Scholar RAG is an agent skill from joshzyj/open-scholar-skill. Build and query a local vector database + GraphRAG over your entire reference library (Zotero or a PDF folder) for literature review. Downloads/locates full-text PDFs, extracts text (pdftotext/PyMuPDF/vision-OCR), chunks and embeds with bge-m3 into LanceDB, and layers a local-LLM GraphRAG (entity/relation extraction + Leiden communities + community summaries) seeded from the scholar-knowledge graph. Adds a bibliographic layer from OpenAlex (direct citations + bibliographic coupling + co-citation + paper-level…
Its SKILL.md is about 7.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 23 other files, including reference files and assets (for example `assets/_lib.sh`, `assets/build-all.sh` and `assets/chunk_embed.py`).
It sits in Research & Science, covering Knowledge graphs, Citation management and Retrieval-augmented generation. It works with Model Context Protocol, Ollama and Zotero. The repository describes itself as: Open scholar skill, a claude code plugin, for academic research.