denario
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Target: Data analysis. Automatically collected · unverified.
Excerpt from the author’s documentation
Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.
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Topics in the source
- Denario
- Overview
- When to Use This Skill
- Installation
- LLM API Configuration
- Core Research Workflow
- 1. Data Description
- 2. Idea Generation
Denario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing. Use this skill when: Analyzing datasets to generate novel research hypotheses Developing structured research methodologies Executing computational experiments and generating visualizations Conducting literature searches for research context Writing journal-formatted LaTeX papers from research results Automating the complete research pipeline from data to publication Install denario using uv (recommended): Or using pip: For Docker deployment or building from source, see references/installation.md. Denario requires API keys from supported LLM providers. Supported providers include: Google Vertex AI OpenAI Other LLM services compatible with AG2/LangGraph Store API keys securely using environment variables or .env files. For detailed configuration instructions including Vertex AI setup, see references/llmconfiguration.md. Denario follows a structured four-s
This is an excerpt. Refer to the complete source and its current requirements.
- Source repository
- davila7/claude-code-templates
- Source file
- cli-tool/components/skills/scientific/denario/SKILL.md
- Fixed revision
- 21be197f100a12b8d0dd9a211cc244c8179f84d3
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Author: davila7/claude-code-templates · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T15:01:40.002Z.
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