document-inventory

name	document-inventory
description	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.
type	reference
version	1.1.0
last_updated	"2026-01-02T00:00:00.000Z"
category	data
related_skills	["knowledge-base-builder","semantic-search-setup"]
capabilities	[]
requires	[]
tags	[]

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

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