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gaik-project/gaik-toolkit

v0.6.1MIT

GAIK agent skills. For developers building document pipelines with the gaik Python toolkit: choosing a document parser that preserves table structure, designing extraction schemas that stay inside provider limits, building hybrid pgvector search whose failures are visible instead of silent, and writing source-cited reports from mixed files with the gaik report writer. Ready-to-run use cases: turning a short brief into an editable slide deck with speaker notes, timing and citations, and Claude Desktop skills that write incident reports, construction site diaries, priced sales orders and structured reports.

parsing-documents

Converts PDFs, scans, and Word documents into text or markdown with the gaik toolkit's parsers, choosing the parser that will not silently destroy the structure the downstream task depends on. Use when reading a PDF or DOCX into text, pulling tables out of a document, running OCR on scans, feeding documents into a RAG pipeline or an LLM, deciding between PyMuPDF, Docling, and vision-LLM parsing, or when a parse appeared to succeed but the tables, columns, or whole pages came out wrong or empty. Also use when document parsing is costing more time or money than expected. Covers parser selection, per-page verification, and escalation from cheap local parsing to vision models.

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