Document Inventory Skill

Overview

This skill scans document collections (PDFs, Word docs, text files) and creates a structured inventory with metadata, automatic categorization, and collection statistics. Essential first step before building knowledge bases.

Quick Start

from pathlib import Path
import sqlite3

# Scan directory
documents = []
for filepath in Path("/path/to/docs").rglob("*.pdf"):
    documents.append({
        'filename': filepath.name,
        'size': filepath.stat().st_size,
        'path': str(filepath)
    })

# Store in database
conn = sqlite3.connect("inventory.db")
cursor = conn.cursor()
cursor.execute("CREATE TABLE IF NOT EXISTS docs (name TEXT, size INTEGER, path TEXT)")
for doc in documents:
    cursor.execute("INSERT INTO docs VALUES (?, ?, ?)",
                   (doc['filename'], doc['size'], doc['path']))
conn.commit()
print(f"Inventoried {len(documents)} documents")

When to Use

• Auditing large document libraries before processing
• Understanding the scope of a document collection
• Categorizing documents by type, source, or content
• Preparing inventories for knowledge base creation
• Generating reports on document collections
• Identifying duplicates or organizing files

Features

• Recursive scanning - Process nested directories
• Metadata extraction - Size, dates, page counts
• Auto-categorization - Pattern-based classification
• Statistics generation - Collection summaries
• SQLite storage - Queryable inventory database
• Multiple formats - PDF, DOCX, TXT, and more

Implementation

Core Inventory Builder

#!/usr/bin/env python3
"""Document inventory builder."""

import sqlite3
import os
from pathlib import Path
from datetime import datetime
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class DocumentInventory:
    """Build and manage document inventories."""

    SUPPORTED_EXTENSIONS = {
        '.pdf': 'PDF',
        '.docx': 'Word',
        '.doc': 'Word',
        '.txt': 'Text',
        '.md': 'Markdown',
        '.xlsx': 'Excel',
        '.xls': 'Excel',
        '.pptx': 'PowerPoint',
        '.ppt': 'PowerPoint',
    }

    def __init__(self, db_path):
        self.db_path = db_path
        self.conn = sqlite3.connect(db_path, timeout=30)
        self._setup_tables()

    def _setup_tables(self):
        cursor = self.conn.cursor()

        cursor.execute('''
            CREATE TABLE IF NOT EXISTS documents (
                id INTEGER PRIMARY KEY,
                filename TEXT NOT NULL,
                filepath TEXT UNIQUE NOT NULL,
                extension TEXT,
                file_type TEXT,
                category TEXT,
                file_size INTEGER,
                created_date TEXT,
                modified_date TEXT,
                parent_dir TEXT,
                depth INTEGER,
                scanned_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')

        cursor.execute('''
            CREATE INDEX IF NOT EXISTS idx_category ON documents(category)
        ''')

        cursor.execute('''
            CREATE INDEX IF NOT EXISTS idx_extension ON documents(extension)
        ''')

        self.conn.commit()

    def scan_directory(self, root_path):
        """Scan directory and build inventory."""
        root = Path(root_path).resolve()
        logger.info(f"Scanning: {root}")

        count = 0
        for filepath in root.rglob('*'):
            if filepath.is_file():
                ext = filepath.suffix.lower()
                if ext in self.SUPPORTED_EXTENSIONS:
                    self._add_document(filepath, root)
                    count += 1

                    if count % 500 == 0:
                        logger.info(f"Scanned {count} documents...")
                        self.conn.commit()

        self.conn.commit()
        logger.info(f"Scan complete: {count} documents found")
        return count

    def _add_document(self, filepath, root):
        """Add document to inventory."""
        cursor = self.conn.cursor()

        try:
            stat = filepath.stat()
            ext = filepath.suffix.lower()

            cursor.execute('''
                INSERT OR REPLACE INTO documents
                (filename, filepath, extension, file_type, category,
                 file_size, created_date, modified_date, parent_dir, depth)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
            ''', (
                filepath.name,
                str(filepath),
                ext,
                self.SUPPORTED_EXTENSIONS.get(ext, 'Unknown'),
                self._categorize(filepath),
                stat.st_size,
                datetime.fromtimestamp(stat.st_ctime).isoformat(),
                datetime.fromtimestamp(stat.st_mtime).isoformat(),
                str(filepath.parent),
                len(filepath.relative_to(root).parts) - 1
            ))

        except Exception as e:
            logger.warning(f"Error adding {filepath}: {e}")

    def _categorize(self, filepath):
        """Auto-categorize document based on patterns."""
        name = filepath.name.upper()
        path_str = str(filepath).upper()

        # Industry standard patterns
        patterns = {
            'API': 'API',
            'ISO': 'ISO',
            'ASME': 'ASME',
            'DNV': 'DNV',
            'NORSOK': 'NORSOK',
            'BSI': 'BSI',
            'ASTM': 'ASTM',
            'AWS': 'AWS',
            'ABS': 'ABS',
            'AISC': 'AISC',
            'IEEE': 'IEEE',
        }

        for pattern, category in patterns.items():
            if pattern in name or pattern in path_str:
                return category

        # Path-based categorization
        path_categories = {
            'STANDARD': 'Standards',
            'SPEC': 'Specifications',
            'MANUAL': 'Manuals',
            'GUIDE': 'Guides',
            'REPORT': 'Reports',
            'DRAWING': 'Drawings',
            'PROCEDURE': 'Procedures',
        }

        for pattern, category in path_categories.items():
            if pattern in path_str:
                return category

        return 'Unknown'

    def get_statistics(self):
        """Get inventory statistics."""
        cursor = self.conn.cursor()

        stats = {}

        # Total count
        cursor.execute('SELECT COUNT(*) FROM documents')
        stats['total_documents'] = cursor.fetchone()[0]

        # Total size
        cursor.execute('SELECT SUM(file_size) FROM documents')
        total_bytes = cursor.fetchone()[0] or 0
        stats['total_size_mb'] = round(total_bytes / (1024 * 1024), 2)

        # By file type
        cursor.execute('''
            SELECT file_type, COUNT(*), SUM(file_size)
            FROM documents
            GROUP BY file_type
            ORDER BY COUNT(*) DESC
        ''')
        stats['by_type'] = {
            row[0]: {'count': row[1], 'size_mb': round((row[2] or 0) / 1024 / 1024, 2)}
            for row in cursor.fetchall()
        }

        # By category
        cursor.execute('''
            SELECT category, COUNT(*)
            FROM documents
            GROUP BY category
            ORDER BY COUNT(*) DESC
        ''')
        stats['by_category'] = dict(cursor.fetchall())

        # By extension
        cursor.execute('''
            SELECT extension, COUNT(*)
            FROM documents
            GROUP BY extension
            ORDER BY COUNT(*) DESC
        ''')
        stats['by_extension'] = dict(cursor.fetchall())

        return stats

    def search(self, query, category=None, file_type=None, limit=50):
        """Search inventory."""
        cursor = self.conn.cursor()

        sql = 'SELECT filename, filepath, category, file_size FROM documents WHERE 1=1'
        params = []

        if query:
            sql += ' AND filename LIKE ?'
            params.append(f'%{query}%')

        if category:
            sql += ' AND category = ?'
            params.append(category)

        if file_type:
            sql += ' AND file_type = ?'
            params.append(file_type)

        sql += ' ORDER BY filename LIMIT ?'
        params.append(limit)

        cursor.execute(sql, params)
        return cursor.fetchall()

    def export_csv(self, output_path):
        """Export inventory to CSV."""
        import csv

        cursor = self.conn.cursor()
        cursor.execute('SELECT * FROM documents')

        columns = [desc[0] for desc in cursor.description]

        with open(output_path, 'w', newline='', encoding='utf-8') as f:
            writer = csv.writer(f)
            writer.writerow(columns)
            writer.writerows(cursor.fetchall())

        logger.info(f"Exported to {output_path}")

CLI Interface

#!/usr/bin/env python3
"""Document Inventory CLI."""

import argparse
import json

def main():
    parser = argparse.ArgumentParser(description='Document Inventory Tool')
    subparsers = parser.add_subparsers(dest='command', help='Commands')

    # Scan command
    scan_parser = subparsers.add_parser('scan', help='Scan directory')
    scan_parser.add_argument('path', help='Directory to scan')
    scan_parser.add_argument('--db', default='inventory.db', help='Database path')

    # Stats command
    stats_parser = subparsers.add_parser('stats', help='Show statistics')
    stats_parser.add_argument('--db', default='inventory.db', help='Database path')
    stats_parser.add_argument('--json', action='store_true', help='Output as JSON')

    # Search command
    search_parser = subparsers.add_parser('search', help='Search inventory')
    search_parser.add_argument('query', help='Search query')
    search_parser.add_argument('--db', default='inventory.db', help='Database path')
    search_parser.add_argument('--category', help='Filter by category')
    search_parser.add_argument('--type', help='Filter by file type')

    # Export command
    export_parser = subparsers.add_parser('export', help='Export to CSV')
    export_parser.add_argument('output', help='Output CSV path')
    export_parser.add_argument('--db', default='inventory.db', help='Database path')

    args = parser.parse_args()

    if args.command == 'scan':
        inventory = DocumentInventory(args.db)
        count = inventory.scan_directory(args.path)
        print(f"\nScanned {count} documents")

        stats = inventory.get_statistics()
        print(f"Total size: {stats['total_size_mb']} MB")
        print(f"\nBy category:")
        for cat, count in list(stats['by_category'].items())[:10]:
            print(f"  {cat}: {count}")

    elif args.command == 'stats':
        inventory = DocumentInventory(args.db)
        stats = inventory.get_statistics()

        if args.json:
            print(json.dumps(stats, indent=2))
        else:
            print(f"Total Documents: {stats['total_documents']}")
            print(f"Total Size: {stats['total_size_mb']} MB")
            print(f"\nBy Type:")
            for t, data in stats['by_type'].items():
                print(f"  {t}: {data['count']} ({data['size_mb']} MB)")
            print(f"\nBy Category:")
            for cat, count in list(stats['by_category'].items())[:15]:
                print(f"  {cat}: {count}")

    elif args.command == 'search':
        inventory = DocumentInventory(args.db)
        results = inventory.search(
            args.query,
            category=args.category,
            file_type=args.type
        )

        print(f"Found {len(results)} results:\n")
        for filename, filepath, category, size in results:
            size_kb = size / 1024
            print(f"  [{category:10}] {filename} ({size_kb:.1f} KB)")

    elif args.command == 'export':
        inventory = DocumentInventory(args.db)
        inventory.export_csv(args.output)

    else:
        parser.print_help()


if __name__ == '__main__':
    main()

Report Generator

def generate_report(db_path, output_path):
    """Generate HTML inventory report."""
    inventory = DocumentInventory(db_path)
    stats = inventory.get_statistics()

    html = f"""
    <!DOCTYPE html>
    <html>
    <head>
        <title>Document Inventory Report</title>
        <style>
            body {{ font-family: Arial, sans-serif; margin: 40px; }}
            h1 {{ color: #333; }}
            .stat-box {{ background: #f5f5f5; padding: 20px; margin: 10px 0; border-radius: 8px; }}
            .stat-value {{ font-size: 2em; font-weight: bold; color: #2196F3; }}
            table {{ border-collapse: collapse; width: 100%; margin: 20px 0; }}
            th, td {{ border: 1px solid #ddd; padding: 12px; text-align: left; }}
            th {{ background: #2196F3; color: white; }}
            tr:nth-child(even) {{ background: #f9f9f9; }}
        </style>
    </head>
    <body>
        <h1>Document Inventory Report</h1>
        <p>Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>

        <div class="stat-box">
            <div class="stat-value">{stats['total_documents']:,}</div>
            <div>Total Documents</div>
        </div>

        <div class="stat-box">
            <div class="stat-value">{stats['total_size_mb']:,.1f} MB</div>
            <div>Total Size</div>
        </div>

        <h2>By File Type</h2>
        <table>
            <tr><th>Type</th><th>Count</th><th>Size (MB)</th></tr>
            {''.join(f"<tr><td>{t}</td><td>{d['count']}</td><td>{d['size_mb']}</td></tr>"
                     for t, d in stats['by_type'].items())}
        </table>

        <h2>By Category</h2>
        <table>
            <tr><th>Category</th><th>Count</th></tr>
            {''.join(f"<tr><td>{c}</td><td>{n}</td></tr>"
                     for c, n in stats['by_category'].items())}
        </table>
    </body>
    </html>
    """

    with open(output_path, 'w') as f:
        f.write(html)

    print(f"Report generated: {output_path}")

Custom Categorization

Extend with Your Patterns

# Add custom patterns for your domain
CUSTOM_PATTERNS = {
    'SPEC': 'Specifications',
    'DWG': 'Drawings',
    'REV': 'Revisions',
    'APPROVED': 'Approved',
    'DRAFT': 'Draft',
    'SUPERSEDED': 'Superseded',
}

def categorize_custom(filepath):
    name = filepath.name.upper()
    for pattern, category in CUSTOM_PATTERNS.items():
        if pattern in name:
            return category
    return 'Uncategorized'

Multi-Level Categories

def categorize_hierarchical(filepath):
    """Create hierarchical categories."""
    name = filepath.name.upper()

    # Primary category
    primary = 'General'
    if 'API' in name:
        primary = 'API Standards'
    elif 'ISO' in name:
        primary = 'ISO Standards'

    # Secondary category
    secondary = 'Other'
    if 'DESIGN' in name:
        secondary = 'Design'
    elif 'SAFETY' in name:
        secondary = 'Safety'
    elif 'QUALITY' in name:
        secondary = 'Quality'

    return f"{primary}/{secondary}"

Execution Checklist

• Identify target directory for scanning
• Create SQLite database for inventory
• Run initial scan and review results
• Customize categorization patterns if needed
• Generate statistics report
• Export to CSV for review
• Generate HTML report for stakeholders
• Plan next steps (knowledge base creation)

Error Handling

Common Errors

Error: PermissionError

• Cause: Insufficient permissions to read files
• Solution: Run with appropriate permissions or skip protected files

Error: sqlite3.OperationalError (database is locked)

• Cause: Concurrent access without timeout
• Solution: Use

  timeout=30

  when connecting

Error: UnicodeDecodeError in filenames

• Cause: Non-UTF8 characters in file paths
• Solution: Use

  errors='replace'

  when processing paths

Error: OSError (too many open files)

• Cause: Not closing file handles properly
• Solution: Use context managers and batch commits

Error: Slow scanning on network drives

• Cause: Network latency for each file access
• Solution: Copy to local drive or use async scanning

Metrics

Metric	Typical Value
Scan speed (local)	~1000 files/second
Scan speed (network)	~100 files/second
Database size	~1KB per 10 documents
Memory usage	~50MB for 100K documents
Report generation	<1 second

Best Practices

1. Scan before processing - Always inventory first
2. Use SQLite timeout -

   timeout=30

   for concurrent access
3. Batch commits - Commit every 500 files
4. Handle errors gracefully - Log and continue on failures
5. Export for review - Generate CSV/HTML for stakeholders
6. Update incrementally - Use

   INSERT OR REPLACE

Example Usage

# Scan directory
python inventory.py scan /path/to/documents --db inventory.db

# View statistics
python inventory.py stats --db inventory.db

# Search
python inventory.py search "API" --category "Standards"

# Export to CSV
python inventory.py export inventory.csv --db inventory.db

Related Skills

• knowledge-base-builder

  - Build searchable database after inventory

• pdf-text-extractor

  - Extract text from inventoried PDFs

• semantic-search-setup

  - Add AI search capabilities

Dependencies

# No external dependencies - uses Python standard library
# Optional: pandas for advanced data manipulation
pip install pandas

---

Version History

• 1.1.0 (2026-01-02): Added Quick Start, Execution Checklist, Error Handling, Metrics sections; updated frontmatter with version, category, related_skills
• 1.0.0 (2024-10-15): Initial release with SQLite storage, auto-categorization, CLI interface