Skill UI

This skill enables deterministic programmatic PDF operations including text and table extraction, merging and splitting pages, filling form fields, and generating new PDFs from JSON data. It utilizes libraries like pypdf, pdfplumber, and reportlab to handle structured PDF work efficiently. Ideal for automating document processing tasks such as report analysis, form completion, and document consolidation within a Python environment.

Python client library for Azure AI Language service, covering sentiment analysis, opinion mining, entity and PII recognition, key phrase extraction, language detection, and healthcare NLP over text documents.

Extract keywords with YAKE or RAKE, detect document language, and generate vector embeddings for RAG and search using the xberg CLI or Python API, with local, LLM, or plugin providers.

Extract structured tabular data from PDFs, spreadsheets, and images using layout-aware detection and configurable table models, with markdown or JSON cell output for LLM ingestion and downstream processing.

Locate, select, and verify evidence in documents or code inventories. Supports source-span extraction, passage reranking, and claim checks. Preserves citations and handles no-match outcomes using graph tools for exact code lookup. Ideal for AI agents performing evidence-based reasoning.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

Guides choosing the right output format (plain, markdown, djot, HTML, JSON, DocTags) and token-reduction level for Xberg document extraction, based on downstream consumers like LLMs, RAG systems, parsers, or human review.

Xberg is a document intelligence library supporting 107 formats including PDF, Office, and images. It extracts text, tables, and metadata using OCR and chunking. Available for Python, Node.js, Rust, and CLI. Ideal for developers building document processing applications with multi-language bindings and structured output.

Use xberg to extract YAKE/RAKE keywords, detect document languages, and generate local or LLM embeddings for RAG and search workflows.

Learn how to configure xberg's --format and --content-format flags to optimize document extraction for different consumers. Covers LLM context, RAG indexing, JSON parsing, and token reduction strategies for efficient data pipelines.