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data-normalizer

Extract text and metadata from archaeological documents into JSONL and CSV indexes.

Updated Nov 12, 2025
Search Tags:#document processing#data extraction#metadata#normalization#PDF#archaeology#HWP
One-click install
npx skills add https://github.com/younga1234/20251112-3 --skill data-normalizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-normalizer
Source: https://github.com/younga1234/20251112-3/tree/main/.claude/skills/data-normalizer
Command: npx skills add https://github.com/younga1234/20251112-3 --skill data-normalizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyPDF2, pdfplumber, olefile.

What problem does it solve?

Archaeological research often involves manually extracting data from numerous documents (PDF, HWP, DOCX, TXT), a process that is both time-consuming and prone to errors. This Skill automates the collection and standardization of this critical information.

Core Features & Use Cases

  • Automated Data Extraction: Scans diverse document types (PDF, HWP, DOCX, TXT) to extract raw text and key metadata.
  • Metadata Normalization: Standardizes extracted information such as title, author, publication year, location, coordinates, historical period, and artifact/feature types.
  • Use Case: Process hundreds of archaeological reports and academic papers from your local folders, automatically creating a unified, structured dataset (JSONL, CSV) ready for in-depth analysis, saving days of manual data entry.

Quick Start

Use the data-normalizer skill to collect and normalize all documents in the '논문/', '발굴조사보고서/', and '주변유적/' folders.

Frequently Asked Questions about data-normalizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract text and metadata from PDF and HWP archaeology documents automatically?▼

Data extraction from archaeology documents processes PDF, HWP, DOCX, and TXT files to pull text, title, author, year, location, coordinates, period, and artifact types into structured format. The Skill scans folders recursively and outputs normalized data as JSONL and CSV files ready for analysis.

Can I normalize metadata across multiple document types and folders at once?▼

Metadata normalization standardizes information across diverse formats by scanning multiple folders (논문/, 발굴조사보고서/, 주변유적/) in one process. It extracts and aligns title, author, publication year, location, and historical period into unified CSV and JSONL outputs.

What file formats does automated document processing support for archaeological reports?▼

Document processing supports PDF, HWP, DOCX, DOC, and TXT files. For scanned PDFs, optional OCR extracts text from images, and encoding safeguards handle non-ASCII characters common in archaeological metadata.

How does folder traversal and batch processing save time on archaeological data collection?▼

Batch processing recursively scans specified folders, extracts metadata and content from hundreds of documents automatically, and produces structured outputs (JSONL with full text, CSV with metadata) plus extraction logs—eliminating manual data entry.

What structured output does the Skill produce for archaeological document indexing?▼

The Skill outputs normalized documents as JSONL with id, path, title, author, year, location, coordinates, period, types, abstract, and page-level text; metadata as CSV; and an extraction log capturing processing success and errors across all files.

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