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Use Docling to convert, parse, configure, extract, chunk, serve, and maintain document-processing workflows across Python APIs, CLI commands, optional pipelines, and repository edits.
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Parse complex documents with IBM's open-source Docling library, which turns PDFs and Office files into structured Markdown or JSON. Use when a user asks to parse a document with tables, extract figures from a document, handle multi-column layouts, convert a complex PDF to structured data, extract content from academic papers, or process documents with mixed layouts. Handles tables, figures, headers, footers, and multi-column text.
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docling CLIを使用したドキュメント変換スキル。PDF、DOCX、PPTX、HTML、画像、Excel等のあらゆるドキュメントをMarkdown、JSON、YAML、HTML、テキストに変換。OCR対応で画像内テキストも抽出可能。Use when converting documents to Markdown or other formats, extracting text from PDFs, processing scanned documents with OCR, or converting office documents. Argument hint: [file_path] [--to md|json|yaml|html|text] [--ocr-lang ja] [--output output_dir]
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Turn books and documents into reusable agent skills by extracting frameworks, decision rules, techniques, and mistakes to avoid. Generate structured skill files with chapter references and glossaries, or review an analysis before creating files.
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Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。
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Converts DOCX/PDF/PPTX and saved HTML/HTM to high-quality Markdown with automatic post-processing. Fixes pandoc grid tables, simple tables, image paths, CJK bold spacing, attribute noise, and code blocks; for PDFs also strips OCR garbage blocks, repeated headers/footers/watermarks, and absolute image paths from pymupdf4llm output. Benchmarked best-in-class (7.6/10) against Docling, MarkItDown, Pandoc raw, and Mammoth. Trigger on "convert document", "docx to markdown", "parse word", "doc to markdown", "解析word", "转换文档", "HTML to Markdown".
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Convert PDF documents to clean structured Markdown for LLM context. Supports two modes: fast (PyMuPDF) and accurate (IBM Docling TableFormer AI). Features aggressive persistent caching, image extraction with metadata, table detection, and batch processing. Use when asked to convert PDFs, extract PDF content, parse documents, or prepare PDF data for AI/LLM consumption.
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Unified document optimization skill that chains docling-converter, imagemagick-expert, and markdown-to-pdf. Automatically detects input format, applies appropriate conversion pipeline, optimizes embedded images, and produces web/print-ready output with configurable quality presets. Use when converting documents through multi-step pipelines, optimizing PDF images, creating print-ready documents from various sources, or batch processing documents with image optimization.
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