Xberg Extraction
Extracts text, tables and images from 107 document formats in several languages.
Install
npx skills add xberg-io/xberg --skill xberg -g -a claude-code -y- Repository
- xberg-io/xberg9.5k
- Category
- Documents
- Updated
- 3d ago
- License
- Elastic-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Extract text, tables, metadata, and images from 107 document formats (PDF, Office, images, HTML, email, archives, academic) using Xberg. Use when writing code that calls Xberg APIs in Python, Node.js/TypeScript, Rust, or CLI. Covers installation, extraction (sync/async), configuration (OCR, chunking, output format), batch processing, error handling, and plugins.
Xberg Document Extraction
Xberg is a document intelligence library with a Rust core and bindings for Python, TypeScript/Node.js, Ruby, PHP, Go, Java, C#, Elixir, WebAssembly, Dart, Kotlin Android, Swift, Zig, and C. It extracts text, tables, metadata, and images from 107 formats across 140 unique file extensions and accepts 53 compatibility MIME aliases, including PDF, Office documents, images, HTML, email, archives, and academic formats.
Use this skill when writing code that:
- Extracts text or metadata from documents
- Performs OCR on scanned documents or images
- Batch-processes multiple files
- Configures extraction options (output format, chunking, OCR, language detection)
- Implements custom plugins (post-processors, validators, OCR backends)
If the
xbergMCP server is registered in this session, prefer its tools over shelling out to the CLI, they expose the same extraction surface with structured arguments and results.
Installation
Python
pip install xberg
Node.js
npm install @xberg-io/xberg
Rust
cargo add xberg
# Cargo.toml
[dependencies]
xberg = { version = "1.1.0", features = ["full"] }
tokio = { version = "1", features = ["full"] }
# feature flags: pdf, ocr, chunking, embeddings, language-detection, keywords, api, mcp
# (or "formats" / "full" aggregates); tokio-runtime is on by default
