Is my data sent to an external server for processing?

No. The extraction and chunking are performed entirely on your local machine. Only the specific text chunks needed to answer your query are sent to the LLM.

How does semantic chunking improve accuracy?

Unlike naive character limits, semantic chunking identifies chapters and sections to ensure that related information stays together, providing Claude with better context for analysis.

Why should I use PDF Smart Extractor instead of standard PDF uploads?

Standard uploads often hit token limits or context window constraints. This skill processes the PDF locally and only sends relevant sections to Claude, saving up to 90% in token costs.

Which file sizes trigger the PDF Smart Extractor automatically?

The skill is designed to automatically suggest its use whenever you provide a PDF file path where the file size exceeds 1MB (1048576 bytes).

Can I reuse the extracted data for multiple queries?

Yes. The skill caches the extracted content locally. Once a PDF is processed, subsequent searches and queries are nearly instantaneous.

PDF Smart Extractor

Name: PDF Smart Extractor
Author: diegocconsolini

bydiegocconsolini

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Web Scraping & Data Collection

Extracts and analyzes large PDF documents locally with semantic chunking to minimize token usage and maximize context efficiency.

About

PDF Smart Extractor is a specialized tool for Claude Code designed to handle massive PDF files that typically exceed LLM context windows. By performing extraction locally using PyMuPDF and implementing semantic chunking, it enables the analysis of complex technical manuals and compliance frameworks like NIST or ISO without wasting tokens. The skill intelligently detects large files, creates a persistent local cache, and provides a search-based retrieval system that can reduce token consumption by up to 100x by only loading relevant document sections for your specific queries.

Key Features

Zero-LLM local PDF extraction to preserve privacy and tokens
Intelligent keyword querying for precise information retrieval
Persistent local caching with hash-based integrity verification
Semantic chunking that maintains document structure and context
2 GitHub stars
Automatic detection and triggering for PDF files exceeding 1MB

Use Cases

Building local knowledge bases from architectural whitepapers and security guides
Searching through multi-hundred-page technical manuals for specific configurations
Analyzing massive compliance frameworks such as NIST, ISO, or SOC2 documentation

About

Key Features

Zero-LLM local PDF extraction to preserve privacy and tokens
Intelligent keyword querying for precise information retrieval
Persistent local caching with hash-based integrity verification
Semantic chunking that maintains document structure and context
2 GitHub stars
Automatic detection and triggering for PDF files exceeding 1MB

Use Cases

Building local knowledge bases from architectural whitepapers and security guides
Searching through multi-hundred-page technical manuals for specific configurations
Analyzing massive compliance frameworks such as NIST, ISO, or SOC2 documentation