Claude Code-powered end-to-end meta-analysis automation: AI-assisted literature review, screening, extraction, analysis, and manuscript generation for systematic reviews and clinical evidence synthesis
Claude Code plugin to draft, review, revise, proofread, and audit quantitative-science journal manuscripts at submission quality. Five strict modes, Semantic Scholar MCP citation integrity (no API key), .docx round-trip. Calibrated for hydrology (HJ, JHRS) and IEEE; generic profile for any field.
Open-source AI skills for AI-assisted research workflows: diagnostics, modeling, manuscript-section drafting, LaTeX assembly, and review checkpoints.
End-to-end automated EEG analysis to manuscript pipeline (MNE-BIDS → AI writing → Manubot/Carbone)
LLM-powered systematic literature review pipeline for clinical medicine—generates submission-ready manuscripts with PRISMA 2020 compliance, parallel writing agents, validated citations, and quality audit loops.
Project Vyasa is a local-first research execution framework for DGX Spark that helps researchers, journal authors, and domain experts turn unstructured documents into defensible, evidence-bound manuscripts for high-stakes, long-running inquiry. It keeps humans in control of judgment while AI handles extracting, validating, and governing evidence.
Open-source AI skills for AI-assisted statistical research workflows: assumption checks, test selection, modeling, manuscript-section drafting, and review checkpoints.
AI-powered meta-analysis pipeline for systematic reviews in oncology and clinical research; reproducible Python framework for rapid literature screening, extraction, and manuscript generation with Claude integration.
To associate your repository with the manuscript-generation topic, visit your repo's landing page and select "manage topics."