for row iteration
• Clear cell objects after processing

Disk Usage

• Cache size: ~3-5x original file size
• Example: 10MB Excel → 30-50MB cache
• Cache location: ~/.claude-cache/xlsx/
• Auto-cleanup: LRU eviction after 30 days

Optimization Tips

1. Extract once, query many times - cache is persistent
2. Use specific queries - sheet/range queries faster than full-text search
3. Chunk first - always chunk after extraction for optimal performance
4. Use --force flag - only when file has changed

Limitations

1. VBA Macros: Not extracted or executed (security risk)
2. Pivot Tables: Structure detected but not fully extracted
3. Charts: Not extracted (consider screenshot + description)
4. External Links: Noted but not followed
5. Real-time Data: Not refreshed (only static snapshot)
6. Password Protection: Cannot open protected files
7. Binary Formats: .xls and .xlsb not supported (use conversion)

Comparison to Alternatives

vs. Loading Full Excel in LLM Context

• Token usage: 20-100x reduction
• Speed: 10-50x faster (no token processing)
• Cost: 20-100x cheaper (fewer tokens)
• Limits: No file size limits (vs 1-2MB context limits)

vs. pandas.read_excel()

• Formulas: Preserved (pandas only gets values)
• Formatting: Preserved (pandas ignores)
• Multiple sheets: Better handling (pandas requires iteration)
• Structure: Preserves full workbook structure (pandas flattens to DataFrame)

vs. Manual extraction

• Speed: Automated (vs manual copy-paste)
• Accuracy: 99%+ preservation (vs human error)
• Repeatability: Cached (vs re-doing work)
• Scalability: Handles 50MB files (vs manual limit ~5MB)

Installation

Prerequisites

# Python 3.8+
python3 --version

# Install dependencies
pip3 install -r requirements.txt

Verify Installation

# Test openpyxl
python3 -c "import openpyxl; print('openpyxl available')"

# Test pandas
python3 -c "import pandas; print('pandas available')"

Troubleshooting

Issue: "Module not found: openpyxl"

Solution:

pip3 install openpyxl

Issue: "Permission denied" when creating cache

Solution:

chmod 755 ~/.claude-cache/xlsx/

Issue: Extraction very slow (>5 minutes for 10MB file)

Possible causes:

• Many formulas (evaluation takes time)
• External data connections (trying to refresh)
• Corrupted file (openpyxl struggling to parse)

Solution: Use --force flag and check for warnings.

Issue: High memory usage

Solution: Process sheets one at a time instead of loading entire workbook.

References

• openpyxl Documentation: https://openpyxl.readthedocs.io/
• pandas Documentation: https://pandas.pydata.org/docs/
• Excel file format (OOXML): https://docs.microsoft.com/en-us/openspecs/office_standards/

Notes

• Extraction is 100% local (no LLM calls, no API requests)
• Cache is persistent across sessions
• Formulas are preserved as text (not evaluated)
• External references noted but not followed
• Security: No macro execution, read-only access