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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, dev
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill denario -a cursor -y
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.
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.
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:
Install denario using uv (recommended):
uv init
uv add "denario[app]"
Or using pip:
uv pip install "denario[app]"
For Docker deployment or building from source, see references/installation.md.
Denario requires API keys from supported LLM providers. Supported providers include:
Store API keys securely using environment variables or .env files. For detailed configuration instructions including Vertex AI setup, see references/llm_configuration.md.
Denario follows a structured four-stage research pipeline:
Define the research context by specifying available data and tools:
from denario import Denario
den = Denario(project_dir="./my_research")
den.set_data_description("""
Available datasets: time-series data on X and Y
Tools: pandas, sklearn, matplotlib
Research domain: [specify domain]
""")
Generate research hypotheses from the data description:
den.get_idea()
This produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:
den.set_idea("Custom research hypothesis")
Develop the research methodology:
den.get_method()
This creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:
den.set_method("path/to/methodology.md")
Execute computational experiments and generate analysis:
den.get_results()
This runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:
den.set_results("path/to/results.md")
Create a publication-ready LaTeX paper:
from denario import Journal
den.get_paper(journal=Journal.APS)
The generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.
Denario supports multiple journal formatting styles:
Journal.APS - American Physical Society formatreferences/research_pipeline.md for the complete listRun the graphical user interface:
denario run
This launches a web-based interface for interactive research workflow management.
from denario import Denario, Journal
# Initialize project
den = Denario(project_dir="./research_project")
# Define research context
den.set_data_description("""
Dataset: Time-series measurements of [phenomenon]
Available tools: pandas, sklearn, scipy
Research goal: Investigate [research question]
""")
# Generate research idea
den.get_idea()
# Develop methodology
den.get_method()
# Execute analysis
den.get_results()
# Create publication
den.get_paper(journal=Journal.APS)
# Provide custom research idea
den.set_idea("Investigate the correlation between X and Y using time-series analysis")
# Auto-generate methodology
den.get_method()
# Auto-generate results
den.get_results()
# Generate paper
den.get_paper(journal=Journal.APS)
For literature search functionality and additional workflow examples, see references/examples.md.
For comprehensive documentation:
references/installation.mdreferences/llm_configuration.mdreferences/research_pipeline.mdreferences/examples.mdCommon issues and solutions:
references/llm_configuration.md)