Denario is a multiagent AI system for Claude Code that automates the scientific research lifecycle, including data analysis, experiment execution, and manuscript writing.

How does Denario handle data analysis?

It uses specialized agents to interpret data descriptions, write analysis code (using tools like pandas and sklearn), and generate visual results.

Which journal formats does Denario support?

It currently supports American Physical Society (APS) formatting, with a modular structure that allows for additional journal styles and custom LaTeX templates.

Can I provide my own research hypotheses to Denario?

Absolutely. While Denario can generate ideas from data, you can also manually set your own research ideas and methodologies for the agents to process.

Does Denario require specific local dependencies?

Yes, Denario requires Python 3.12 or higher. For LaTeX compilation, it is recommended to have a TeX distribution installed or use the provided Docker container.

Denario Scientific Research Assistant

Name: Denario Scientific Research Assistant
Author: Activer007

byActiver007

データサイエンスとML

Automates end-to-end scientific research workflows from initial data analysis to publication-ready LaTeX manuscripts.

概要

Denario is a powerful multiagent AI system designed to streamline the entire scientific research process within Claude Code. By orchestrating specialized agents through frameworks like AG2 and LangGraph, it assists researchers in generating novel hypotheses, developing structured methodologies, executing computational experiments, and producing journal-formatted papers in LaTeX. Whether you are starting with a raw dataset or a specific research question, Denario provides a modular and reproducible pipeline to accelerate scientific discovery and simplify the path to academic publication.

主な機能

Execution of computational experiments and data visualizations
Journal-formatted LaTeX paper generation (e.g., APS style)
Structured research methodology development and documentation
Literature search integration for research context
0 GitHub stars
Automated hypothesis generation from raw datasets

ユースケース

Building reproducible research pipelines using multiagent orchestration
Generating novel research ideas and hypotheses from experimental data
Automating the creation of publication-ready manuscripts for academic journals

概要

主な機能

Execution of computational experiments and data visualizations
Journal-formatted LaTeX paper generation (e.g., APS style)
Structured research methodology development and documentation
Literature search integration for research context
0 GitHub stars
Automated hypothesis generation from raw datasets

ユースケース

Building reproducible research pipelines using multiagent orchestration
Generating novel research ideas and hypotheses from experimental data
Automating the creation of publication-ready manuscripts for academic journals