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OCR and classify scanned PDFs with an LLM+vision pipeline, then organize files into category folders with markdown and metadata sidecars.
Extracts text from scanned PDFs (Docling + vision OCR), classifies documents into categories using a language model, and moves files into <scans_dir>/<category>/ with .md and .meta.json sidecars. Provides auditability via a manifest and supports dry-run, reclass, undo, and single-file processing.
Use when you have a directory of scanned PDFs that need automated text extraction, classification (medical, financial, insurance, tax, legal, personal, household, other), and structured output for downstream workflows (search, ingestion, compliance). Good for medical records, invoices, insurance paperwork, and bulk archive processing.
Best used with agents that can call shell tooling and file I/O (Copilot/Codex/CLI-capable agents). The skill assumes an OpenAI-compatible model endpoint for classification (OpenAI, Ollama, OpenRouter).
Scan Organizer processes scanned PDFs through a Docling + vision OCR pipeline, classifies them via LLM into categories (medical, financial, insurance, etc.), and organizes files into subfolders with markdown and metadata sidecars. No bundled scripts were present to test. The SKILL.md is well-structured with clear commands, output format, and architecture overview, but lacks error handling details and edge case guidance.
Clean skill with no security concerns. No hardcoded credentials, no remote code execution patterns, no destructive commands. The skill requires external LLM API access and uv package manager, which adds setup complexity. Source is from an insurance-focused org (FDU-INS), category scope is narrow (insurance document workflows) limiting broad usefulness.
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