Plugins

Local literature knowledge-base RAG: 10 tools (kb_ingest, kb_search, kb_rag, kb_zotero, kb_dedup, kb_clear, kb_fetch, kb_scope, kb_stats, kb_status), hybrid BM25+vector+reranker search, cited QA with clickable DOI links, all-local bge embeddings over a single-file SQLite index. Needs Python 3.9+ with pymupdf, faiss-cpu and sentence-transformers; the `npx dsh-kb-rag-install` installer sets those up and pre-downloads the model.

Turns one journal paper PDF into a Chinese group-meeting literature report deck (.pptx) in the Nanyang Normal University physics template, plus a matching Word speaker script. Requires Python 3 with python-pptx, python-docx, Pillow and pymupdf (pypdf fallback); install them with pip install -r requirements.txt.

Pipeline for scanned textbooks: OCR at roughly 5x the ONNXRuntime speed (requires Python 3.10+ with pymupdf, rapidocr, openvino and numpy), then a structure report and a per-chapter reading plan before you open the PDF.

Local knowledge base for DeepSeek Harness: five tools to ingest Markdown, plain text and PDF files, search them with BM25 or hybrid BM25 plus optional local embeddings, and answer with cited source passages. Runs offline with no database server. Requires Node.js 18+ and Python 3.9+; PDF ingest needs pymupdf.

Document intake for agents: doc_route sniffs a file's real format by magic bytes and measures a PDF's text layer and page column layout before any conversion, and pdf_markdown converts PDFs to Markdown in column reading order with PyMuPDF. The PDF path needs a Python 3 interpreter with PyMuPDF.

Translate text-based PDFs via DeepSeek or OpenAI-compatible APIs while preserving layout, fonts, images and links, with editable output.

Bundles a deep-read paper library and the paper-deepread-html skill: a sidebar panel to browse interactive reading reports, upload PDFs into a managed library, deep-read them with an agent (or through an optional site auto-read worker), and manage or rebuild the library; ships the paper_deepread, paper_deepread_uploads and paper_deepread_reports model tools. Deep-read/ingest and report builds require Python 3 (the bundled skill prepares a PyMuPDF venv on first use; index rebuild falls back to plain python3).