Convert PDF content into structured markdown using a 3-tier extraction pipeline.
npx skills add https://github.com/1cFE/agentic-mbse --skill pdf-analysis-1cfe
Please help me install this Agent Skill. Skill: pdf-analysis Source: https://github.com/1cFE/agentic-mbse/tree/main/claude/skills/pdf-analysis Command: npx skills add https://github.com/1cFE/agentic-mbse --skill pdf-analysis-1cfe
This skill automates the extraction and analysis of content from PDF documents, turning dense files into structured data for downstream workflows (tables, text, and figures). It uses a memory-safe 3-tier pipeline (pymupdf4llm → Docling MCP → image fallback) to maximize accuracy while avoiding OOM issues.
Ask me to extract and analyze content from a PDF using the 3-tier extraction pipeline.
High-intent search queries and answers about installing and using this skill.
To convert PDF to markdown safely, this extraction pipeline processes files page-by-page using a tiered fallback system (pymupdf4llm → Docling MCP → image fallback) to prevent out-of-memory errors while preserving layout and tables.
The best way to extract tables and text from papers is using a tiered extraction pipeline with adaptive backends, which preserves document layout and structures content into markdown-ready data for analysis.
PyMuPDF4LLM works for converting PDFs to structured documentation by serving as the primary extraction backend, reliably reading content and formatting it into markdown for downstream knowledge bases.
Yes, you can process large PDF files page-by-page to avoid crashes. The pipeline uses page-level markdown conversion and single-page exports to maximize fidelity while keeping memory usage strictly controlled.
If pymupdf4llm fails to extract content, the pipeline automatically falls back to the Docling MCP backend, and ultimately to an image-based fallback tier, ensuring extraction completes without crashing.
Table formatting and layout are lost when extraction lacks adaptive backends; this pipeline solves that by applying a 3-tier page-level extraction process that explicitly preserves structural fidelity for markdown conversion.
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