Optimize document ingestion with OCR and text parsing for multi-page PDFs.
npx skills add https://github.com/uriva/p2b-skills --skill p2b-document-pipeline
Please help me install this Agent Skill. Skill: p2b-document-pipeline Source: https://github.com/uriva/p2b-skills/tree/main/document-pipeline Command: npx skills add https://github.com/uriva/p2b-skills --skill p2b-document-pipeline
This skill addresses the high cost and inaccuracy associated with naive document processing by providing a structured architecture for intelligent document ingestion and extraction.
Analyze the provided document using the p2b-document-pipeline guidelines to determine whether to perform direct text extraction or OCR before mapping the data to the target JSON schema.
High-intent search queries and answers about installing and using this skill.
To build a cost-effective document extraction pipeline, you must optimize ingestion by selecting the right parsing strategy. This involves choosing between direct native PDF text extraction and OCR-first processing for scanned images, then mapping the extracted data to a target JSON schema for high-precision output.
When processing large-context multi-page documents, you should leverage Gemini for its large-context capabilities, whereas Claude is better suited for complex reasoning tasks. This intelligent model selection ensures high-precision document ingestion without unnecessary computational overhead.
You should use direct text extraction for native PDFs to save costs and increase speed, whereas an OCR-first pipeline is required for scanned images. Determining the document type before parsing ensures high-precision output and avoids expensive, unnecessary OCR processing.
Yes, you can map complex document layouts from different providers into a single enterprise database format by using JSON schema-based mapping. This technique normalizes data extracted from varied provider layouts into a consistent structured format for high-precision ingestion.
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