denario

Automate end-to-end scientific research workflows from data analysis to LaTeX publication.

16|7|Updated Nov 20, 2025
One-click install
npx skills add https://github.com/jackspace/ClaudeSkillz --skill denario
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: denario
Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-denario
Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill denario

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Denario provides a multiagent AI workflow to transform raw data into hypothesis, methodology, results, and publication-ready manuscripts, enabling end-to-end scientific research automation.

Core Features & Use Cases

  • Data description, idea generation, methodology planning, results computation
  • Publication-ready LaTeX output tailored to journals
  • LLM configuration and orchestration across agents

Quick Start

  • Initialize a project, describe data, generate an idea, and generate a paper

Frequently Asked Questions about denario

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate a research workflow from raw data to publication?

Denario automates end-to-end research workflows by orchestrating multiple AI agents to transform raw data into hypothesis, methodology, results, and publication-ready LaTeX manuscripts through customizable multiagent coordination.

Can I generate research ideas and papers automatically from datasets?

Yes. Denario accepts data descriptions, generates research ideas, develops methodologies, executes computational experiments, and produces journal-formatted LaTeX papers all within a single automated pipeline.

What does end-to-end scientific research automation include?

End-to-end automation covers data analysis, idea generation, literature searches, methodology planning, computational execution, results generation, and publication-ready manuscript production with LaTeX formatting tailored to journal requirements.

How does multiagent orchestration work for research workflows?

Denario uses AG2 and LangGraph frameworks to coordinate multiple specialized agents across each research stage—data interpretation, hypothesis generation, methodology development, and manuscript authoring—enabling modular, configurable pipeline design.

Can I customize LLM configuration and agent coordination in research pipelines?

Yes. Denario supports configurable LLM settings and multiagent orchestration, allowing you to adjust agent behavior, task sequencing, and external tool integration to match your research workflow requirements.

What outputs does Denario produce for publication?

Denario generates publication-ready LaTeX manuscripts with journal-specific formatting, alongside structured outputs for methodology, results, and supplementary references derived from your data and computational experiments.