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Automates scientific research workflows from data analysis to publication using a multiagent AI system. Use for generating hypotheses, developing methodologies, executing experiments, and writing LaTeX papers.
npx claudepluginhub alterlab-ieu/alterlab-academic-skills --plugin alterlab-writing-toolsHow this skill is triggered — by the user, by Claude, or both
Slash command
/alterlab-writing-tools:alterlab-denarioThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
Automates scientific research workflows from data analysis to publication using multiagent AI orchestration. Generates hypotheses, develops methodologies, executes experiments, and produces LaTeX papers.
Orchestrates full research pipeline from Brainstorming to Reporting via Planning, Implementation, Testing & Visualization phases with user checkpoints. Configurable for physics, AI/ML, statistics, math domains, depth, and agent personas.
Routes ambiguous or multi-step research requests to the correct skill in the medsci-skills bundle. Use when the user has a research goal but doesn't name a specific skill, or when the task spans multiple skills.
Share bugs, ideas, or general feedback.
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)