# Document Inventory > Scan and catalog document collections with metadata extraction, categorization, and statistics. Use for auditing document libraries, preparing for knowledge base creation, or understanding large file collections. - Skill: `vamseeachanta/document-inventory` (Agent Skill) - Install (CLI): `npx skillmds@latest add vamseeachanta/document-inventory` - Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/document-inventory/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: vamseeachanta (https://skillmd.com/u/vamseeachanta) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/vamseeachanta/document-inventory --- # Document Inventory ## 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 ```python 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 ```python #!/usr/bin/env python3 """Document inventory builder.""" import sqlite3 import os from pathlib import Path from datetime import datetime import logging *See sub-skills for full details.* ### CLI Interface ```python #!/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') *See sub-skills for full details.* ### Report Generator ```python def generate_report(db_path, output_path): """Generate HTML inventory report.""" inventory = DocumentInventory(db_path) stats = inventory.get_statistics() html = f""" *See sub-skills for full details.* ## Custom Categorization ### Extend with Your Patterns ```python # Add custom patterns for your domain CUSTOM_PATTERNS = { 'SPEC': 'Specifications', 'DWG': 'Drawings', 'REV': 'Revisions', 'APPROVED': 'Approved', 'DRAFT': 'Draft', 'SUPERSEDED': 'Superseded', } *See sub-skills for full details.* ### Multi-Level Categories ```python 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: *See sub-skills for full details.* ## Example Usage ```bash # 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 ## 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 ## Sub-Skills - [Best Practices](best-practices/SKILL.md) ## Sub-Skills - [Execution Checklist](execution-checklist/SKILL.md) - [Error Handling](error-handling/SKILL.md) - [Metrics](metrics/SKILL.md) - [Dependencies](dependencies/SKILL.md)