Xberg Document Intelligence Skills
Extract and analyze documents with Xberg’s polyglot processing tools.
The official plugin Skills guide document extraction, configuration, OCR, server integration, and related Xberg workflows across supported formats.
Use cases
- Extract text, tables, images, and metadata
- Integrate document processing into applications
How to use
Install Xberg through the required binding or CLI and use its official plugin. Keep the configured extraction runtime available; other plugin Skill trees are host mirrors.
Skills
batch-extraction
Use when extracting from many files at once with shared config, bounded parallelism, per-file overrides, and error recovery. Covers the `batch` command, `--file-configs`, `--max-concurrent`, and output layout.
xberg-io/xbergchunking
Use when splitting extracted text into chunks for LLM context windows or RAG ingestion. Covers chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone `chunk` command.
xberg-io/xbergextracting-keywords
Use when extracting keywords (YAKE/RAKE) from documents — and, secondarily, when detecting document language or generating embeddings for RAG and search. Covers the keyword config (and its feature gating), `--detect-language`, and the standalone `embed` command with real flags.
xberg-io/xbergextracting-tables
Use when extracting tabular data from PDFs, spreadsheets, or images. Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits.
xberg-io/xbergextracting-with-ocr
Use when extracting text from scanned PDFs, photographed pages, or images that have no embedded text layer. Covers OCR backends, language packs, force-OCR, and performance tuning.
xberg-io/xbergpicking-a-format
Use when choosing an output format for extracted documents — plain text, markdown, djot, HTML, JSON, or DocTags. Maps consumer (LLM, parser, archive) to the right `--format` / `--content-format` pair.
xberg-io/xbergxberg
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-io/xberg